Railway track transitions are prone to uneven settlements and track geometry degradation. Traditional monitoring methods are limited in coverage, which highlights the need for novel solutions. This study proposes a method that systematically integrates the high spatial resolution of Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) with the broader coverage of Small Baseline

Recent advances in deep learning have enabled automated ground‐penetrating radar (GPR) image analysis, particularly through two‐stage models like mask region‐based convolutional neural (Mask R‐CNN) and single‐stage models like you only look once (YOLO), which are two mainstream approaches for object detection and segmentation tasks. Despite their potential, the limited comparative analysis of these

Near-fault pulse-like ground motions can cause severe damage to long-period engineering structures. A rapid and accurate identification method is essential for seismic design. Deep learning offers a solution by framing pulse-like motion identification as an image classification task. However, the application of deep learning models faces multiple challenges from data and models for pulse-like motion

Excavator safety and efficiency are crucial for construction progress. Monitoring their 3D poses is vital but often hampered by resource and accuracy issues with traditional methods. This paper presents a method to reconstruct the 3D poses of excavators using a cost‐effective monocular camera while considering physical constraints. The approach involves two steps: deep learning to identify 2D key points

Signal noise removal is an indispensable and critical procedure in obtaining clean pavement texture data for reliable pavement evaluation and management. Nevertheless, the presently established denoising approaches to pavement texture data still rely on traditional techniques that have long struggled with removing noise accurately and consistently. This paper innovatively initiates a one‐dimensional

Ground‐penetrating radar (GPR) offers nondestructive subsurface imaging but suffers from a trade‐off between frequency and penetration depth: High frequencies yield better resolution with limited depth, while low frequencies penetrate deeper with reduced detail. This paper introduces a novel frequency enhancement method for GPR images using domain‐adaptive generative adversarial networks. The proposed

Failure of buried pipelines can result in serious impacts, such as explosions, environmental contamination, and economic losses. Early detection and location of unexpected events is crucial to prevent such events. However, conventional monitoring methods exhibit limited generalization performance under varying environmental and operational conditions. Furthermore, the cross‐correlation‐based time difference

While semi‐autonomous drones are increasingly used for road infrastructure inspection, their insufficient ability to independently handle complex scenarios beyond initial job planning hinders their full potential. To address this, the paper proposes a human–drone collaborative inspection approach leveraging flexible surface electromyography (sEMG) for conveying inspectors' speech guidance to intelligent

Traffic congestion has substantially induced significant mobility and energy inefficiency. Many research challenges are identified in traffic signal control and management associated with artificial intelligence (AI)‐based models. For example, developing AI‐driven dynamic traffic system models that accurately capture high‐resolution traffic attributes and formulate robust control algorithms for traffic

Although estimating the International Roughness Index (IRI) is crucial, previous studies have faced challenges in addressing IRI prediction for asphalt concrete (AC) overlays on Portland cement concrete (PCC) pavements. This study introduces machine learning to predict the IRI of AC overlays on PCC pavements, focusing on incorporating pre‐overlay treatments to reflect their composite characteristics

Vision‐based monitoring of construction equipment is limited in scalability due to the high resource demands of collecting and labeling large datasets across diverse environments. This study proposes a framework that employs Zero‐Shot Learning (ZSL) and Multimodal Large Language Model (MLLM) to recognize construction equipment tasks from video frames without additional training data. The framework

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The number and proportion of aging infrastructures are increasing, thereby necessitating accurate inspection to ensure safety and structural stability. While computer vision and deep learning have been widely applied to concrete cracks, domain shift issues often result in the poor performance of pretrained models at new sites. To address this, a self‐supervised domain adaptation method using generative

The assembly and production of sandwich panels for prefabricated components is crucial for the safety of modular construction. Although computer vision has been widely applied in production quality and safety monitoring, the large‐scale differences among components and numerous background interference factors in sandwich panel prefabricated components pose substantial challenges. Therefore, maintaining

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Accurate long‐term deformation prediction is essential to ensure the structural security and ongoing stability of large water‐related concrete structures like ultra‐high arch dams. Traditional statistical regression and shallow machine learning approaches, due to their algorithmic constraints, often fail to comprehensively capture the complex temporal and spatial dependencies inherent in high‐dimensional

The prediction of freezing depth and thawing time of unbound pavement layers in cold regions is a critical task in pavement design and management. This study developed long short‐term memory (LSTM)‐based encoder–decoder models to accurately predict freezing depth and thawing time, with air temperature as the sole input variable. The models, which aim to offer a 14‐day advance prediction of the thawing

Computer vision‐based ship detection using extensively labeled images is crucial for visual maritime surveillance. However, such data collection is labor‐intensive and time‐demanding, which hinders the practical application of newly built ship inspection systems. Additionally, well‐trained detectors are usually deployed on resource‐constrained edge devices, highlighting the lowered complexity of deep

As a fundamental driving behavior, the accurate modeling of car‐following (CF) dynamics is essential for improving traffic flow and advancing autonomous driving technologies. Due to the stochastic nature of CF behaviors, the CF model parameters often exhibit heterogeneity (multimodal trends), distribution uncertainty, and parameter correlations. Most studies have examined correlations among CF model

Safe, efficient, and comfortable autonomous driving is essential for high‐quality transport service in an open road environment. However, most existing driving strategy learning approaches for autonomous driving struggle with varying driving environments, only working properly under certain scenarios. Therefore, this study proposes a novel hierarchical continual reinforcement learning (RL) framework

Real‐time prediction of subgrade settlement caused by shield tunneling is crucial in engineering applications. However, data‐driven methods are prone to overfitting, while physical methods rely on certain assumptions, making it difficult to select satisfactory parameters. Although there are currently physics‐data‐driven methods, they typically require extensive iterative calculations with physical

Infrastructure facilities require proper maintenance, including diagnosing structural durability and determining appropriate repair methods. Structural analysis is widely used to assess structural conditions, necessitating three‐dimensional (3D) data that accurately reflect the locations of deterioration. Therefore, we investigate a method to generate 3D data of manholes from single‐shot panoramic

Traffic signal timing and vehicle routing have been empirically demonstrated as the two most promising paradigms for network‐level urban road traffic management. However, mainstream studies based on Wardrop's theory continues to treat these two modules separately without achieving effective coupling. Optimization‐based methods face the challenge of increasing computational complexity as urban scales

Three‐dimensional (3D) digital design is extensively adopted in the architecture, engineering, consulting, operations, and maintenance (AECOM) industry to enhance collaboration among stakeholders. Although recommendation systems are commonly employed to facilitate purchasing in e‐commerce websites, none involves recommending online products to users from 3D building design models due to dimensional

Accurately predicting metro commuter flows under changing urban conditions is essential for guiding infrastructure investments and service planning. However, existing methods show limited adaptability to evolving urban conditions. To address this, we propose an adaptive graph sharing embedding cascade interaction network (AGSECIN), which establishes a dynamic mapping relationship between changing urban

The spoke cable‐net structure is a typical flexible tensile structure that relies solely on cables as load‐bearing components. Its unique topological characteristics, composed of ring cables and radial cables, determine that the main challenge in its form‐finding lies in controlling the spatial configuration of the inner ring. Existing computational methods primarily rely on numerical iteration based

In construction projects, resource fluctuations not only decrease work efficiency but also lead to high costs. However, current resource leveling strategies exhibit limitations in crew‐based scheduling mechanisms, constraining the flexibility of resource allocation. This study addresses the resource leveling problem of repetitive projects, focusing on leveling resource utilization from the perspective

Pose estimation of excavators is a fundamental yet challenging task with significant implications for intelligent construction. Traditional methods based on cameras or sensors are often limited by their ability to perceive spatial structures. To address this, 3D light detection and ranging has emerged as a promising paradigm for excavator pose estimation. However, these methods face significant challenges:

This study proposes a Markov chain-based deterioration prediction framework that incorporates spatial relationships between structural components. Despite spatial clustering and propagation of damage, conventional research has left spatial dependencies underexplored. This study constructs graph representations that reflect component adjacency and employs a Graph Transformer to capture both local and

This study presents an intelligent system for predicting incident reports (IRs) in sectorized water distribution networks, such as drains in sidewalks, lack of pressure, lack of water, leaks, or others, based on pressure and flow data. Currently, incident detection in the industry is highly inefficient, as it is always performed reactively—only after an incident has already occurred and its negative

Fast and accurate identification of traffic anomalies on highways is of utmost importance. This study presents an integrated framework for multiple traffic anomaly detection on highways using vehicle trajectories. The framework addresses both macroscopic congestion patterns and microscopic driving behaviors, offering a comprehensive solution that simultaneously detects multiple anomalies within a unified

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First‐order link‐based traffic flow models are computationally efficient in simulating freeway networks. However, the standard link transmission models fall short of reproducing traffic phenomena such as capacity drop (CD). Moreover, traffic control measures such as variable speed limits (VSLs) control may change the fundamental diagram and should be captured by traffic flow models. This study proposes

High‐resolution flood probability maps are instrumental for assessing flood risk but are often limited by the availability of historical data. Additionally, producing simulated data needed for creating probabilistic flood maps using physics‐based models involves significant computation and time effort, which inhibit its feasibility. To address this gap, this study introduces Precipitation‐Flood Depth

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Grain morphology is a fundamental characteristic of lunar soil that influences its mechanical properties, sintering behavior, and in situ resource utilization. However, traditional two‐dimensional imaging methods are time‐consuming and lack full three‐dimensional (3D) structural information. This study presents an automated deep learning‐based segmentation and reconstruction algorithm for high‐resolution

Transport infrastructure is vulnerable to crack formation and deterioration due to aging and repetitive loading. Accurate and timely crack assessment and prediction are crucial for effective road maintenance, but existing studies often rely on individual indicators such as crack types, attributes, and severity, which fail to capture the full complexity of crack deterioration. Furthermore, limited research

This paper explores a novel methodology for identifying prestress force (and bending rigidity) from the perspective of static deflection methods and derives an incremental beam–column equation (iBCE) by elucidating the mechanisms underlying the long‐ and short‐term behaviors, with particular emphasis on a physical system that disregards long‐term deflections, including self‐weight and equivalent lateral

Accurate and efficient prediction of wind pressure distributions on high‐rise building façades is crucial for mitigating structural risks in urban environments. Conventional approaches rely on extensive sensor networks, often hindered by cost, accessibility, and architectural limitations. This study proposes a novel hybrid machine learning (ML) framework that reconstructs high‐fidelity wind pressure

Existing methods for signal timing and vehicle trajectory coordination often rely on fixed‐phase designs or leading vehicle guidance, limiting efficiency in dynamic traffic and multi‐vehicle coordination. This study models signal timing as right‐of‐way allocation for each inbound lane at discrete time intervals and integrates trajectory planning into a mixed integer linear programming framework for

Climate change has intensified extreme weather events, with floods causing significant socioeconomic and environmental damage. Accurate flood forecasting is crucial for disaster preparedness and risk mitigation, yet traditional hydrodynamic models, while precise, are computationally prohibitive for real‐time applications. Machine learning surrogates, such as graph neural networks (GNNs), improve efficiency

Ensuring the safe, reliable, and cost‐efficient operation of transportation systems such as elevators is critical for the maintenance of civil infrastructures. The ability to monitor the health state and classify different operational states (elevator moving up/down, stopped, doors opening/closing) may lead to the development of intelligent solutions, such as diagnostics and predictive maintenance

The stability of embankment slopes for heavy‐haul railway foundations is essential for safe railway operations. Railway embankment slope stability datasets often rely on engineering judgment for analysis. The labor‐ and resource‐intensive processes of data preparation result in small dataset sizes. Machine learning analysis of small‐sample potential features is a key low‐cost approach for slope prediction

Climate change exacerbates natural disasters, demanding rapid damage and risk assessment. However, expert‐reliant analyses delay responses despite drone‐aided data collection. This study develops and compares multimodal AI approaches using advanced large language models (LLMs) for expert‐level landslide image analysis. We tackle landslide‐specific challenges: capturing nuanced geotechnical reasoning

High‐speed railway ballastless track structure crack detection usually has a high demand for the efficiency of crack detection technology. To overcome the limitation that current crack quantification methods usually require multiple steps, this paper proposes an efficient quantification method for track structure cracks using deep learning. This method applies the deep neural network (DNN) to the direct

This paper proposes a key origin–destination (OD) pairs perception reasoning (KODPR) approach for route guidance (RG) in urban traffic networks with numerous OD pairs. First, to reduce a real‐world RG problem's complexity with large OD sizes, a long‐term perception module is developed to identify a few critical OD pairs, making real‐world application feasible. Second, the issue of multi‐OD cooperation

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This study presents a scalable multi‐camera system (S‐MCS) for high‐precision displacement measurement and deformation monitoring of long‐span arch bridges during construction. Traditional methods such as robotic total stations (RTS) and single‐camera systems face limitations in dynamic scalability, synchronous multi‐point monitoring, and robustness against environmental disturbances. To address these

Roof inspections are crucial but perilous, necessitating safer and more cost‐effective solutions. While robots offer promising solutions to reduce fall risks, robotic vision systems face efficiency limitations due to computational constraints and scarce specialized data. This study presents real‐time roof defect segmentation network (RRD‐SegNet), a deep learning framework optimized for mobile robotic

This study proposes a deep line segment detection model named DLSD, for identifying four ubiquitous line segments on concrete pavements: joint, sealed joint, bridge expansion joint, and roadway boundary. DLSD associates a category with the triple‐point representation to encode a line segment. Its network employs a localization head and a classification head, attaching several auxiliary branches to