Large-scale pavement distress inspection has gained attention recently. Traditional methods, involving dedicated vehicles and professionals, are effective for small-scale evaluations but are labor-intensive for extensive areas. Logistics vehicles, equipped with driving recorders, provide a vast resource of street view images covering urban roads, capturing pavement distress data. This paper proposes a novel framework leveraging logistics vehicle data for rapid distress detection and tracking. A deep convolutional neural network, enhanced with a dual-layer routing attention mechanism, improves detection precision for minor distresses. The optimized Boundary Box Regression (BBR) loss function increases accuracy for common distresses like cracks. A three-stage distress matching algorithm, based on an attentional graph neural network and adjacent-local-area matching, removes duplications and tracks distress deterioration. The Bernoulli function assesses minimal sampling frequency for road segments. Validated in Shanghai, this method achieves 79.6% mean Average Precision (mAP) and a 75.66% matching rate, enabling daily updates for timely maintenance decisions.

The increase in highway mileage and lifespan is driving up the demand for road maintenance. With most research focusing on corrective maintenance, remedial maintenance(such as sealing and patching) optimization is understudied. Oriented toward remedial maintenance, data-driven models often fall short due to difficulty in establishing and implementing the model under complex road conditions, while the experts’ decision lacks consistency amidst multifaceted factors. To address this gap, this paper proposes a fine-grained maintenance decision model that combines data-driven methods with expert knowledge through Reinforcement Learning from Expert Feedback (RLEF). The experts’ experience introduced in decision-making model could improve the engineering application ability of decisions. The research uses a pavement performance prediction model as the environment and applies reinforcement learning to optimize strategies in the decision model. Additionally, the model integrates multidimensional expert feedback into reward functions to better understand ambiguous decision rules. Real-world data validation demonstrates that the RLEF model can adapt to engineering scenarios and applications better as well as achieve superior cost-effectiveness.

Pavement distress, manifested as cracks, potholes, and rutting, significantly compromises road integrity and poses risks to drivers. Accurate prediction of pavement distress deterioration is essential for effective road management, cost reduction in maintenance, and improvement of traffic safety. However, real-world data on pavement distress is usually collected irregularly, resulting in uneven, asynchronous, and sparse spatial-temporal datasets. This hinders the application of existing spatial-temporal models, such as DCRNN, since they are only applicable to regularly and synchronously collected data. To overcome these challenges, we propose the Spatial-Temporal Graph Autoregression Network (STGAN), a novel graph neural network (GNN) model designed for accurately predicting irregular pavement distress deterioration using complex spatial-temporal data. Specifically, STGAN integrates the temporal domain into the spatial domain, creating a larger graph where nodes are represented by spatial-temporal tuples and edges are formed based on a similarity-based connection mechanism. Furthermore, based on the constructed spatiotemporal graph, we formulate pavement distress deterioration prediction as a graph autoregression task, i.e., the graph size increases incrementally and the prediction is performed sequentially. This is accomplished by a novel spatial-temporal attention mechanism deployed by the proposed STGAN model. Utilizing the ConTrack dataset, which contains pavement distress records collected from different locations in Shanghai, we demonstrate the superior performance of STGAN in capturing spatial-temporal correlations and addressing the aforementioned challenges. Experimental results further show that STGAN outperforms baseline models, and ablation studies confirm the effectiveness of its novel modules. Our findings contribute to promoting proactive road maintenance decision-making and ultimately enhancing road safety and resilience.

Pavement performance prediction is the foundation of maintenance decisions, which is the key problem of infrastructure management. Most prediction methods focus on section-based and annual deterioration on pavement, while it is hardly supporting daily and preventive maintenance plans. To fill in gaps in pavement forecasting for refined maintenance, this paper introduces a prediction model which is fine-grained on both temporal and spatial scales. Due to the coupling effects and action delay of multiple environmental factors, it is difficult to fathom and model the detailed deterioration process of pavement. Another problem is there are rare refined pavement datasets opening to public for research. Therefore, we establish a high-frequency and real-world pavement dataset and causal discovery is brought in to explicate the inner mechanism of the process. The proposed model first applies Partial Mutual Information from Mixed Embedding (PMIME) method for causal discovery, obtaining a causal graph and impact delays between factors and pavement performance. Based on this, we use an advanced pavement performance prediction model called Causal-Temporal Graph Convolution Network (CTGCN), combining the Graph Convolution Networks (GCNs) and the Long Short-Term Memory models (LSTMs) to capture causal features and temporal features simultaneously. We validate CTGCN model using collected datasets with two predictive time lengths. The experimental results prove that CTGCN model has better performance in both prediction accuracy and robustness than the state-of-art baseline. Dataset and more information are available at https://github.com/wowocai/CTGCN-dataset.

Analysis of pavement deterioration is critical for road maintenance. Many section-based pavement performance evaluation methodologies have been investigated to determine the deteriorating tendency from a macro perspective. However, little research shed light on the refined deterioration analysis for single distress, which is valuable for daily and preventive maintenance. This paper proposed a deep-learning-based tracking framework to construct a large-scale continuous observation data set for every distress. A deep learning model is applied to detect six types of distress automatically. Then we adopted the spatial clustering method to match the pavement images in the same scene. Finally, image feature matching and perspective conversion methods are adopted to track the distress in the same scene. Using the data collected from the bus driving recorder, we have realized the daily observation of over 270 kilometers of the urban road network. More than 14,000 pavement distress have been continuously tracked, proving this framework’s effectiveness. In addition, the features of pavement deterioration are further discussed. The results show that heavy rain will significantly accelerate road surface deterioration. Under its influence, an intact pavement may suddenly deteriorate into serious potholes within a day. The established continuous pavement distress tracking dataset is significant for distress-level performance prediction research.

Serious Road anomalies caused by bridge approach settlement, pavement rutting, etc., not only seriously affect traffic safety and user experience but also aggravate the damage of road structure. It is more relevant to automated vehicles (AVs) as they are currently not designed to measure the pavement roughness directly. Without prior-collected road anomalies information, AVs’ active suspension control system can only passively reduce the negative impact of road anomalies to a certain extent. This paper proposed a response-type road anomaly detection and evaluation method by collecting the vibration data from AVs. A mechanical estimation model for the height of anomaly (HoA) is constructed to evaluate the degree of road anomaly. Passenger’s comfort is evaluated by three featured indicators: maximal acceleration, weighted root-mean-square acceleration, and jerk. A full-car simulation model is programmed based on the Simulink platform to reveal the relationship among road anomalies, comfort, and speed, which helps design a steady driving velocity profile for AVs. The results show that the root-mean-square error of road anomalies estimation is about 0.63cm. AVs’ comfort can be improved significantly by employing the proposed steady driving strategies.

Vibration of concrete pavement contains valuable information in the time, space, and frequency domain, which is beneficial to loading characteristics identification and structural health monitoring. However, these multidimensional features have a significant locality and need to measure effectively. This paper proposes a novel method by using distributed fiber optic to measure the temporal, spatial, and spectral distribution of pavement vibration, as well as reconstruct them into a vibration field for feature analysis. First, a vibration monitoring system with designed units was developed to measure the vibration of concrete pavement. Then reconstruction and analysis methods of the vibration field were proposed to process the one-dimensional measured data. Finally, a series of experiments were conducted to validate the performance of the system and methods under different loading types, speeds, magnitude, and positions. According to the results of the impulse loading test, accelerated pavement test, and traffic loading test, it indicates the system can measure the vibration of concrete pavement with sufficient positioning precision (0.33 m), sampling frequency (2500 Hz), and frequency accuracy (<±1 Hz). Also, based on the data processing method, the vibration field can be reconstructed and analyzed effectively to reflect traffic behaviors like braking and lane change.