Bridge Maintenance Management integrated with AI tools will help in Predictive maintenance
Bridge Maintenance Management integrated with AI tools will help in Predictive maintenance
Bridges are the backbone of any Nation and it is categorized under Critical Infrastructure. Modern bridges are complex and with the rapid advancements in the construction of Highway infrastructures all over the world, the bridges are gradually showing multiunit characteristics. Safe and reliable operation of bridges are very much required to be ensured in the interest of any nation.
Following are some detrimental factors which may lead to deterioration in the various bridge elements causing harm to its operational life: –
Due to rapid urbanization, traffic volumes on highways are increasing day by day and sometimes this increment is exponential in nature;
Vehicle composition is also changing w.r.to size & number from the baseline traffic condition which leads to significant increase in load on the bridge structures;
Traffic speed also increases due to enhancement in quality of linking infrastructures;
Changes in existing environment and occurrence of natural calamities also affects the bridge quality and it adversely affects its performance abilities.
Bridges are important asset of any transport infrastructure and management of any such assets in the procurement stage requires most appropriate selection complying with all the prevailing national standards. During the operation and maintenance phase of a bridge, there is a need to ensure its conformance with quality performance parameters and it should have the quality of adaptable behavior in the fast-changing environment.
There may be sequential four stages for asset management of Bridges starting from the planning phase. Subsequent to this planning phase, there are Procurement/ Construction phase; Operation & Maintenance phase and finally there comes the disposal & decommissioning phase which marks the end of a bridge asset’s lifecycle. Any Bridge asset management system should be modular so as to answer the variation in objectives. It should be comprehensive by inclusion of the basic modules (IABSE Congress Ghent 2021) as below-
Bridge inventory;
Condition assessment of various element of the Bridge including the effect of aging. Load carrying capacity assessment is important to evaluate the risk to the users;
The various maintenance strategies with their cost implications and the optimal management option are to be determined. All bridges have socio-economic importance and the cost for it i.e. indirect cost evaluation is also necessary;
Financial allocation for optimum benefit after establishment of maintenance priorities is to be taken care of
Bridge Health monitoring (BHM) means the monitoring of its static and dynamic characteristic’s. This Monitoring consists of Technical Inspection i.e. visual assessment of the structure condition and quantitative assessment i.e. obtaining mechanical properties of materials and structures components by destructive tests in the conventional approach. These assessments help in timely depiction of defects and in the determination of residual capacity of the structure thereby facilitating the required intervention at the appropriate time.
Bridge maintenance involves the primary task of assessment of the structural performance ability of the bridge and subsequently making plan & taking decision for required appropriate interventions regarding its maintenance.
All over the world, different countries have recognized that the Bridge health monitoring has a crucial role in the safety of these assets and accordingly they have established management system to get the detail inventory of the existing operating conditions including the age of the bridges. Federal Highway Administration (FHWA) in USA is responsible for safe operation of the bridges. In USA, PONTIS renamed as BrM is the predominant bridge management system undertaken and managed by AASTHO. Likewise, Australia is using the Bridge-ASYST, MRWA and NSW. In Japan, MICHI, RPIBMS is currently in operation for the bridge management system.
An inventory is prepared based on topography and environmental conditions and this inventory shall include the condition of the bridge including its geometrical & material properties. The inventory will include the detail information found during previous inspection & corresponding maintenance activities of it. A damage assessment study is also required which shall be able to detect the damage, its characteristics and the part where damage is detected. Estimation of the remaining useful life is essential so as to take a proper decision regarding the continuance of the bridge under the present environmental factors.
Depending upon the functionality and life cycle assessment (durability aspect), there are different maintenance strategies followed by different countries and in each country have designated a body to apply & implement the strategy. These maintenance strategies may be categorized as corrective maintenance; Preventive maintenance and Predictive maintenance. Corrective maintenance is primarily based on restoration approach i.e. to restore the functionality. It is very costly among the three, due to its character of unplanned interventions in case of emergencies. The second i.e. the preventive maintenance works in line with the scheduled activities of maintenance work programme and in this case, there is little effect on the bridge overall performance. The most useful and cost-effective strategy is Predictive maintenance as it is need based and it utilizes the real time condition data for maintenance intervention.
Due to very large inventory of existing bridges and limited human & financial resources availability, it is not easy to visually inspect and assess the structural conditions of each and every bridge continuously in a conventional way (PIARC,2023). There are limitations of visual inspections as they cannot identify the sub-surface damages including corrosion in the reinforcements & concrete delamination. There is safety issues of the inspecting person involved in the visual inspections. Cost implications are also high due to demanding equipment such as high platforms and scaffoldings required for inspection. Further and the most important concerning factor is that due to discrepancies or difference in individual perceptions, there is inconsistencies in the inspection result.
Some countries are following the codal practice (relevant codes of their country) of inspection at prefixed periodic intervals, but this is also not so effective as sometimes damage in a bridge may occur just after the inspection and this damage is noticed only at the time of next scheduled inspection. It is to be noted here that even in case of, the Non-Destructive Testing (a test which are carried out without causing any damage to the structure), there are space constraints for the inspecting team due to the complicated surroundings of the structure & its facilities.
Now, there is felt for better quality of inventory on real time basis so as to have better control over the deterioration prediction and subsequently in better bridge asset management. With the help of modern IT technology and with the help of AI (AI is not new and it is already being used to some extent by many industries as well as by many governments. Development of AI is also not new, its theoretical & technological developments are undergoing since many years), it is now possible to monitor the static & dynamic characteristics of the bridge structures and to solve much more complexities which may cause trouble during operation period of a bridge life cycle.
Now a days, AI driven BHM is being increasingly used. Demand for Integration of machine learning, IoT enabled sensor networks, drone-based inspections in BHM are increasing so as to get a predictive maintenance model. On the basis of research findings, it has been felt that the machine learning algorithms is more accurate in the detection of cracks and fatigue than those by traditional inspection method. Predictive maintenance models optimize the investment requirement and helps in selecting the best intervention strategy. It also helps in minimizing the emergency repairs. Thus, this model enables in reducing the maintenance cost in comparison to the conventional approach by approximately 30 to 50 percent. The inspection time is also drastically reduced, damage detection efficiency is significantly improved and there is great savings in data acquisition time by adopting AI based BHM which includes IoT enabled wireless sensor networks & AI assisted drone inspections.
AI and Machine learning in Bridge inspections helps to automate image and data collection. AI and Machine learning algorithms is able to quickly analyze enormous data with accuracy and in the case of bridge inspections, the most indistinct signs of wear, any hairline cracks or other subtle sign of defects can be detected by the use of these algorithms. This makes it possible to understand the deterioration pattern of the bridges with clarity and helps in the successful implementation of predictive maintenance strategy.
Drones combined with intelligent algorithms can help in rapid inspection of bridge high risk component’s such as piers and abutments. Speeding data collection, real time reporting, enabling detection of cracks, structural anomalies, reinforcement corrosion, any hidden defects such as moisture trapping and material degradation are the benefits by a drone equipped with high resolution cameras, LIDAR and thermal imaging facilities.
When damage occurs inside a bridge member, this damage can be better depicted by the thermal image using the infrared thermography. Drones equipped with thermal cameras can detect easily the following defects by thermal imaging-
With the help of thermal imaging, temperature variations indicating presence of trapped moisture in the bridge element can be identified;
Through heat signatures, delamination in concrete can be detected;
Any constructional defects or weaknesses due to aging can be easily identified by thermal conductivity variations.
LIDAR drones can help in assessment of deformation and load distribution by making high precision models. AI integrated drones can process captured data in real time to identify any subtle cracks in the surface of the bridge and the embedded software helps in automate generation of reports indicating the severity of risks leading to forecasting of structural behavior and weaknesses in it.
Carbon foot print associated with the traditional method of inspection are also minimized by using the drones for bridge inspection.
With the capability improvements in Beyond Visual Line of Sight (BVLOS), it is now possible to use inspection of bridge by drones without any pilot i.e. UAV. Unmanned Aerial Vehicle (UAV) have the capability to take the optical base measurement in a fast manner with accuracy and with minimum interference. Similarly, Ubiquitous Bridge Inspection Robot System having a robotic arm integrated with a camera may be used to scan and generate map of the cracks in the bridge element.
Bridge monitoring sensors such as strain gauges, accelerometers, displacement sensors, temperature sensors, load cells, tilt sensors, corrosion sensors aid in the continuous structural health monitoring of the bridge and facilitate effective maintenance planning and accurate evaluation of risks. Integration of AI and Machine learning algorithms with the aforesaid monitoring sensors helps in predictive analysis with more accuracy. Sensor suites should be modular, scalable (ability to monitor bridges of different sizes, of different materials & with different load capacities), durable and it should be well- equipped with strain gauges, ultrasonic sensors, AI integrated traffic classification capacity and with all such advance tools.
Bridges of importance or bridges on important rivers/waterways needs continuous monitoring so as predict various events including force majeure events with an aim to safeguard human lives. Bridges of reinforced concrete are most vulnerable to corrosion and hence monitoring for cracks & stress in it are necessary. In the case of suspension and cable stayed bridges, cable tension and tower health are sensitive points to be monitored continuously.
For monitoring of a long span continuous bridge, a voluminous data is generated on a daily basis and in such case Wireless sensor network (WSN) is deployed. WSN is a layered network communication protocol which consists of five layers viz-physical layer, data link layer, network layer, transport layer and application layer with each layer having individual roles in the overall performance. Due to ease in installation, low cost and excellent data collection capacity, WSN is a preferrable choice in BHM also. It can be used both for the existing bridges and the bridge under construction. If properly handled, this can help in the bridge health monitoring for load conditions, vibrations, displacement and strain (this is a measure for structural integrity) occurring in the bridge element. It can also be useful in the monitoring of bearings as well as of expansion joints. It can track movement, wear and potential damage of bridge critical components. Scour critical monitoring and fracture critical monitoring can also be done with the help of it.
Building Information Modelling (BIM) may be utilized for getting defect & deterioration information in the bridge structure and this can be done by using a digitalization method. With the help of BIM model, a detail and real-time digital representation of a building may be assessed with accuracy. In bridge case also, a smarter and automated management during the life-cycle of the bridge may be achieved by integrating BMS framework with the BIM model with the help of methods like structured query language, or machine learning and artificial intelligence algorithms. Here, defect condition in the bridge is better visualized by combining the bridge model with a bridge defect 3D BIM library (The BIM model library for bridge defects is designed on the basis of geometric features of the defect. The BIM models are categorized as damage model; corrosion model; deficiency model and distortion model).
After evaluating the bridge technical condition (Bridge is divided into three parts; sub-structure, super-structure and bridge decking for calculation of its technical condition and condition rating scores are evaluated on the basis of relevant Evaluation standards), the results are graded, displayed in separate color and then required maintenance intervention is suggested according to the technical condition scores of that particular bridge. With the integration of IoT (internet of things) in the BMS framework, it is easier to get a geometric representation of the bridge and planning for interventions has become easier once the life-cycle analysis is contemplated into the system. Improved structural analysis leading to better predictions of deteriorations makes it easy to decide the budget for intervention.
Any organization dealing with the bridge health monitoring (which includes real time intervention for predictive maintenance) should adopt such technology package which is capable to cater to all types of bridges in the inventory. It should be able to encompass & address; a combination of most advanced sensors, storage system i.e. securing the captured critical data (detailed history of the usage, environmental exposure and all the maintenance activities performed so as to manage the life cycle with optimal benefit) directly on the bridge and it should be able to secure communication technologies so that periodic data retrieval is possible by UAV or by any person.
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