Technical Field

[0001] The present disclosure relates to systems, computer implemented methods, and computer-readable media for managing drainage assets, and in some embodiments, assessing and managing the structural condition of drainage assets such as stormwater and sewer pipes.

Background

[0002] It is estimated that there are approximately $11 billion worth of assets such as underground sewer and stormwater pipes (which may hereafter collectively be referred to as “wastewater conduits”) installed in Australia. It is further estimated that only 20% of the total cost of wastewater conduits are consumed by upfront capital expenditure, and another 80% is spread over operation, maintenance, rehabilitation, and disposal. Planning the wastewater conduit lifecycle may be crucial to their efficient operation.

[0003] Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present disclosure as it existed before the priority date of each of the appended claims.

[0004] Throughout this specification the word "comprise", or variations such as "comprises" or "comprising", will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.

Summary

[0005] Some embodiments relate to a method comprising: determining a set of structural data relating to a drainage asset, wherein the set of structural data comprises defect data; determining an overall length of the drainage asset; determining an overall condition measure of the drainage asset based on the set of structural defect data and the overall length, wherein the overall condition measure represents the structural condition of the drainage asset; determining a future time for performing a maintenance operation of the drainage asset based on the overall condition measure.

[0006] The defect data of the set of structural data may comprise at least one of: (i) one or more locations of defects along a length of the drainage asset; (ii) an extent of one or more defects; and (iii) a type of one or more defects.

[0007] Determining the overall condition measure may comprise dividing the overall length of the drainage asset into a plurality of sections, each section having a respective section length. Determining the overall condition measure may comprise assigning a local condition measure to each section of the plurality of sections based on the defect data for each section. Determining the overall condition measure may comprise determining a proportion of the drainage asset with a specific local condition measure, wherein the determined proportion is a total length of the sections having that local condition measure; and determining the overall condition measure based on the calculated or determined proportions.

[0008] The method may further comprise scheduling future maintenance of the drainage asset based on the determined future time.

[0009] Determining a future time for performing a maintenance operation of the drainage asset based on the overall condition measure may comprise providing the overall condition measure as an input to a first predictive model. Determining the future time may comprise receiving as an output of the first predictive model, the future time for performing the maintenance operation. The first predictive model may be configured to determine the future time as a time when the overall condition measure of the drainage asset is predicted to reach a threshold overall condition measure.

[0010] The threshold overall condition may be indicative of an end of life of the drainage asset, and the maintenance operation comprises removing or replacing the drainage asset.

[0011] Determining a future time for performing a maintenance operation of the drainage asset based on the overall condition measure may comprise providing the overall condition measure as an input to a second predictive model. Determining the future time may comprise receiving as an output of the second predictive model, the future time for performing the maintenance operation. The second predictive model may be configured to determine the future time as a time based on an assessment of a current risk of failure of the drainage asset.

[0012] Determining the future time for performing the maintenance operation may be based on one or more further structural characteristics associated with the drainage asset. The one or more structural characteristics may comprise one or more of: size of the drainage asset, an internal pipe diameter of the asset, a pipe wall thickness of the asset, and a material of the asset.

[0013] The method may further comprise determining a set of environmental data relating to the drainage asset, wherein the set of environmental data comprises data relating to one or more environmental factors with a potential to impact the structural integrity of the drainage asset. The method may further comprise determining the future time for performing a maintenance operation of the drainage asset based on the set of environmental data.

[0014] The one or more environmental factors may comprise one or more of: location data of the asset; plant information in a vicinity of the asset; and soil information of soil in a vicinity of the asset. [0015] The plant information may comprise one or more of: location of nearby plants; canopy size of nearby plants; and extent of plant root spread.

[0016] The soil information may comprise one or more of: soil acidity; soil composition; and soil moisture.

[0017] The method may further comprise determining a set of operational data relating to the drainage asset, wherein the third set of operational data comprises data relating to the current operational performance of the drainage asset. The method may further comprise determining the future time for performing a maintenance operation of the drainage asset based on the set of operational data.

[0018] The one or more operational data may comprise one or more of: an operating time of the asset; an operating frequency of the asset; a maintenance history of the asset; and a current throughput of material through the asset.

[0019] The method may further comprise determining an initial operating efficiency and/or capacity of the drainage asset based on a design throughput when the asset was new. The method may further comprise comparing the design throughput to the current throughput. The method may further comprise determining a current operating efficiency and/or capacity of the drainage asset.

[0020] Scheduling future maintenance of the drainage asset based on the determined future time may comprise determining an importance rating of the drainage asset relative to other drainage assets in a drainage asset network. Scheduling the future maintenance may comprise prioritising the scheduling of the future maintenance based on the importance rating.

[0021] Scheduling future maintenance of the drainage asset based on the determined future time may comprise grouping a work order for maintenance of the drainage asset with similar work orders. Scheduling the future maintenance may comprise scheduling the future maintenance of the drainage assets of the work orders of the group as a bulk work order.

[0022] Some embodiments relate to a system comprising: one or more processors; and memory comprising computer executable instructions, which when executed by the one or more processors, cause the system to perform the aforementioned method.

[0023] Some embodiments relate to a computer-readable storage medium storing instructions that, when executed by a computer, cause the computer to perform the aforementioned method.

Brief Description of Drawings

[0024] Embodiments are described in further detail below, by way of example, with reference to the accompanying drawings, in which:

[0025] Fig. 1 is a diagram of an example drainage asset or conduit, such as a stormwater or sewer pipe;

[0026] Fig. 2 is a perspective view of a section of a wall for two adjacent conduits;

[0027] Fig. 3 is an example of structural data derived during an inspection of the conduit of Fig. 1, according to some embodiments;

[0028] Fig. 4 is a schematic of an asset maintenance network comprising a system configured to monitor and/or manage a plurality of assets in the network;

[0029] Fig. 5 is a process flow diagram of a method of managing the structural condition of a drainage asset, such as the conduit of Fig. 1, according to some embodiments; [0030] Fig. 6 is a process flow diagram of a method of determining an overall condition measure of a drainage asset, according to some embodiments;

[0031] Fig. 7 is an example screenshot of a conduit condition determination as output to a user interface of a computing device of the network of Fig. 3; and

[0032] Fig. 8 is an example screenshot of a map with an overlay of a drainage network and vegetation.

Detailed Description

[0033] Described embodiments relate to systems, computer implemented methods, and computer-readable media for managing drainage assets, and in some embodiments, assessing and managing the structural condition of drainage assets such as stormwater and sewer pipes.

[0034] Systems and methods which accurately estimate asset degradation over time and/or asset lifespan may reduce the likelihood or frequency of major repairs or premature replacement, and thereby reduce lifecycle costs. Being able to accurately estimate asset degradation over time and/or asset lifespan may allow early identification of potential problems, such as structural defects. If a potential problem is identified and addressed before it develops into a more extensive problem, the cost and time associated with the repair is often lower. Furthermore, described embodiments allow for monitoring and maintenance planning of a relatively large number of assets within a network, and may supplement maintenance planning based on information obtained by visual inspection of the condition of the assets.

[0035] Drainage assets such as stormwater and sewer pipes are used to convey fluids such as rainwater, wastewater, and sewerage from a drainage point to a water treatment facility. The drainage point may be a drain at a built property (e.g. residential, industrial, or commercial), or a council drain on the street. Green recreational areas such as parks and golf courses may have underground drainage points where water filters through the soil above.

[0036] The drainage points and drainage assets are part of a drainage network which includes pumps and filters to move the fluids towards the water treatment facility. The water treatment facility treats the fluid to remove pathogens, chemicals, or foreign material such as rubbish and plant matter. The treated fluid may then be recycled or discharged to sea.

[0037] The sewer and stormwater pipes (which may hereafter collectively be referred to as “wastewater conduits”) may be built from a wide variety of materials such as reinforced concrete, fibre cement, polyethylene (PE), polypropylene (PP), or polyvinylchloride (PVC). The choice of material depends on the size of the conduit, its intended use, and its location. Some conduits are above ground, whereas some are underground.

[0038] Fig. 1 is a diagram of an example wastewater conduit 100. The conduit 100 comprises a wall 110 which defines an internal space 112. As shown, the wall 110 is a pipe wall wherein the internal space 112 is a lumen of the pipe or conduit 100. For some conduits 100, the internal space 112 may be open to the air, such as a canal or ditch (not shown).

[0039] The wall 110 comprises an inner surface 114 which defines the lumen 112. The lumen 112 comprises the internal environment 102 of the conduit 100. The wall 110 further comprises an outer surface 116 which is exposed to the external environment 104, which may be above or below ground. The internal environment 102 and the external environment 104 may have different levels of temperature, humidity, pressure, pH level/acidity, or salinity.

[0040] The conduit 100 may comprise premade lengths of conduit 100. This allows fluid to be conveyed over longer distances without having to manufacture a single continuous length of conduit 100. This facilitates manufacture and installation. During installation on site, these lengths of conduit 100 are joined together at a seam 120 to effectively form a continuous length of conduit 100.

[0041] Fig. 2 shows a section of the wall 110 for two adjacent conduits 100. In the conduit 100 shown, the wall 110 has a structural defect 200. The structural defect 200 is shown as a large crack for ease of illustration, but is more likely to be a series of fine, hairline cracks or spot damage.

[0042] The structural defect 200 may be caused by the internal environment 102. For example, the fluid in the conduit 100 may be acidic and prolonged contact with the inner surface 114 may eventually cause damage to the inner surface 114 of the conduit wall 110.

[0043] The structural defect 200 may also be caused by an acidic external environment 104. This may be particularly prevalent for underground conduits 100 which are buried in acidic soil. For conduits 100 in the open air, acid rain may cause similar damage.

[0044] The salinity of the soil or air may also affect the structural condition of the conduit 100. Temperature changes may affect pipes, with cyclic thermal expansion and contraction caused by hot temperatures during the day and cool temperatures at night causing stresses in the wall 110.

[0045] The conduit 100 may also be subject to dead and live loading. Dead loading may be the weight of the soil and any above built structure (e.g. building foundations and walls). Live loading may include vibration. For example, where the conduit 100 is under a road, the weight of the road is a dead load while the weight (and associated vibration) of the traffic travelling on the road is a live load.

[0046] The roots of nearby plants and trees grow towards water sources and can be attracted to the moisture of the internal and external environments 102, 104. The roots may enter the lumen 112 through the join/seam 120 where adjacent conduits 100 meet, or through an existing crack or defect 200 in the wall 110. In doing so, the roots may widen the crack or defect 200, affecting the structural condition of the conduit 100.

[0047] Root ingress can lead to blockages of the lumen 112. Once a root enters the lumen 112, the root may feed on the moisture in the internal environment 102 and grows. Growth of the root may eventually lead to a root mass which substantially obstructs the flow of fluid through the lumen 112.

[0048] The Critical Root Zone (CRZ) or Structural Root Zone (SRZ) is the minimum root growth zone around a tree necessary for the tree’s stability in the ground. The Tree Protection Zone (TPZ) or Root Protection Area (RPA) is a measure of estimating the extent of root growth. The TPZ and the SRZ are determined according to formulas given in Australian Standard AS 4970-2009. These formulas rely on measuring the trunk diameter 1.4m above ground level to estimate the TPZ size, while estimating the SRZ size relies on measuring the trunk diameter immediately above the tree’s buttress root. Some estimates of the SRZ and TPZ size are achieved by multiplying the canopy diameter of the tree or plant by a factor. This factor may vary depending on the height of the tree or plant or by the size of the tree’s trunk. For example, if a tree has a canopy measuring 3m in diameter, it may have a SRZ measuring anywhere between 4m to 9m in diameter, depending on the type of tree. Any underground structure within the SRZ or TPZ (such as building foundations or conduits 100) would be at increased risk of damage from root ingress.

[0049] It is advantageous to perform inspections of the conduit 100 to identify and address any structural defect 200 before the structural defect 200 becomes more extensive. The frequency of these inspections can be adjusted based on various factors, such as the air temperature and humidity, the soil pressure/weight, the air or soil pH level/acidity or salinity, or the proximity/SRZ of trees and plants. Inspections of the conduit 100 may provide an overview of the structural condition of the conduit 100 to allow assessments of the asset lifecycle to be made e.g. planning replacements, particularly where the conduit 100 is part of a larger drainage network. The structural condition of the conduit 100 may be an internal condition and/or an external condition. [0050] Inspections of the conduits 100 typically involve a human operator on site using a remote robotic camera, or other sensing equipment. The camera is entered into conduit 100 at a maintenance point such as a manhole or access pit. The manhole is called a “start node”. As the camera moves along inside the conduit 100, the operator collects structural data 300, including structural defect data, by logging any defects 200 identified visually or through sensor feedback (such as laser, sonar etc). These defects 200 are typically logged or encoded using a shorthand notation according to a national standard such as WSAA’s WSA05 in Australia, or NASSCO, PACP, MACP, LACP, Wrc and ISYBAU internationally. These codes may be used to determine a grade or score which represents the overall structural condition of the conduit 100.

[0051] Fig. 3 visually depicts an example of the structural data 300 derived during an inspection of the conduit 100. The structural data 300 may be collected in real time as the camera moves along inside the conduit 100, or upon later review of the camera footage taken along the conduit 100. In some embodiments an operator or reviewer reviews the camera footage to determine the structural data 300. In some embodiment, the camera footage may be applied or provide as input to an video or image content analysis engine configured to determine the structural defect data. For example, the video or image content analysis engine may comprise image recognition software (or computer vision) based on machine learning model(s). In other words, the structural defect data may be determined automatically or without human input.

[0052] The output shows that a 12.23 meter length of conduit 100 has been inspected, with various structural defects 200 observed along its length. The structural defect data 300 may comprise at least one of a location, extent, and type of the defect 200 observed for the conduit 100, as explained in more detail below. The structural defect data 300 may also comprise other observations which may not be a structural “defect”, but are relevant to the structural and/or operational performance of the conduit 100. For example, the structural defect data 300 may include comments or observations, such as regarding the amount of water flowing through the conduit 100, which may indicate the lack of a particular defect 200. [0053] The defects 200 of the example of Fig. 3 are encoded according to the Australian national standard WSA05 with a “chainage” (or distance from the start node) and any relevant comments. In this instance, the structural data 300 commences with shorthand notation code “STMH” at 0.45m (indicating a Start node at Maintenance Hole). The start node has a corresponding node name “2941” for identification purposes. At 0.45m from the start node, the operator also encodes a code of “WLC”, indicating clear flow (the invert is visible) of water with a depth of 10%.

[0054] At 5.19m and 7.74m from the start node, the operator has noted a defect 200 caused by root ingress into the conduit 100, and has marked this using the code “RF” (fine roots). At each location, the operator has included comments indicating an extent to which the conduit 100 is obstructed, leading to a reduction in the cross-sectional area of the conduit 100. For example, at 5.19m along, approximately 5-20% of the lumen 112 is obstructed. At 7.74m along, approximately 5% or less of the lumen 112 is obstructed.

[0055] The operator may also make note of the radial location of each defect 200. The radial locations indicate the position of the defect 200 along the wall 110. The radial location may be expressed as clock positions, wherein “12 o’clock” indicates the top-most position along the wall 110. For example, at 5.19m along and at 7.74m along, the respective “RF” defect 200 is at approximately the 7 o’clock to the 9 o’clock position. As this position is towards the bottom of the conduit 100 where fluid initially flows by gravity, this defect 200 (fine roots) indicates that fluid flow through the conduit 100 may be slightly impeded. In contrast, at 10.19m along, the defect 200 (“RT” indicating a tap root) is at approximately the 11 o’clock to the 1 o’clock position. As the position of this defect 200 is towards the top of the conduit 100 where fluid will only flow when the conduit 100 is full, the location of this defect 200 may be assigned a lower priority compared to the defect 200 at 5.19m along.

[0056] At 10.19m and 12.23m from the start node, the operator has noted further defects 200 caused by root ingress into the conduit 100. These defects 200 are marked using codes of “RT” (tap roots) and “RM” (mass of roots), respectively. The tap root is the main, vertical part of the root from which other fine roots grow from, so this defect code may be given a higher priority than other defects involving root ingress.

[0057] As before, the operator has made comments pertaining to percentage of the conduit 100 that is obstructed, and the radial location of roots are included in the classification data. At 10.19m, approximately 5% or less of the lumen 112 is obstructed by a tap root at the 11 o’clock to the 1 o’clock position of the wall 110. At 12.23m, approximately 75% or more of the lumen 112 is obstructed by a mass of roots at the 12 o’clock position. This obstruction is sizable enough to halt further progress of the remote camera along the conduit 100, thereby ending the inspection (code “SAR”). The operator may also make additional comments about the defect 200. These additional comments may help determine the maintenance and type of equipment required. At 5.19m, 7.74m, and 10.19m along the conduit 100, the respective “RF” and “RT” defects are noted to be at joints in the conduit 100 (where separate sections of pipe have been connected, such as via the seam 120, to form the conduit 100). At 12.23m along the conduit 100, the “RM” defect is noted to be a mass of mostly fine roots, which has developed into an interwoven clump.

[0058] Fig. 4 is a schematic of an asset maintenance network 400. The asset maintenance network 400 comprises a system 402, which may be configured to monitor and/or manage a plurality of assets in the network. The system 402 may be in communication with database(s) 404 and/or and one or more computing devices 406 across a communications network 408. Examples of a suitable communications network 408 include a cloud server network, wired or wireless internet connection, Bluetooth™ or other near field radio communication, and/or physical media such as USB.

[0059] The system 402 may comprise one or more servers configured to perform functionality and/or provide services to user devices, such as the one or more computing devices 406. The system 402 comprises one or more processors 410 and memory 412 storing instructions (e.g. program code), which, when executed by the processor(s) 410, causes the system 402 to function according to the described methods, such as methods 500, 600 below. The processor(s) 410 may comprise one or more microprocessors, central processing units (CPUs), graphical/graphics processing units (GPUs), application specific instruction set processors (ASIPs), application specific integrated circuits (ASICs) or other processors capable of reading and executing instruction code.

[0060] Memory 412 may comprise one or more volatile or non-volatile memory types. For example, memory 412 may comprise one or more of random access memory (RAM), read-only memory (ROM), electrically erasable programmable readonly memory (EEPROM) or flash memory. Memory 412 is configured to store program code accessible by the processor(s) 410. The program code comprises executable program code modules. In other words, memory 412 is configured to store executable code modules configured to be executable by the processor(s) 410. The executable code modules, when executed by the processor(s) 410 cause the system 402 to perform certain functionality, as described in more detail below.

[0061] Memory 412 may comprise an asset management module 414 configured to pre-process and/or prepare the structural data 300 for processing by a maintenance module 416, as discussed with reference to method 600 of Fig. 6. Memory 412 may further comprise the maintenance module 416, which is configured to process the structural data 300 to determine a suitable time in the further for scheduling maintenance of the conduit 100, which may comprise repair or replacement of the conduit 100. The maintenance module 416 may be further configured to schedule the maintenance of the conduit 100 for a time/date or date range derived from the determined suitable time.

[0062] The maintenance module 416 may comprise a predictive model 420 configured to receive as an input, structural data associated with the conduit 100, and provide as an output, the suggested or recommended time for performing maintenance. The predictive model 420 may be configured to determine or predict a time at which a condition of the conduit 100 is expected to fall below a given threshold. In some embodiments the threshold may be indicative of a need for repair of the conduit, or an end of life for the conduit.

[0063] In some embodiments, the structural data comprises structural defect data. In some embodiments, the structural data comprises an age of the conduit 100. In some embodiments, environmental data and/or operational data may be provided as input(s) to the predictive model 420. Accordingly, the determined or predicted time may be based on structural data, environmental data and/or operational data.

[0064] The predictive model 420 may be derived from or depend on historical conduit data accessible to the system 402 about the performance and/or structural degradation or deterioration of a plurality of conduits 100 in the network 400 (or in similar networks) over a period of time. For example, the database 404 may comprise the historical conduit data.

[0065] The historical conduit data may provide an indication of expected deterioration for the conduit 100. For example, the predictive model 420 may consider historical data indicative of the deterioration of similar drainage assets (with similar structural defects, and optionally, with similar other structural attributes, environmental factors, and/or operational conditions) over a period of time, such as a lifetime of the asset, to a conduit 100 in question. The historical data may indicate that similar drainage assets tend to develop a structural defect 200 within a five year period due to tree root ingress. The predictive model 420 may therefore determine that the conduit in question will reach a condition threshold in five years’ time.

[0066] In some embodiments, the predictive model 420 is a machine learning based model, such as classification model based on decision trees or Support Vector Machines (SVMs) for example. The predictive model 420 may be trained using a dataset extracted from the historical conduit data. For example, the training dataset may comprise labelled conduit examples including structural, environmental and/or operational information associated with the respective conduit at a particular point in time (for example, dynamic data) and an associated later time at which the conduit 100 reached a particular structural condition, for example a threshold structural condition. The training dataset may comprise labelled conduit examples including structural, environmental and/or operational information associated with the respective conduit and an indication of whether or not the conduit failed. The conduit examples may also comprise structural, environmental and/or operational information associated with the respective conduit of a relatively static or fixed nature, such as geographical location of the conduit or the material from which is composed. The labelled examples may be used to train the predictive model 420 to predict when a candidate conduit will reach a threshold condition based on input values for the one or more attributes of the conduit.

[0067] In some embodiments, the labelled examples may be used to train the predictive model 420 to predict when a likelihood of failure of the conduit 100 based on input values for the one or more attributes of the conduit. The predictive model 420 may be configured to receive as an input, structural data associated with the conduit 100, such as condition data, and provide as an output, as estimated overall risk or likelihood of failure of the conduit 100. In some embodiments, the predictive model 420 may be further configured to receive as input(s) other structural data, environmental data and/or operations data. For example, in some embodiments, the predictive model 420 may be configured to receive one or more of the following as inputs: whether the conduit is in a residential, commercial or rural; what the surface cover of the conduit (grass/asphalt/under building etc) is; what the proximity of the conduit to high traffic/essential services is; what the carrying capacity of the conduit is; how many upstream assets would be affected by failure; whether the soil is acidic or stable; a number of customer requests logged in the street; a number of customer requests logged for upstream assets (i.e., assets that feed the conduit in question).

[0068] In some embodiments, the predictive model 420 comprises a plurality of specific fault failure risk models (not shown), each fault failure risk model configured to predict the likelihood of a conduit failing as a result of a particular fault type. For example, fault types may include root ingress, soil ingress, void issues. For example, conduits that have holes (e.g. uncapped lifting lug, broken conduit, open joints), that are in areas of loose soil and high precipitation, that are in a high traffic area, and have a high carrying capacity, may be predicted to have a high likelihood and/or consequence of failure due to soil ingress and/or void issues, and may be flagged for immediate action, for example, by scheduling maintenance. In another example, conduits that intersect with a tree’s structural root zone, in an area that has multiple records in a customer request system for flooding may be predicted to have a high likelihood of failure due to root ingress and may be flagged for root treatment, for example, by scheduling maintenance.

[0069] In some embodiments, the maintenance module 416 may also determine a respective consequence of the fault type.

[0070] In some embodiments, specific fault failure risk models (not shown) may be based on Decision Tree based model(s). Outputs from the specific fault failure risk models (not shown) may be combined to provide the overall risk or likelihood of failure of the conduit 100 as output by the predictive model 420. For example, a Bayesian predictor may be used to combine the outputs of the specific fault failure risk models (not shown) into the output of the predictive model 42.

[0071] In some embodiments, the overall risk or likelihood of failure of the conduit 100 may be combined with the determined consequences of failure of the specific fault failure risk model(s) to determine a suggested or recommended time for performing maintenance.

[0072] In some embodiments, the predictive model 420 is a statistical or mathematical based model such as a best fit model. The best fit model may be derived from a dataset extracted from the historical conduit data and/or external data sources, such as those providing information about environmental factors, buildings, vegetation, transport etc. The dataset may comprise information about multiple example conduits 100 with one or more common attributes (structural, environmental and/or operational information) to the conduit 100 being considered, i.e., the candidate conduit. Sample bias may be mitigated by selected a dataset that well represents the candidate 100 in terms of similarity of attributes or features for generating the predictive model 420. [0073] For example, in some embodiments, by plotting the deterioration of the multiple example conduits overtime (for example, condition of conduit against time), a best fit curve to the data may be determined, i.e., one that minimises the sum of the squares of the differences between measured and predicted values. The predictive model 420 can then use the determined best fit curve to estimate or predict when a given candidate conduit 100 with a current condition level will reach a threshold condition level. For example, condition information over time about conduits having a similar age, depth, material type, and proximity to trees may be used to determine a best fit curve representative of how the candidate conduit 100 is likely to progress between grades. Similarly, plots of risk of fault for one or more fault types for multiple examples may be determined and a best fit curve function used to determine a predictive model for determining risk of failure of the conduit 100.

[0074] The dataset used to generate the predictive model 420 may comprise measures or values for a structural condition of the conduits 100 over time, and may also comprise values or measures for one or more structural, operational, and/or environmental characteristics or attributes. Values for the one or more structural, operational, and/or environmental characteristics or attributes may be stored in an asset record associated with the particular asset, for example in database 404, and/or may be retrieved or obtained from third party systems or servers, or from user input. In some embodiments, missing values for attributes, such as an age of an example conduit, may be inferred from available information. The available information may be information related to other assets or data sets that would not normally be considered relevant when managing the conduit. For example, an age of a particular conduit may be estimated or inferred from build dates of surrounding assets in other or similar asset classes (e.g., assets in the proximity of the conduit).

[0075] In some embodiments, missing values for condition measure, for example, at particular times, may be interpolated or extrapolated from the available data. This may be achieved, for example, by performing linear or polynomial/curvilinear regression to predict unknown or missing values of variables, and using a best fit equation to estimate the unknown value(s). [0076] The dataset from which the predictive model 420 is generated (via ML learning statistical inference) may be updated and added to regularly and the predictive model may be retrained or re-evaluated using the updated data via a view to improve its performance.

[0077] The system 402 further comprises a communications module 422 to facilitate communications with components of the asset maintenance network 400 across the communications network 408, such as the one or more computing devices 406, database 404 and/or other systems or servers (not shown). The communications module 422 may comprise a combination of network interface hardware and network interface software suitable for establishing, maintaining and facilitating communication over a relevant communication channel.

[0078] The database 404 may form part of, or be local to, the system 402, or may be remote from and accessible to the system 402. The database 404 may be configured to store a plurality of structural data 300 associated with a respective plurality of drainage assets 100. The plurality of structural data 300 may form part of the existing (historical) maintenance data for the conduit 100. Each set of structural data 300 may be associated with one or more identifiers. In some embodiments, the one or more sets of structural data 300 may be associated with a unique identifier configured to identify one set of structural data 300 from another. In some embodiments, the one or more sets of structural data 300 may be associated with an identifier indicative of the drainage asset 100 associated with the set of structural data 300. For example, the database may comprise a plurality of asset records, each asset record having an asset identifier and comprising details or information associated with a particular asset. The information may include structural, environmental, and/or operational information, and may include text, image and video data, for example.

[0079] The database 404 may be configured to store details about the environmental attributes or characteristics relating to the conduit 100. As explained above, the database 404 may comprise similar information about a plurality of conduits being managed or monitored by the system 402 in the network 400. For example, such environmental attributes may comprise the geographical location of the conduit 100, soil and/or ground conditions in a vicinity of the conduit 100, and/or the location or proximity of nearby plants and/or trees relative to the conduit 100 (to account for the risk of root ingress). The database 404 may be configured to store details about the operational attributes or characteristics relating to the conduit 100, as discussed in more detail below.

[0080] The computing device(s) 406 may be a mobile device, a tablet, a laptop computer, a smart sensor or any other suitable device. The computing device(s) 406 may comprise one or more processors 424 and memory 426 storing instructions (e.g. program code), which when executed by the processor(s) 424, causes the computing device(s) 406 to perform certain functionality, including, for example, providing inputs to and outputs from the method 500 of Fig. 5 or the method 600 of Fig. 6, as discussed below.

[0081] To this end, memory 426 may comprise an asset management application 428. The asset management application 428 may be configured to communicate with the system 402 and/or the database 404 to allow users to provide inputs to the method 500 and/or the method 600. The asset management application 428 may similarly communicate with the system 402 and/or the database 404 to provide outputs from the method 500 and/or the method 600 to the user. Memory may comprise a web browser 432 to access, retrieve and display, for example, on a user interface 430, content from system 402. The web browser 432 may further receive input from a user via the user interface 430 and provide those inputs to the system 402.

[0082] The computer device(s) 406 may comprise the user interface 430 to provide outputs to users, such as an operator or maintenance worker. The user interface 430 may comprise one or more displays, touchscreens, light indicators (LEDs), sound generators and/or haptic generators which may be configured to provide feedback (e.g. visual, auditory or haptic feedback) to the user. The user interface 430 displays the structural data 300 for a drainage asset selected by the user. [0083] The computing device(s) 406 may comprise a network interface to facilitate communication with the components of the communications network 406, such as the system 402 and/or database 404 and/or other computing devices.

[0084] Fig. 5 is a process flow diagram of a method 500 for managing the structural condition of a drainage asset, such as the conduit 100. The method 500 may broadly comprise the steps of: (i) assessing or determining the structural condition of the conduit 100; and (ii) determining a future time for maintenance operations based on the determined structural condition. The method 500 is a computer-implemented method, and may be implemented, for example, by the system 402 executing computer executable instructions stored in memory 412 of the system 402, such as maintenance module 416, and in some embodiments, the asset management module 414 and the maintenance module 416.

[0085] At 502, the system 402 determines a set of structural data 300 relating to a drainage asset, such as the conduit 100. The set of structural data 300 may comprise defect data about the drainage asset.

[0086] In some embodiments, the set of structural data 300 is received from a computing device 406 over the communications network 408. For example, the structural data 300 may comprise video footage, images and/or comments captured using the computing device 406. In some embodiments, the set of structural data 300 is retrieved from database 404, using for example, an identifier of the asset.

[0087] At 504, the system 402 determines an overall length of the drainage asset 100. For example, the system 402 may determine the overall length of the conduit 100 from the structural data 300.

[0088] At 506, the system 402 determines an overall condition measure (such as a value or score) of the drainage asset 100 based on the set of structural defect data and the overall length. The overall condition measure represents the structural condition of the drainage asset 100. The overall condition measure represents the internal and/or external structural condition of the drainage asset 100.

[0089] In some embodiments, the system 402 may be configured to determine the overall condition measure of the drainage asset according to method 600, as depicted in Fig. 6. The method 600 is a computer- implemented method, and may be implemented, for example, by the system 402 executing computer executable instructions stored in memory 412 of the system 402, such as the asset management module 414.

[0090] At 602, the system 402 divides the overall length of the drainage asset into a plurality of sections, each section having a respective section length.

[0091] For example, the conduit 100 in Fig. 1 is physically constructed from two lengths of pipe, but for the purposes of assessment may be divided into 3 sections. The boundaries of each section may be arbitrarily placed along the length of the conduit 100. Each sector or section may be of similar or relatively equal length. In some embodiments, at least some of the sectors or sections may have different lengths - unequal lengths may be desirable to align the boundaries of each section with certain real-world structural features of the conduit 100 (e.g. a particular seam 120, or a junction) for the operator to more easily identify particular sections of the conduit 100 during inspection.

[0092] At 604, the system 402 assigns a local condition measure to each section of the plurality of sections. The local condition measure may be based on the defect data of the conduit. For example, the local condition may be based on one or more of: (i) one or more locations of defects along a length of the drainage asset; (ii) an extent of one or more defects; and (iii) a type of one or more defects. The local condition may be based on the point defect data with the defect extent data for each section. The local condition measure may represent an internal and/or an external condition of the asset.

[0093] In some embodiments, the system 402 may assign a numerical score and/or a grade based on the codes logged by the operator (corresponding to observed defects 200). These codes may be used to determine an overall grade or score which represents the overall structural condition of the conduit 100. The numeric score may be according to the relevant standard such as WSAA’s WSA05 in Australia, or NASSCO, PACP, MACP, LACP, Wrc and ISYBAU internationally.

[0094] At 606, the system 402 groups the sections having the same local condition measure.

[0095] In embodiments where the sections may comprise different lengths, the system 402 may add up the respective section lengths. This provides a total length of the sections having the same local condition measure. For example, there may be a Im section and a 2m section each having a local condition measure of “4”, which means a total of 3m of the conduit 100 has a local condition measure of “4”.

[0096] In embodiments where all sections have equal length (a standard or fixed length), there may be no need to add up the lengths of the sections, and the system 402, may instead add up the number of sections having the same local condition measure and multiply by the standard length of the section.

[0097] At 608, the system 402 determines or calculates the total length of the sections having the same local condition measure as a proportion of the overall length. In the aforementioned example, a total of 3m of the conduit 100 has a local condition measure of “4”. Assuming the conduit 100 has an overall length of 10m, this means that 30% of the conduit 100 has a local condition measure of “4”.

[0098] At 610, the system 402 determines the overall condition measure of the drainage asset based on the calculated proportion. The calculated proportion indicates the amount of pipe that has a particular state of degradation. For example, sections of the conduit 100 with a condition measure of “4” or “5” would require urgent rehabilitation. Conversely, sections of the conduit 100 with a condition measure of “1” or “2” would be considered to be in good condition and not needing priority repair. A section of the conduit 100 with a condition measure of “3” would not be in urgent need of repair, but would need some routine maintenance such as cleaning, and be scheduled for re-inspection in a 5/10/15 year interval for example.

[0099] A conduit 100 having a low proportion or percentage of condition measure “4” or “5” would indicate that the conduit 100 should be evaluated for point repairs. For example, if 10% of the conduit 100 has a condition measure of “4”, this would indicate that serious defects 200 are limited to specific areas (or one specific area). In comparison, a high proportion or percentage (for example, 70% or more) would indicate relining or replacement of the entire conduit 100 may be required, as it would likely be more efficient than multiple spot repairs. An intermediate proportion, such as around the 40% -60% mark, may indicate local patch repairs.

[0100] Below is a tabular data set (Table 1) for an example of a conduit condition determination as depicted in a screenshot of Fig. 7. In the example shown in Fig. 7, the survey was incomplete and abandoned (for example, due to obstructions in the conduit) and the length of the conduit was based on data from a Geographic Information System (GIS). Based on the GIS data, the determined length of the conduit was 48 meters, while the inspected length was 10 meters (equating to about 21% of the determined length of the conduit). The screenshot is indicative of an output that may be presented to a user on computing device 406 via user interface 430.

Table 1 [0101] Condition Grades 3, 4 and 5 indicate sections of the conduit that are generally degraded and needing maintenance. In particular, Condition Grades 4 and 5 are considered to be sections of the conduit in most urgent need of maintenance/repair. In this example, 4.16% of the conduit length (being the sum of Condition Grades 3, 4 and 5) is considered to be “degraded” and generally need maintenance. Of this, 2.08% of the conduit length (being the sum of only Condition Grades 4 and 5) is considered to be in urgent need of maintenance. However, the condition of 79.17% of most of the conduit 100 was unable to be determined (and therefore designated as being “Condition unknown”). In such a scenario, the system 402 may determine the overall structural measure or condition as being “condition unknown”.

[0102] In some embodiments, the overall structural condition of the drainage asset may be represented as a peak score or as a mean score. The peak score may represent a condition of the drainage asset or conduit 100 on the basis of the worst defect 200 observed. The peak score may be reported at a single point, even if there are multiple points with similar defects 200. The peak score method can be advantageous as it identifies the conduit 100 with the defect 200 whose repair or monitoring should be prioritised.

[0103] However, focusing on peak score can divert attention and maintenance resources from other conduits 100 which have a lower peak score but a greater number of lower scored defects 200. For example, the conduit 100 with more (but lower scored) defects 200 may be prioritised instead of the conduit 100 that has a single higher scored defect 200 but is otherwise structurally sound. Accordingly, in some embodiments, the overall condition measure is based on a mean score method.

[0104] The mean score method involves averaging the score of all the defects 200 in the conduit 100. This provides an indication as to the overall deterioration, but the mean score can be heavily distorted with short conduit length (theoretically meaning fewer defects 200). Where there are few defects, a single high scoring defect 200 can also skew the mean score. The mean score may also not be able to account for combinations and clusters of defects. In some instances, the mean score or the peak score considered alone may therefore not provide an accurate picture of the overall structural condition, and a combined approach may be chosen when determining the maintenance operations and schedule.

[0105] In some embodiments, an overall condition measure may be scored on a 1 (excellent) to 5 (failed) scale. Unlike other asset classes, a conduit that has completely failed structurally may still provide full service. The risks to person and property at grade 4 and 5 however are significant, and ideally maintenance should be made immediately when these grades are reported. Maintenance is not only limited to cleaning and repair, but may include total or partial replacement of the conduit 100.

[0106] Referring again to Fig. 5, at 508, the system 402 determines a future time for performing a maintenance operation of the drainage asset or conduit 100 based on the overall structural condition measure.

[0107] In some embodiments, the system 402 provides the overall structural condition measure as an input to the predictive model 420 of the asset management module 414. The predictive model 420 may then provide, as an output, the future time for maintenance operations.

[0108] In some embodiments, the predictive model 420 is configured to determine the future time as a time when the overall condition measure of the drainage asset is predicted to reach a threshold overall condition measure. The threshold overall condition measure may be indicative of a recommendation or need for the conduit 100 to be serviced, or for a maintenance operation to be performed. In some embodiments, the maintenance operation may comprise repairing or replacing at least a component or section of the conduit 100. In some embodiments, the threshold overall condition measure is indicative of a condition measure of a conduit 100 at an end of life, or in other words, a point at which the lifespan of the conduit 100 is considered to be expired. In such embodiments, the maintenance operation may comprise removing the conduit 100, and may also comprise replacing the conduit 100. In some embodiments, the maintenance operation may comprise performing a visual inspection of the conduit 100 to determine a current state of the structural condition of the conduit 100.

[0109] In some embodiments, the predictive model 420 is configured to determine the future time as a time based on an overall risk or likelihood of failure of the conduit 100. In some embodiments, the predictive model 420 comprises a plurality of specific fault failure risk models (not shown), each fault failure risk model configured to predict the likelihood of a conduit failing as a result of a particular fault type. Each fault failure risk model may be considered to receive as inputs, attributes which may impact the likelihood of the conduit 100 failing as a result of the specific fault type. For example, fault types may include root ingress, soil ingress, void issues.

[0110] In some embodiments, the overall risk or likelihood of failure of the conduit 100 may be combined with the determined consequences of failure of the specific fault failure risk model(s) to determine a suggested or recommended time for performing maintenance.

[0111] When determining the future time for maintenance operations, the system 402 in performing method 400 may consider various other factors in addition to the structural overall condition measure of the conduit 100. For example, in some embodiments, the system 402 may be configured to determine a future time for performing a maintenance operation of the conduit 100 based on the overall condition measure and one or more characteristics or attributes of the conduit 100. The one or more characteristics may relate to structural, operational, and/or environmental characteristics. The predictive model 420 may be configured to receive the overall structural condition of the drainage asset in addition to measures for one or more structural, operational, and/or environmental characteristics or attributes, and to output the future time for maintenance operations. Values for the one or more structural, operational, and/or environmental characteristics or attributes may be stored in an asset record associated with the particular asset, for example in database 404, and/or may be retrieved or obtained from third party systems or servers, or from user input. [0112] The structural characteristics may include one or more of: an age of the conduit 100, a material or materials from which the conduit 100 is composed, dimensions (e.g. diameter or thickness of the conduit wall 110), shape, and/or orientation of the conduit 100. The environmental characteristics may include one or more of: geographical location of the conduit 100, soil and/or ground conditions in a vicinity of the conduit 100, and/or the location or proximity of nearby plants and/or trees relative to the conduit 100 (to account for the risk of root ingress). The operational characteristics may include one or more of: dead and/or live loading on the conduit 100, operating time the conduit 100, operating frequency the conduit 100, and/or maintenance history the conduit 100.

[0113] In some embodiments, the system 402 determines a set of environmental data relating to the drainage asset. The set of environmental data may comprise data relating to one or more environmental factors with a potential to impact the structural integrity of the drainage asset. The system may determine the future time for performing a maintenance operation of the drainage asset based on the set of environmental data.

For example, the data relating to one or more environmental factors may be provided as an input to the predictive model 420.

[0114] The one or more environmental factors comprises one or more of: location data of the asset; plant information in a vicinity of the asset; soil information of soil in a vicinity of the asset; a measure of the acidity of an environment immediately external to, or internal to the conduit 100; a measure of dead or live loading.

[0115] In some embodiments, information about the environmental factors associated with a conduit may be stored in an asset record for the conduit in database 404. The information may be updated periodically or on an ad hoc basis.

[0116] In some embodiments, location information for the asset may be stored in the associated asset record in the database 404. For example, the asset record may include a Global Positioning System (GPS) location for the asset. The system 404 (for example, the asset management module 414) may be configured to access environmental records using the location information for the asset to determine values or measures for any environmental that may impact the rate of degradation the conduit 100 at, in or near the vicinity of the asset.

[0117] An example of a resource containing soil data is the Australian Soil Resource Information System (ASRIS) by the Commonwealth Scientific and Industrial Research Organisation (CSIRO), colloquially known as the “Soil Atlas”. Soil data may be provided as an input to the predictive model 420. Soil data may comprise information on soil features such as soil pH level/acidity, salinity, water, nutrient, and/or clay content of the soil. For example, in areas of known acid sulphate soil, an underground drainage asset which would otherwise have a 100 year expected lifespan might instead have a reduced lifespan of 40 years, and accordingly may reach a threshold condition level whereby maintenance of the conduit would be recommended sooner that if the soil data were not taken into consideration.

[0118] Similarly, and as discussed above, other environmental factors that may impact the rate of deterioration or degradation of the conduit 100 may include the salinity of the air in the vicinity of the conduit 100, the acidity of fluid in the conduit 100, dead loading (e.g. the weight of soil and/or buildings above the conduit 100 and/or live loading (e.g. vibrations from traffic). If the conduit 100 degrades to allow some of the surrounding soil to enter the conduit 100, the soil leaves behind a void which may be likely to collapse.

[0119] In some embodiments, plant information in a vicinity of the asset (e.g., relative to the location or position information of the conduit) may be taken into consideration and provided as an input to the prediction model 420. Plant information may include location of nearby plants, canopy size of nearby plants, and/or extent of plant root spread.

[0120] In some embodiments, the asset management module 414 is configured to determine an impact or potential impact plants or trees in the vicinity of drainage assets or conduits in a drainage network may have on the deteriorations of the conduits of the drainage network. The asset management module 414 may determine a current or most recently determined SRZ size of vegetation in the vicinity of the drainage asset being considered.

[0121] An example screenshot 800 of a street view or map with an overlay 810 of a drainage network 820 and vegetation is depicted in Fig. 8. The drainage network 820 (shown in Fig. 8 as bold lines for clarity) comprises a plurality of the conduits 100. The vegetation includes various trees, represented by the “cloud- shaped” dashed lines 830. Trees (specifically their trunks) are identified in the overlay 810 using an icon 812 comprising a triangle 840. In the overlay 810, the icon 812 further comprises an inner circle 842 surrounding the triangle 840, and an outer circle 844 surrounding the inner circle 842. The inner circle 842 is indicative of a determined structural root zone of the tree (e.g. the SRZ) and the outer circle 844 is indicative of a determined protection zone of the tree (e.g. the TPZ). Asset management application 428 or web browser of computing device 406 may be used to communicate with the asset management module 414 of the system 402 to present or display the map with overlay 810 on the user interface 430 of the computing device 406.

[0122] The drainage network 820 further comprises a plurality of pits 850, which are at or near ground level and provide maintenance access to the underground conduits 100. The overlay 810 may also show the location of these pits 850 and conduits 100 relative to the SRZ 842 and TPZ 844 of the tree(s) 830. Fig. 8 shows a first pit 851 and a second pit 852 which are outside the SRZ 842 and TPZ 844 of nearby trees. Fig. 8 shows a third pit 853 which is (at least partly) within the TPZ 844 of a nearby tree. The third pit 853 is connected to a conduit 100, a portion 822 of which also falls within the same TPZ 844 as the third pit 853. Fig. 8 shows a fourth pit 854 which is in the SRZ 842 of a nearby tree. The fourth pit 854 is connected to a conduit 100A, a first portion 824 of which also falls within the same SRZ 842 as the fourth pit 854. A second portion 826 of the conduit 100A falls within the associated TPZ 844. Based on the location of the pits 850 and conduits 100 relative to the SRZ 842 and TPZ 844, the system 402 may determine which conduits 100 and/or pits 850 are at risk of root ingress. The system 402 may indicate the risk and/or condition grade of these pits 850 and conduits 100. In some embodiments, the system 402 shows the risk and/or condition grade on the overlay 810. The system 402 may show any conduits 100 (or portions thereof) and pits 850 that are in or overlapping with the SRZ 842 in a first colour, such as red (not shown). For example, conduit portion 824 and pit 854 may be coloured red as they are within the SRZ 842 of a nearby tree. This may indicate a high risk to the drainage asset and/or the tree. The system 402 may show any conduits 100 (or portions thereof) and pits 850 that are in or overlapping with the TPZ 844 in a second colour, such as orange (not shown). For example, conduit portions 822, 826 and pit 853 may be coloured red as they are within the TPZ 844 of a nearby tree. This may indicate a reduced/medium risk to the drainage asset and/or the tree. Any conduits 100 (or portions thereof) and pits 850 that fall outside the TPZ 844, such as pits 851, 852, may be shown in a third colour, such as blue (not shown). This may indicate a lower or zero risk to the drainage asset and/or the tree. These colours may be shown in the overlay 810 to facilitate visual identification of at-risk conduits 100 (or portions thereof) and pits 850 by the user of device 406. In some embodiments, the current and/or a projected or estimated future SRZ may assist in determining maintenance recommendations such as the use of root control measures including root barriers to reduce or the guide the spread of roots, thereby reducing or preventing the risk of root ingress into nearby conduits 100. The overlay 810 may also show the drainage network 820 and trees 830 relative to other infrastructure, such as a road 860. This may help maintenance crews to determine vehicle access as well as to locate the relevant drainage asset.

[0123] In some embodiments, the system 402 determines a set of operational data relating to the drainage asset. The set of operational data may comprise data relating to current or most recently determined operational performance of the drainage asset. The operational data may be indicative of how well the drainage asset is performing and may provide some insight or predictive value as to how the drainage asset may deteriorate over time. The system 402 may determine the future time for performing a maintenance operation of the drainage asset based on the set of operational data. For example, the operational data may be provided as an input to the predictive model 420. [0124] The operational data may comprise one or more of: an operating time of the asset; an operating frequency of the asset; a maintenance history of the asset; and a current throughput of material through the asset.

[0125] In some embodiments, the system 404 may determine an operating efficiency of the conduit 100. In some embodiments, the system 404 receives a measure of the operating efficiency of the conduit from a remote system (not shown) or computing device 406, for example, as may be input by a user. In some embodiments, the system 404 retrieves the operating efficiency of the conduit from an asset record in the database 404, using an asset identifier, for example.

[0126] In some embodiments, the system 404 is configured to calculate the operating efficiency of the conduit. For example, the operating efficiency may be based on a comparison of the conduit 100’s operating capacity and its expected capacity. The operating capacity of the drainage asset may be calculated by determining the size of blockages and/or number of defects 200 in the conduit 100, for example. The expected capacity of the conduit 100 may be determined as a calculated throughput (amount of flow) of the conduit 100 when newly installed. The throughput of the conduit 100 may be defined by size of the conduit 100, the capacity of any pumps in the drainage network, and the capacity of other conduits 100 upstream or downstream.

[0127] In some embodiments, the system 402 (for example, the asset management module 414) may be configured to determine an initial operating efficiency and/or capacity of the first drainage asset based on a design throughput when the asset was new, compare the design throughput to the current throughput and determine a current operating efficiency and/or capacity of the first drainage asset.

[0128] In some embodiments, the system 402 schedules the maintenance operation based on the determined future time for performing maintenance. A suitable scheduled time for performing the maintenance may be further based on the type of maintenance operation required, the desired operating capacity of the conduit 100, and/or the desired expected lifespan of the conduit 100. [0129] In some embodiments, maintenance operations are prioritised before being scheduled. For example, an importance of the particular conduit 100 to the overall network of conduit may be considered in determining the future time for performing maintenance. Each maintenance operation to be performed may be assigned an action priority based on its relative importance and the maintenance operation scheduled according to the action priority.

[0130] In some embodiments, maintenance operations to be scheduled may be grouped or organised into list(s) of similar work(s) to be performed, for example with a similar time period and/or in a specific geographical location or locations(s). The system 402 may be configured to determine the future time for performing maintenance of the conduit 100 based on the list of similar work(s) to be performed. Example works may include root cutting, patching, full pipe reline, immediate CCTV, and/ replace/upgrade. This may improve resource planning and efficiency.

[0131] Scheduling the maintenance operation may comprise generating a work item or order identifying the conduit 100 and a date, or date range for maintenance to be performed. In some embodiments, the system 402 is configured to assign the work item to a human operator to go to the installation site of the conduit 100. In some embodiments, scheduling the maintenance operation may comprise causing the system 402 to adjust operating parameters for the conduit or to transmit instructions to a control device (not shown) associated with the operation of the conduit to adjust operating parameters for the conduit 100. For example, flow may be diverted to other drainage assets. This may reduce or remove the load on the conduit 100 until repair or replacement of the conduit 100 can be completed. Adjusting the operating parameters for the conduit 100 may be done remotely, for example if the conduit 100 is part of a drainage network which is connected to a computer network.

[0132] In some embodiments, an operator conducting the maintenance may access an asset record associated with the drainage asset being investigated and/or worked on using the drainage asset tracking application 428 stored in memory of the computing device 406 such as a tablet or smart phone. For example the operator may access the asset record to assist the operator in navigating the camera through the conduit 100.

The drainage asset tracking application of the computing device 406 may be configured to receive information including logs, codes, and/or comment on the defects 200 observed during the inspection process. Where the assets are located underground, visual inspection of the exterior of the asset is not possible without excavation of the surrounding soil.

[0133] It will be appreciated by persons skilled in the art that numerous variations and/or modifications may be made to the above-described embodiments, without departing from the broad general scope of the present disclosure. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive.