Introduction to 'Artificial intelligence in failure analysis of transportation infrastructure and materials'

Transportation infrastructures (roads, bridges, tunnels, stations, airports, subways) are crucial to modern society. Failures can cause significant public damage. Artificial intelligence (AI) has emerged as a powerful tool for understanding and analyzing these failures. This theme issue showcases recent advancements in AI's application to failure analysis in transportation infrastructure and materials. The introduction highlights the importance of AI in addressing the challenges of detecting and preventing infrastructure failures, emphasizing the timeliness and relevance of the research presented in the theme issue. The increasing complexity and scale of transportation networks demand efficient and accurate methods for failure detection and prediction. AI offers a powerful solution by automating data analysis, pattern recognition, and predictive modeling. This reduces reliance on traditional, often time-consuming and labor-intensive methods, ultimately leading to improved safety, reduced costs, and more efficient maintenance strategies. The theme issue explores how AI techniques are revolutionizing different aspects of transportation infrastructure maintenance and management, moving beyond simple detection towards predictive modeling and proactive mitigation strategies.

The introduction extensively cites various research papers (referenced by numbers [1]-[12] in the provided text) focusing on AI applications within the field. These papers cover different aspects of transportation infrastructure, including road crack detection using deep convolutional neural networks and transformer networks, analysis of pavement conditions using falling weight deflectometer (FWD) tests and AI-based model updating, fatigue life prediction of asphalt pavement using hybrid AI-based models, prediction of urban road performance using recurrent neural networks, dielectric modeling of asphalt for electrified roads, automatic pavement texture recognition using few-shot learning, assessment of autonomous vehicle impacts on pavement performance, stability assessment of shield tunnels using multi-objective data mining, identification of segment joint failure in tunnels using machine learning, and rail corrugation recognition using machine learning. This literature review implicitly demonstrates the significant body of existing research that the theme issue builds upon and expands. The papers demonstrate the diverse applications of AI across various aspects of transportation infrastructure, highlighting the breadth and depth of the field.

The methodology section is implicitly described through the summary of the included papers. Different AI techniques are used in the research papers summarized here. These include: * **Deep Convolutional Neural Networks (CNNs):** Used for automatic identification of pavement cracks, achieving accuracy levels up to 99%. Specific architectures like Mask R-CNN are mentioned. * **Transformer Networks:** An alternative approach (TransCrack) is presented for road crack detection, leveraging a sequence-to-sequence perspective to model long-range dependencies. * **Falling Weight Deflectometer (FWD) Dispersion Curve Method:** A new methodology is introduced for FWD data analysis, focusing on deflection-time history to calculate pavement modulus profiles. Modifications to existing FWD devices are suggested for improved data acquisition. * **AI-based Finite Element Model Updating:** This approach combines finite element models with FWD tests and optimization algorithms to determine asphalt pavement condition. * **Recurrent Neural Networks (RNNs):** Specifically, LSTM and GRU networks are used to predict urban road performance, considering maintenance costs and historical pavement data. * **Hybrid AI Approaches:** Combines data-driven AI model updating with model-driven mechanistic equations for fatigue life prediction of asphalt pavement. * **Dielectric Modeling:** Develops a nonlinear fitting model to estimate the dielectric loss factor and a prediction model for the dielectric constant of asphalt mixtures, considering temperature effects. * **Few-Shot Learning:** A Siamese network-based model is used for automatic pavement texture recognition with limited datasets. * **Gradient Boosting Ensemble Learning:** Used to predict pavement performance (IRI) based on driving patterns and other context variables from human-driven and autonomous vehicles. * **Multi-objective Data Mining:** Includes rough set algorithms and BP neural networks for stability assessment of shield tunnels. * **Back-propagation Neural Networks (BPNN):** Used for identifying segment joint failure in underground tunnels based on tunnel settlement curves. * **Particle Probabilistic Neural Network (PPNN):** Combined with particle swarm optimization for rail corrugation recognition from metro vehicle response and noise. * **Graph Convolution Network-Informer (GCN-Informer) and Gaussian Bayes Models:** Used for passenger flow anomaly detection in urban rail transit networks.

The key findings are dispersed across the various studies summarized: * **Road Crack Detection:** High accuracy (up to 99%) achieved in automatic road crack detection using optimized deep CNN models and transformer-based approaches, showing superior performance in contiguous crack recognition and fine-grained profile extraction. * **Pavement Modulus Calculation:** The FWD dispersion curve method provides a new approach for determining pavement modulus profiles, offering insights beyond traditional deflection-based analysis. * **Asphalt Pavement Condition Assessment:** AI-based model updating methods allow for accurate determination of asphalt pavement condition using FWD tests and finite element models. * **Urban Road Performance Prediction:** Recurrent neural networks, considering maintenance costs, significantly improved the prediction accuracy of urban road performance. * **Asphalt Pavement Fatigue Life Prediction:** A hybrid approach combining AI-based model updating and mechanistic equations accurately predicts the fatigue life of in-service asphalt pavement. * **Dielectric Properties of Asphalt:** Models are developed to accurately predict the dielectric properties of asphalt mixtures, crucial for the development of electrified roads. * **Pavement Texture Recognition:** A lightweight few-shot learning model achieves high accuracy (89.8%) in classifying pavement textures using limited datasets. * **Autonomous Vehicle Impact on Pavement:** Autonomous vehicles show a tendency to deteriorate pavements faster (8.1% on average) compared to human-driven vehicles. * **Shield Tunnel Stability Assessment:** A multi-objective data mining approach provides an accurate and efficient method for assessing the stability of shield tunnels, improving decision-making during construction. * **Tunnel Segment Joint Failure Identification:** Machine learning models enable accurate identification of segment joint failure in underground tunnels, reducing labor costs. * **Rail Corrugation Recognition:** Machine learning models achieve high accuracy (96.43% and 95.40%) in recognizing rail corrugation from metro vehicle response and noise data. * **Passenger Flow Anomaly Detection:** A novel GCN-informer and Gaussian Bayes model-based framework efficiently detects anomalies in urban rail transit networks.

The theme issue demonstrates the significant potential of AI in revolutionizing failure analysis and management of transportation infrastructure. The diverse applications of various AI techniques highlight the versatility and adaptability of AI to address different challenges across various transportation systems. The high accuracy rates achieved in several studies showcase the effectiveness of AI in automating tasks and improving the efficiency of inspection and maintenance processes. The findings related to autonomous vehicles' impact on pavement suggest a need for further research into the long-term effects of autonomous driving on infrastructure maintenance and design. The integration of AI in existing testing methods (like FWD) highlights the value of combining traditional techniques with advanced data analysis capabilities. The studies contribute to the development of proactive, predictive maintenance strategies, moving away from reactive repairs towards more efficient resource allocation and improved overall infrastructure lifespan. These improvements are crucial given the increasing demands on transportation systems and the need for resilient and sustainable infrastructure.

This theme issue provides a comprehensive overview of cutting-edge AI applications in transportation infrastructure failure analysis. The diverse range of studies and the high accuracy achieved in various tasks highlight the transformative potential of AI in improving infrastructure safety, efficiency, and sustainability. Future research should focus on integrating these AI-driven solutions into real-world applications, developing more robust and generalized models, and exploring the ethical and societal implications of widespread AI adoption in infrastructure management. Further research should also address the limitations of current methods and explore new AI techniques to address evolving challenges in the field.

While the studies presented demonstrate significant advancements, certain limitations exist. The generalizability of findings might be limited by the specific datasets used in individual studies. Data availability and quality remain crucial challenges for many AI applications in infrastructure management. Further research is needed to address the potential biases in datasets and ensure the robustness of AI models across different contexts and geographic locations. The computational cost of some AI techniques, especially deep learning models, should be considered, and further research is needed to optimize model efficiency and reduce computational resource requirements.