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

Salient object detection (SOD) is a crucial preprocessing technique in visual computing, which identifies the salient regions in an image by simulating the human visual perception system. It achieves remarkable results in tasks such as image quality assessment, editing, and object recognition. However, due to the particularity of pavement crack detection in terms of scale and feature requirements,

Corrosion in steel transmission towers poses a challenge to structural integrity and safety, requiring efficient detection methods. Traditional visual inspections are unsustainable due to the complexity and volume of structures. Their manual, qualitative, and subjective nature often leads to inconsistencies in maintenance planning. This study proposes a deep learning‐based approach for semantic segmentation

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With the advancement of urbanization, cities are constructing expressways to meet complex travel demands. However, traditional link‐based road network design methods face challenges in addressing large‐scale expressway network design problems. This study proposes an expressway network design method tailored for multi‐subregion road networks. The method employs the macroscopic fundamental diagram to

With the shortage of skilled labors, there is an increasing demand for automation in the construction industry. This study presents an autonomous construction framework for crane control with enhanced soft actor–critic (SAC‐E) algorithm and real‐time progress monitoring. SAC‐E is a novel reinforcement learning algorithm with superior learning speed and training stability for lifting path planning.

Computer vision‐based vehicle load monitoring methods could obtain spatiotemporal data of vehicle loads, which is important for bridge monitoring and operation. However, during the process of vehicle detection and tracking, current research usually focuses on the vehicle as a whole, and there is a lack of research on the accurate positioning of vehicle wheel loads. For the fatigue analysis of orthotropic

This paper addresses the challenge of real‐time, continuous trajectory planning for autonomous excavation. A hybrid method combining particle swarm optimization (PSO) and reinforcement learning (RL) is proposed. First, three types of excavation trajectories are defined for different geometric shapes of the digging area. Then, an excavation trajectory optimization method based on the PSO algorithm is

Gaining insights into pedestrian flow patterns in public spaces can greatly benefit decision‐making processes related to infrastructure planning. Interestingly, even pedestrians are unfamiliar with one another, they often follow others, drawing on positive information and engaging in a spontaneous collective behavior of pedestrians. To model this collective behavior, this paper proposed a social force‐based

Precise three‐dimensional (3D) instance segmentation of indoor scenes plays a critical role in civil engineering, including reverse engineering, size detection, and advanced structural analysis. However, existing methods often fall short in accurately segmenting complex indoor environments due to challenges of diverse material textures, irregular object shapes, and inadequate datasets. To address these

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Cracks impact the performance and durability of asphalt pavements, necessitating a comprehensive understanding of the mixture cracking behavior. While discrete element modeling has been implemented, many studies oversimplify the simulation of asphalt mortar, a critical component affecting mixture cracking resistance. This study proposes a mortar model that is applicable to both two‐dimensional (2D)

Particle morphology influences the mechanical behavior of granular soils. Generating particles with realistic shapes for discrete element method simulations is gaining popularity. However, it is still challenging to efficiently generate very angular particles with less computational cost. Addressing this challenge, this paper introduces a novel noise‐based framework for generating realistic soil particle

Quantifying tiny cracks is crucial for assessing structural conditions. Traditional non‐contact measurement technologies often struggle to accurately measure tiny crack widths, especially in hard‐to‐access areas. To address these challenges, this study introduces an image‐based, handheld parallel laser line‐camera (PLLC) system designed for automated tiny crack localization and width measurement from

Suspender cables/hangers occupy a crucial role during the whole service life of suspension bridges/arch bridges/space structures, and their long‐term repeated vibration under corrosion and high‐stress service state will cause fatigue damage and even induce fatigue failure. To mitigate the vibration of the vertical suspender cables/hangers, a four‐wire pendulum tuned mass damper (FWPTMD) is proposed

Particle morphology is a crucial factor influencing the mechanical properties of granular materials particularly in infrastructure construction processes where accurate shape descriptors are essential. Accurately measuring three‐dimensional (3D) morphology has significant theoretical and practical value for exploring the multiscale mechanical properties of civil engineering materials. This study proposes

Focusing on learning‐based semantic segmentation (SS) methods for bridge point cloud data (PCD), this study proposes a structure‐oriented concept (SOC) with training focused on the spatial distribution patterns of bridge components, including both the horizontally absolute location of each component and its vertically relative position compared with other components. Then a structure‐oriented loss

In sewer pipe closed‐circuit television inspection, accurate temporal defect localization is essential for effective pipe assessment. Industry standards typically do not require time interval annotations, which are more informative but lead to additional costs for fully supervised methods. Additionally, differences in scene types and camera motion patterns between pipe inspections and temporal action

Three‐dimensional (3D) buried object detection using ground penetrating radar (GPR) benefits from the powerful capacity of image‐wise deep neural networks. However, it still faces the challenge of information loss from raw GPR signals to two‐ and three‐dimensional images, such as the frequency‐domain information loss when normalizing GPR signals into gray‐scale images and spatial information loss when

The longevity of porous asphalt pavement is inevitably compromised by the clogging of voids by various particles, leading to a degradation in its drainage function. Numerical simulations with real pore structures were used to investigate the clogging behavior of porous asphalt concrete (PAC) to clearly and intuitively understand its void clogging process. In this study, a three‐dimensional model of

Corrosion is one of the main damages in steel bridges, which appears as a loss of material and sectional area and causes member failure over time. A reliable bridge management system not only should help in preventing catastrophic structural failure by employing an in‐time anomaly detection approach for all the bridges within a network but also should reduce overall network costs commonly raised by

The deployment of sensors to monitor traffic flow between origin–destination (OD) pairs, within a specified budget, remains a critical concern for both academic researchers and transportation managers. While these technologies are essential for capturing traffic data, the aspect of privacy has often been overlooked. To bridge this gap, this paper introduced the concept of privacy distance and then

Unlike most urban rail transit (URT) resilience studies on URT lines or networks under major disturbances, this paper focuses on the resilience assessment of URT stations under high‐frequency daily disturbances with minor impacts. A resilience assessment metric with different resilience levels is proposed, which is calculated based on multiple criteria, including the number of delayed passengers, degree

This study primarily focuses on generating mobility flow in regions and cities, which plays an important role in urban planning and management. The majority of existing mobility flow models, including conventional statistical models and deep learning‐based models, are heavily dependent on historical data to predict future mobility flows. The application of these models poses significant challenges

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Accurately estimating traffic volumes in construction work zones is crucial for effective traffic management. However, one of the key challenges transportation agencies face is the limited coverage of continuous count station (CCS) sensors, which are often sparsely located and may not be positioned directly on roads where construction work zones are present. This spatial limitation leads to gaps in

For existing bridge weigh‐in‐motion technologies, the main challenge in accurate weight estimation is to overcome the difficulty of identifying the closely spaced axles. To do so, many field test data are generally required for each bridge in application. To address such a challenge, a novel two‐level sequential artificial neural network (ANN) model trained by the hybrid simulated‐experimental data

Suspended ceiling systems constitute a pivotal non‐structural component in buildings, and the warping of panels not only compromises the seismic performance but also affects the functional integrity. This paper proposes a novel multi‐stage warped panel detection (MWPD) method to automatically locate warped panels from two‐dimensional images and quantify their deformation. First, the Deep Hough Transform

This study developed Geometry Physics neural Operator (GPO), a novel solver framework to approximate the partial differential equation (PDE) solutions for solid mechanics problems with irregular geometry and achieved a significant speedup in simulation time compared to numerical solvers. GPO leverages a weak form of PDEs based on the principle of least work, incorporates geometry information, and imposes

Accurately checking the position and presence of internal components before casting prefabricated elements is critical to ensuring product quality. However, traditional manual visual inspection is often inefficient and inaccurate. While deep learning has been widely applied to quality inspection of prefabricated components, most studies focus on surface defects and cracks, with less emphasis on the

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Over the past decade, pavement imaging systems, particularly 3D laser technology, have been widely adopted by transportation agencies for network‐level pavement condition evaluations. State Highway Agencies, including Georgia Department of Transportation (DOT), Florida DOT, and Texas DOT, have been collecting pavement images for over 5 years. However, these multi‐year pavement images have not been

The steel girder of high‐speed railway bridges requires regular inspections to ensure bridge stability and provide a safe environment for railway operations. Unmanned aerial vehicle (UAV)‐based inspection has great potential to become an efficient solution by offering superior aerial perspectives and mitigating safety concerns. Unfortunately, classic convolutional neural network (CNN) models suffer

Deterioration of the concrete deck surface, including disintegration and delamination between the deck slab and pavement, presents significant challenges in bridge maintenance due to its hidden nature and the risk it poses to the deck's durability as damage progresses. Early detection is critical for preventing issues such as pothole formation and ensuring long‐term durability. However, traditional

This study presents an automated, quantitative classification method for near‐fault pulse‐like ground motions, distinguishing between forward‐directivity and fling‐step (FS) motions. The method introduces two novel parameters—the pulse velocity ratio and pulse area ratio—which transform the classification standard from a qualitative to a quantitative framework. Combined with an enhanced pulse extraction

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Achieving fully unmanned operations in large‐scale excavating machinery relies on robust autonomous driving capabilities. Electric shovels, with their steering limitations and reversing difficulties, present unique challenges, compared to lighter, high‐speed‐tracked vehicles. This paper explores these operational and technical challenges and introduces a trajectory planning scheme combining the Guidance‐Hybrid

Accurately predicting dam deformation is crucial for understanding its operational status. However, existing models struggle to effectively capture the spatiotemporal correlations in monitoring data and quantify uncertainty within dam systems. This paper presents an innovative uncertainty quantification model for evaluating regional deformation in arch dams. First, a method to extract the spatiotemporal

Underwater defects in piers pose potential hazards to the safety and durability of river‐crossing bridges. The concealment and difficulty in detecting underwater defects often result in their oversight. Acoustic methods face challenges in directly achieving accurate measurements of underwater defects, while optical methods are time‐consuming. This study proposes a two‐step rapid inspection method for

In structural design, accurately extracting information from floor plan drawings of buildings is essential for building 3D models and facilitating design automation. However, deep learning models often face challenges due to their dependence on large labeled datasets, which are labor and time‐intensive to generate. And floor plan drawings often present challenges, such as overlapping elements and similar

Construction robots are challenging the paradigm of labor‐intensive construction tasks. Imitation learning (IL) offers a promising approach, enabling robots to mimic expert actions. However, obtaining high‐quality expert demonstrations is a major bottleneck in this process as teleoperated robot motions may not align with optimal kinematic behavior. In this paper, two innovations have been proposed

In the realm of using acoustic methods for locating leakages in water supply pipelines, existing research predominantly focuses on single leak localization, with limited exploration into the challenges posed by multiple leak scenarios. To address this gap, a genetic algorithm‐optimized frequency‐domain convolutional blind source separation algorithm is proposed for the precise localization of multiple

The pavement skid resistance is crucial for ensuring driving safety. However, the reproducibility and comparability of field measurements are constrained by various influencing factors. One solution to these constraints is utilizing laser‐based 3D pavement data, which are notably stable and can be employed to estimate pavement skid resistance indirectly. However, the integration of tire–road friction

Rock quality designation (RQD) plays a crucial role in the design and analysis of rock engineering. The traditional method of measuring RQD relies on manual logging by geologists, which is often labor‐intensive and time‐consuming. Thus, this study presents an autonomous framework for expeditious RQD estimation based on two‐dimensional corebox photographs. The scale‐invariant feature transform (SIFT)

An event‐based supervisory control scheme, in the Ramdage–Wonham framework, will be proposed for the cyber‐physical Waterway Lock system, known as Lock III, in Tilburg, the Netherlands. The proposed control scheme imposes desired behavior, by appropriately disabling controllable events, so as to avoid activation of actuator commands that may lead to undesired and potentially hazardous operating states

Accurate soil stratification is essential for geotechnical engineering design. Owing to its effectiveness and efficiency, the cone penetration test (CPT) has been widely applied for subsurface stratigraphy, which relies heavily on empiricism for correlations to soil type. Recently, deep learning techniques have shown great promise in learning the relationship between CPT data and soil boundaries automatically

The structural response of reinforced concrete dapped‐end beams is simulated through finite element analysis. The case study consists in experimental tests performed in the framework of an Italian research project on bridges. The study assesses both the local and global behavior of the beam and characterizes the damage patterns. A blind prediction is initially performed inputting the main basic material

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A novel coupled mechanical and mass transport lattice discrete particle model is developed to quantitatively assess the impact of cracks on the mass transport properties in concrete members subjected to short‐ and long‐term loading conditions. In the developed approach, two sets of dual lattice networks are generated: one to resolve the mechanical response and another for mass transport analysis. The

Aero‐structural shape design and optimization of bridge decks rely on accurately estimating their self‐excited aeroelastic forces within the design domain. The inherent nonlinear features of bluff body aerodynamics and the high cost of wind tunnel tests and computational fluid dynamics (CFD) simulations make their emulation as a function of deck shape and reduced velocity challenging. State‐of‐the‐art

Long-span suspension bridges are significantly susceptible to windstorm-induced vibrations, leading to critical challenges of field measurements along with multicollinearity and nonlinearity between wind features and bridge dynamic responses. To address these issues, this article proposes an innovative machine learning-assisted predictive method by integrating a predictor selector developed from regularized