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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

To overcome the limitations of fragility analysis in the assessment of partition walls, specifically data shortage, general uncertainties, and subjective criteria, this study proposes a probabilistic method to evaluate seismic damage of partition walls. A proposed multi‐spring numerical model balances the damage representation and computational efficiency in simulations, thus avoiding extensive experimental

Climbing robots present transformative potential for automated structural inspections, yet their deployment remains limited by the reliance on manual control due to the absence of effective environment perception and path‐planning solutions. The critical bottleneck lies in the difficulty of generating accurate planning maps solely through onboard sensors due to the challenge of capturing open, large‐scale

This paper proposes a method for bridge damage identification using a small amount of damage labeling data. This method first trains a deep neural network (DNN) with undamaged bridge inclination responses as inputs and bridge equivalent loads as labels. The ratio curve related to the bridge damage state can be obtained by quantifying the change in the DNN prediction error before and after bridge damage

Strain is one of the key indicators for structural health monitoring. In this study, we developed a low‐cost microscopic vision‐based real‐time strain sensor using Raspberry Pi (called MISS‐Dym). By strategies for image processing accelerated and the specific running logic, the strain can be outputted at a frequency of more than 30 Hz in real time. The MISS‐Dym integrates multiple functions including

High‐frequency detection of track defects is crucial for accurate track condition assessment and system safety. Onboard vibration data collection devices can significantly increase detection density without additional costs. However, defect assessment based on this is significantly challenging, including the spatial heterogeneity of track parameters, distribution mismatch between vibration data and

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Passenger flow control and bus bridging are used widely in the operations and management of urban rail transit to relieve the pressure of urban rail transit passenger flow, especially in peak periods. This paper presents an optimization method based on time‐varying running time in links. We first develop a mixed integer nonlinear programming model seeking to achieve the minimum total passenger travel

Coordinating a platoon of connected and automated vehicles significantly improves traffic efficiency and safety. Current platoon control methods prioritize consistency and convergence performance but overlook the inherent interdependence between the platoon and the the non‐connected leading vehicle. This oversight constrains the platoon's adaptability in car‐following scenarios, resulting in suboptimal

This paper proposes a novel origin–destination (OD) prediction (ODP) model, namely, knowledge‐enhanced hybrid spatial–temporal graph neural networks (KE‐H‐GNN). KE‐H‐GNN integrates a deep learning predictive model with traffic engineering domain knowledge and a multi‐linear regression (MLR) module for incorporating external factors. Leveraging insights from the gravity model, we propose two meaningful

In structural health monitoring, only the deflection of key sections of the bridge can be monitored; the spatial continuous deflection of the main girder cannot be identified. To solve this problem, a method for expanding sparse point deflection measurements to spatially continuous data via optical fiber sensors in long‐span suspension bridges is proposed. First, the distributed fiber‐optic sensors

With the rapid increase in the number of vehicles, accurately identifying vehicle loads is crucial for maintaining and operating transportation infrastructure systems. Existing load identification methods typically rely on collecting vehicle load data from weigh-in-motion (WIM) systems when vehicles pass over them. However, cumbersome installation, high costs, and regular maintenance are the main obstacles

Structural performance prediction for structures and their components is a critical issue for ensuring the safety of civil engineering structures. Thus, enhancing the reliability of the prediction models with better generalization capability and quantifying the uncertainties of their predictions is significant. However, existing mechanism‐driven and data‐driven prediction models for reliable engineering

Short-term prediction of track degradation facilitates flexible and efficient maintenance, thereby meeting the railway system's escalating demands for track safety and smoothness. However, the track condition evolution presents challenges to accurate prediction, with diverse influential factors resulting in heterogeneous degradation patterns across space and time. In a short-term context, time series

Drop brackets (DB) and swivel clevises (SC) are critical components of railway catenary systems, playing a key role in maintaining cantilever stability. The condition of these components significantly impacts the safe operation of the catenary, necessitating periodic inspections to detect defects. This task is typically performed by onboard cameras using computer vision. However, traditional image

Current three‐dimensional (3D) displacement measurement algorithms exhibit practical limitations, such as computational inefficiency, redundant point cloud data storage, reliance on preset targets, and restrictions to unidirectional measurements. This research aims to address computation efficiency and accuracy issues in binocular camera‐based 3D structural displacement measurement by proposing a lightweight

The quick‐view (QV) technique serves as a primary method for detecting defects within sewerage systems. However, the effectiveness of QV is impeded by the limited visual range of its hardware, resulting in suboptimal image quality for distant portions of the sewer network. Image super‐resolution is an effective way to improve image quality and has been applied in a variety of scenes. However, research

Advancements in automated 3D scene reconstruction are essential for accurately capturing and documenting the current state of buildings and infrastructure. Traditional 3D reconstruction relies on laser scanning to obtain as‐built conditions, but this process is often labor‐intensive and time‐consuming. This study introduces an optimization algorithm incorporating methods for viewpoint generation, occlusion

Multivariate engineering formulas are the foundation of various engineering standards worldwide for constructing complex systems. Traditional formula discovery methods suffer from low efficiency, the curse of dimensionality, and low physical interpretability. To address these limitations, this study proposes a knowledge‐based method for efficiently generating multivariate engineering formulas directly

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Pavement crack measurement (PCM) is essential for automated, precise road condition assessment. However, balancing speed and accuracy on edge artificial intelligence (AI) mobile devices remains challenging. This paper proposes a real‐time PCM framework for edge deployment, incorporating a lightweight distillation network and a surface feature measurement algorithm. Specifically, the proposed instance‐aware

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The partitioning of a heterogeneously loaded road network into homogeneous, compact subregions is a fundamental prerequisite for the implementation of network‐level traffic management and control based on the network macroscopic fundamental diagram. This study proposes a flexible road network partitioning framework that leverages the powerful feature extraction capabilities of self‐supervised graph

Precision segmentation of cracks is important in industrial non‐destructive testing, but the presence of shadows in the actual environment can interfere with the segmentation results of cracks. To solve this problem, this study proposes a two‐stage domain adaptation framework called GAN‐DANet for crack segmentation in shadowed environments. In the first stage, CrackGAN uses adversarial learning to

Visual inspection is crucial for the maintenance of built infrastructures, facilitating early detection and quantification of damage. Traditional manual methods, however, often require inspectors to access dangerous or inaccessible areas, posing significant safety risks and inefficiencies. In response to these challenges, this paper introduces a portable visual inspection device (VID) integrated with

Underwater structural inspection is essential for ensuring the safety and longevity of bridges. To improve the efficiency and accuracy of these inspections, this paper presents a method for measuring the morphology of bridge piers through refraction correction and multi‐camera calibration. Using an underwater visual inspection platform with appropriate lighting, the measurement equipment mitigates

Coastal communities are increasingly vulnerable to hurricanes, which cause billions of dollars in damage annually through wind, storm surge, and flooding. Mitigation efforts are essential to reduce these impacts but face significant challenges, including uncertainties in hazard prediction, damage estimation, and recovery costs. Resource constraints and the disproportionate burden borne by socioeconomically

Cracking impacts asphalt concrete durability primarily due to cohesive asphalt binder failures. The poker chip test has recently been introduced to better characterize the cracking potential of asphalt binders by fracturing a specimen in a realistic stress state to a thin binder film. However, broader adoption faces challenges due to high instrumentation costs for measuring load and displacement. This

This paper introduces a novel hybrid multi‐model thermo‐temporal physics‐informed neural network (TT‐PINN) framework for thermal loading prediction in composite bridge decks. Unlike the existing PINN applications in heat transfer that focus on simple geometries, this framework uniquely addresses multi‐material domains and realistic boundary conditions through a dual‐network architecture designed for

Road cracks pose a serious threat to the stability of road structures and traffic safety. Therefore, this paper proposes an optimized accurate road crack segmentation network called MBGBNet, which can solve the problems of complex background, tiny cracks, and irregular edges in road segmentation. First, multi‐scale domain feature aggregation is proposed to address the interference of complex background

Semantic segmentation struggles with detecting undefined road obstacles, critical for autonomous driving in urban environments. This study addresses the need for accurate unknown obstacle detection, inspired by drivers’ instinctual vigilance toward unexpected objects. It explores the impact of unexpected object position patterns on anomaly detection using human fixation scan‐paths and gaze density

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The identification of tire text codes (TTC) during the production and operational phases of tires can significantly improve safety and maintenance practices. Current methods for TTC identification face challenges related to stability, computational efficiency, and outdoor applicability. This paper introduces an automated TTC identification system founded on a robust framework that is both user‐friendly

The reconstruction of monitoring data reconstruction is an important step in the process of structural health monitoring. Monitoring data reconstruction involves generating values that are close to the true or expected values, and then using the generated values to replace the anomalous data or fill in the missing data. Deep learning models can be used to reconstruct dam monitoring data, but current

This study develops an integrated delivery assignment and route planning strategy for food banking operations, considering food supply and demand constraints, food item restrictions, and vehicle capacity constraints. A mixed‐integer linear model is formulated to maximize the total demand served and minimize the total travel cost imposed on delivery volunteers. An integrated solution algorithm is developed