Monitoring the condition of road infrastructure is crucial for maintaining its structural integrity and ensuring safe transportation.This study proposes a deep learning framework based on Temporal Convolutional Networ...Monitoring the condition of road infrastructure is crucial for maintaining its structural integrity and ensuring safe transportation.This study proposes a deep learning framework based on Temporal Convolutional Networks(TCN)integrated with Adaptive Parametric Rectified Linear Unit(APReLU)to predict future road subbase strain trends.Our model leverages time-series strain data collected from embedded triaxial sensors within a national highway,spanning August 2021 to June 2022,to forecast strain dynamics critical for proactive maintenance planning.The TCN-APReLU architecture combines dilated causal convolutions to capture long-termdependencies and APReLU activation functions to adaptively model nonlinear strain patterns,addressing limitations of traditional ReLU in handling bidirectional strain signals(compressive and tensile).Comparative experiments demonstrate TCN-APReLU’s superior performance.These improvements highlight its enhanced accuracy in predicting strain accumulation under cyclic traffic loads,enabling maintenance teams to prioritize interventions 5-7 days before critical thresholds(e.g.,>100με)are exceeded.This work provides a robust data-driven solution for urban road health monitoring,emphasizing scalability through parallelizable convolutions and adaptability to sensor noise.Future extensions will integrate multi-modal data to further generalize predictions across diverse infrastructure scenarios.展开更多
Poor road conditions in underground mine tunnels can lead to decreased production efficiency and increased wear on production vehicles. A prototype system for road condition monitoring is presented in this paper to co...Poor road conditions in underground mine tunnels can lead to decreased production efficiency and increased wear on production vehicles. A prototype system for road condition monitoring is presented in this paper to counteract this. The system consists of three components i.e. localization, road monitoring, and scheduling. The localization of vehicles is performed using a Rao-Blackwellized extended particle filter, combining vehicle mounted sensors with signal strengths of Wi Fi access points. Two methods for road monitoring are described: a Kalman filter used together with a model of the vehicle suspension system, and a relative condition measure based on the power spectral density. Lastly, a method for taking automatic action on an ill-conditioned road segment is proposed in the form of a rescheduling algorithm.The scheduling algorithm is based on the large neighborhood search and is used to integrate road service activities in the short-term production schedule while minimizing introduced production disturbances.The system is demonstrated on experimental data collected in a Swedish underground mine.展开更多
The rising number of vehicles on roadways expedites the urge to increase efforts in implementing monitoring systems that look after road pavement conditions. This rising in number of vehicles on roadways also cause mo...The rising number of vehicles on roadways expedites the urge to increase efforts in implementing monitoring systems that look after road pavement conditions. This rising in number of vehicles on roadways also cause more damages and distresses on road pavement. Road pavement conditions should be accurately evaluated to identify the severity of pavement damages and types of pavement distress. Therefore, monitoring systems are considered a significant step of maintenance processes. Paved roads and unpaved roads require regular maintenance to provide for and preserve users' usability, accessibility, and safety. Transport agents and researches would spend a lot of time and money in inspecting some sections of the roadway surface;that inspection would then be followed by results recording and data analysis to diagnose the type of treatment required. These monitoring systems have been developed using various methods that include smart technologies and prepared equipment. Many related studies evaluate road pavement degradation and distress, while others focus on identifying the best maintenance monitoring approach in terms of time and cost. This paper set out to explore different monitoring techniques used to evaluate road pavement surface condition. Also, this study introduces dynamic and static monitoring systems used in both paved and unpaved roads to identify the severity of pavement degradations and types of pavement distress on road surfaces and also this study explains the used equipment in the previous monitoring studies.展开更多
The condition of the road infrastructure has severe impacts on the road safety, driving comfort, and on the rolling resistance. Therefore, the road infrastructure must be moni- tored comprehensively and in regular int...The condition of the road infrastructure has severe impacts on the road safety, driving comfort, and on the rolling resistance. Therefore, the road infrastructure must be moni- tored comprehensively and in regular intervals to identify damaged road segments and road hazards. Methods have been developed to comprehensively and automatically digitize the road infrastructure and estimate the road quality, which are based on vehicle sensors and a supervised machine learning classification. Since different types of vehicles have various suspension systems with different response functions, one classifier cannot be taken over to other vehicles. Usually, a high amount of time is needed to acquire training data for each individual vehicle and classifier. To address this problem, the methods to collect training data automatically for new vehicles based on the comparison of trajectories of untrained and trained vehicles have been developed. The results show that the method based on a k-dimensional tree and Euclidean distance performs best and is robust in transferring the information of the road surface from one vehicle to another. Furthermore, this method offers the possibility to merge the output and road infrastructure information from multiple vehicles to enable a more robust and precise prediction of the ground truth.展开更多
A road network is essential to a country’s transportation and socio-economic development.Its maintenance requires regular monitoring to guide maintenance decisions.Artificial intelligence now enables the automatic de...A road network is essential to a country’s transportation and socio-economic development.Its maintenance requires regular monitoring to guide maintenance decisions.Artificial intelligence now enables the automatic detection of damage,but monitoring a roadway does not end with detection.It also involves estimating the severity and extent of damage and determining the Is index.Therefore,this study allowed the development of a digital tool based on artificial intelligence and image processing for complete monitoring.This consists of detecting the roadway damages,then estimating their severity and extent to calculate the index Is.Four databases were designed from videos of damaged roads collected on different roads in Benin.These data were used in transfer learning to train the YOLOv9 and Roboflow 3.0 Object Detection models.A script was developed to estimate the extent of the degradations,setting a collection speed of 10 km/h,a picking height of 1.20 m and a viewing angle equal to 45˚,covering the entire width of the roadway.Another script determines the index Is by estimating the cracking and deformation indices,with possible corrections depending on the repairs present.The best model obtained,ROCNN4,results from training Roboflow 3.0 Object Detection with the fourth base.It detects 19 classes of degradations with a precision P of 90.8%,a mAP of 91.8%and a recall R of 89.5%.These results pave the way for better road maintenance planning by providing managers with a reliable and automated decision-support tool.They thus help optimize intervention costs and improve the durability of the road network.展开更多
基金Supported by open project fund of National Engineering Research Center of Digital Construction and Evaluation Technology of Urban Rail Transit(2024023).
文摘Monitoring the condition of road infrastructure is crucial for maintaining its structural integrity and ensuring safe transportation.This study proposes a deep learning framework based on Temporal Convolutional Networks(TCN)integrated with Adaptive Parametric Rectified Linear Unit(APReLU)to predict future road subbase strain trends.Our model leverages time-series strain data collected from embedded triaxial sensors within a national highway,spanning August 2021 to June 2022,to forecast strain dynamics critical for proactive maintenance planning.The TCN-APReLU architecture combines dilated causal convolutions to capture long-termdependencies and APReLU activation functions to adaptively model nonlinear strain patterns,addressing limitations of traditional ReLU in handling bidirectional strain signals(compressive and tensile).Comparative experiments demonstrate TCN-APReLU’s superior performance.These improvements highlight its enhanced accuracy in predicting strain accumulation under cyclic traffic loads,enabling maintenance teams to prioritize interventions 5-7 days before critical thresholds(e.g.,>100με)are exceeded.This work provides a robust data-driven solution for urban road health monitoring,emphasizing scalability through parallelizable convolutions and adaptability to sensor noise.Future extensions will integrate multi-modal data to further generalize predictions across diverse infrastructure scenarios.
基金partially supported by the Wallenberg AIAutonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation
文摘Poor road conditions in underground mine tunnels can lead to decreased production efficiency and increased wear on production vehicles. A prototype system for road condition monitoring is presented in this paper to counteract this. The system consists of three components i.e. localization, road monitoring, and scheduling. The localization of vehicles is performed using a Rao-Blackwellized extended particle filter, combining vehicle mounted sensors with signal strengths of Wi Fi access points. Two methods for road monitoring are described: a Kalman filter used together with a model of the vehicle suspension system, and a relative condition measure based on the power spectral density. Lastly, a method for taking automatic action on an ill-conditioned road segment is proposed in the form of a rescheduling algorithm.The scheduling algorithm is based on the large neighborhood search and is used to integrate road service activities in the short-term production schedule while minimizing introduced production disturbances.The system is demonstrated on experimental data collected in a Swedish underground mine.
文摘The rising number of vehicles on roadways expedites the urge to increase efforts in implementing monitoring systems that look after road pavement conditions. This rising in number of vehicles on roadways also cause more damages and distresses on road pavement. Road pavement conditions should be accurately evaluated to identify the severity of pavement damages and types of pavement distress. Therefore, monitoring systems are considered a significant step of maintenance processes. Paved roads and unpaved roads require regular maintenance to provide for and preserve users' usability, accessibility, and safety. Transport agents and researches would spend a lot of time and money in inspecting some sections of the roadway surface;that inspection would then be followed by results recording and data analysis to diagnose the type of treatment required. These monitoring systems have been developed using various methods that include smart technologies and prepared equipment. Many related studies evaluate road pavement degradation and distress, while others focus on identifying the best maintenance monitoring approach in terms of time and cost. This paper set out to explore different monitoring techniques used to evaluate road pavement surface condition. Also, this study introduces dynamic and static monitoring systems used in both paved and unpaved roads to identify the severity of pavement degradations and types of pavement distress on road surfaces and also this study explains the used equipment in the previous monitoring studies.
基金project of Technical Aspects of Monitoring the Acoustic Quality of Infrastructure in Road Transport(3714541000)commissioned by the German Federal Environment Agencyfunded by the Federal Ministry for the Environment,Nature Conservation,Building and Nuclear Safety,Germany,within the Environmental Research Plan 2014.
文摘The condition of the road infrastructure has severe impacts on the road safety, driving comfort, and on the rolling resistance. Therefore, the road infrastructure must be moni- tored comprehensively and in regular intervals to identify damaged road segments and road hazards. Methods have been developed to comprehensively and automatically digitize the road infrastructure and estimate the road quality, which are based on vehicle sensors and a supervised machine learning classification. Since different types of vehicles have various suspension systems with different response functions, one classifier cannot be taken over to other vehicles. Usually, a high amount of time is needed to acquire training data for each individual vehicle and classifier. To address this problem, the methods to collect training data automatically for new vehicles based on the comparison of trajectories of untrained and trained vehicles have been developed. The results show that the method based on a k-dimensional tree and Euclidean distance performs best and is robust in transferring the information of the road surface from one vehicle to another. Furthermore, this method offers the possibility to merge the output and road infrastructure information from multiple vehicles to enable a more robust and precise prediction of the ground truth.
文摘A road network is essential to a country’s transportation and socio-economic development.Its maintenance requires regular monitoring to guide maintenance decisions.Artificial intelligence now enables the automatic detection of damage,but monitoring a roadway does not end with detection.It also involves estimating the severity and extent of damage and determining the Is index.Therefore,this study allowed the development of a digital tool based on artificial intelligence and image processing for complete monitoring.This consists of detecting the roadway damages,then estimating their severity and extent to calculate the index Is.Four databases were designed from videos of damaged roads collected on different roads in Benin.These data were used in transfer learning to train the YOLOv9 and Roboflow 3.0 Object Detection models.A script was developed to estimate the extent of the degradations,setting a collection speed of 10 km/h,a picking height of 1.20 m and a viewing angle equal to 45˚,covering the entire width of the roadway.Another script determines the index Is by estimating the cracking and deformation indices,with possible corrections depending on the repairs present.The best model obtained,ROCNN4,results from training Roboflow 3.0 Object Detection with the fourth base.It detects 19 classes of degradations with a precision P of 90.8%,a mAP of 91.8%and a recall R of 89.5%.These results pave the way for better road maintenance planning by providing managers with a reliable and automated decision-support tool.They thus help optimize intervention costs and improve the durability of the road network.
