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

Despite progress in automated bridge point cloud segmentation based on deep learning, challenges persist. For instance, the absence of a public point cloud dataset specifically designed for bridge instances, and the existing bridge point cloud datasets display a lack of diversity in bridge types and inconsistency in component labeling. These factors may hinder the further improvement of accuracy in

Structural health monitoring (SHM) aims to assess civil infrastructures' performance and ensure safety. Automated detection of in situ events of interest, such as earthquakes, from extensive continuous monitoring data, is important to ensure the timeliness of subsequent data analysis. To overcome the poor timeliness of manual identification and the inconsistency of sensors, this paper proposes an automated

Growth in urban population, travel, and motorization continue to cause an increased need for urban projects to expand road capacity. Unfortunately, these projects also cause travel delays, emissions, driver frustration, and other road user adversities. To alleviate these ills, road agencies often face two work zone design choices: close the road fully and re‐reroute traffic or implement partial closure

Computer vision strain sensors typically require the camera position to be fixed, limiting measurements to surface deformations of structures at pixel-level resolution. Also, sensors have a service term significantly shorter than the designed service term of the structures. This paper presents research on a high durable computer vision sensor, microimage strain sensing (MISS)-Silica, which utilizes

The cover image is based on the Article A multi-phase mechanical model of biochar–cement composites at the mesoscale by Muduo Li et al., https://doi.org/10.1111/mice.13307.

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This study proposes a multi‐perspective fusion model for operating speed prediction based on knowledge‐enhanced graph neural networks, named RoadGNN‐S. By utilizing message passing and multi‐head self‐attention mechanisms, RoadGNN‐S can effectively capture the coupling impacts of multi‐perspective alignment elements (i.e., two‐dimensional design, 2.5‐dimensional driving, and three‐dimensional spatial

Real‐time hybrid simulation (RTHS) divides structural systems into numerical and experimental substructures, providing a cost‐effective solution for analyzing structural systems, especially those that are large or complex. However, the actuation systems between these substructures inevitably introduce delays, affecting the stability and accuracy of RTHS. To address this issue, this study proposes an

Asbestos has been used extensively in several applications. Once it is known as a dangerous mineral, its usage has been prohibited and its identification and remediation play a very important role from the health safety point of view. Nowadays, deep learning techniques are used in many applications, especially for image analysis. They can be used to significantly reduce the time and cost of traditional

Decision‐making of project‐level road maintenance is the process of mapping road information into a maintenance plan. Even though benefitting from deep learning, the decision‐making still faces the problem of maintenance data uncertainty. The data uncertainty derives from imperfect road information collection and arbitrary selection of maintenance plans. Such uncertainty always leads to unreasonable

This study proposes a collaborative control framework under the mixed traffic environment of connected and automated vehicles and connected human‐driven vehicles, which can simultaneously optimize the signal timing, lane settings, and vehicle trajectories at isolated intersections. Initially, considering the dynamics of traffic demand and incompatible signals, we analyze the vehicle delay of each lane

A novel damage detection model, which utilizes the spatiotemporal characteristics of the acceleration data, is proposed to assess the structural integrity of railway bridges. For this, the measured acceleration data are decomposed into several intrinsic mode functions (IMFs) using the sparse random mode decomposition model. The generated IMFs are subsequently integrated into the enhanced time series

In damage‐level classification, deep learning. models are more likely to focus on regions unrelated to classification targets because of the complexities inherent in real data, such as the diversity of damages (e.g., crack, efflorescence, and corrosion). This causes performance degradation. To solve this problem, it is necessary to handle data complexity and uncertainty. This study proposes a multimodal

Structural response estimation based on deep learning can suffer from reduced estimation performance owing to discrepancies between the training and test data as the noise level in the test data increases. This study proposes a short‐time Fourier transform‐based long short‐term memory (STFT‐LSTM) model to improve estimation performance in the presence of noise and ensure estimation robustness. This

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In railway transportation, the evaluation of track irregularities is an indispensable requirement to ensure the safety and comfort of railway vehicles. A promising approach is to directly use vehicle dynamic responses to assess the impact of track irregularities. However, the computational cost of obtaining the dynamic response of the vehicle body using dynamics simulation methods is large. To this

To address the inaccurate classification of coarse aggregate particle size due to insufficient height information in single‐view, a multi‐view and graph convolutional network (GCN) based method for coarse aggregate particle size classification was proposed in this study. First, the viewpoint selection and projection strategies were designed to build the aggregate multi‐view datasets. Then, the surface

Predicting the performance of composite materials is crucial for their application in civil infrastructure, yet limited experimental data often hinder the development of accurate and generalizable models. This study introduces a deep neural network (DNN) approach that combines summarizing statistics (SS) and transfer learning (TL)—termed the SSTL‐DNN approach—to address data scarcity in modeling composite

This paper presents a distributed adaptive signal control and advisory speed coordination method based on shockwave theory, which accommodates diverse traffic conditions. In order to assess signal control efficiency under various scenarios, an innovative evaluation index termed synthetic delay is introduced based on the analysis of traffic dynamics at intersections. Considering the formation and dissipation

In vibration testing of civil engineering structures, the first two vibration modes are crucial in representing the global dynamic behavior of the structure measured. In the present study, a comprehensive method is proposed to identify the first two vibration modes of wind turbine towers, which is based on the analysis of fractional order statistical moments (FSM). This study offers novel contributions

This study introduces an asynchronous decentralized coordinated signal control (ADCSC) framework for multi‐agent traffic signal control in the urban road network. The controller at each intersection in the network optimizes its signal control decisions based on a prediction of the future traffic demand as an independent agent. The asynchronous framework decouples the entangled interdependence between

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Mobile sensing has emerged as an economically viable alternative to spatially dense stationary sensor networks, leveraging crowdsourced data from today's widespread population of smartphones. Recently, field experiments have demonstrated that using asynchronous crowdsourced mobile sensing data, bridge modal frequencies, and absolute mode shapes (the absolute value of mode shapes, i.e., mode shapes

Bolt loosening assessment is crucial to identify early warnings of structural degradation and prevent catastrophic events. This paper proposes an automatic bolt loosening assessment methodology. First, a novel end‐to‐end ensemble vision model, Bolt‐FP‐Net, is proposed to reason the locations of bolts and their hexagonal feature patterns concurrently. Second, an adaptive target‐free perspective correction

As conventional machine learning models often struggle with scarcity and structural variation of training data, this paper proposes a novel regression transfer learning framework called transferable tabular regressor (TransTabRegressor) to address this challenge. The TransTabRegressor integrates natural language processing (NLP) for feature encoding, transformer for enhanced feature representation

Surrogate‐based traffic signal optimization (TSO) is a computationally efficient alternative to simulation‐based TSO. By replacing the simulation‐based objective function, a surrogate model can quickly identify solutions by searching for extreme points on its response surface. As a popular surrogate model, the ensemble of multiple diverse deep learning models can approximate complicated systems with

Recent advancements in construction robotics have significantly transformed the construction industry by delivering safer and more efficient solutions for handling complex and hazardous tasks. Despite these innovations, ensuring safe robotic navigation in intricate indoor construction environments, such as attics, remains a significant challenge. This study introduces a robust 3‐dimensional (3D) robotic

Offshore earthquakes and subsequent tsunamis pose significant risks to many coastal populations worldwide. This paper introduces a data‐driven machine learning model that synthesizes accurate 3D earthquake–tsunami fragility surfaces from randomly selected 2D fragility curves. The integration of physics‐based simulations enhances the model's reliability for these specific hazards, making it a valuable

Ensuring the safety of water networks is a research hotspot in the current water conservancy industry, and dams are an important part. However, over time, the dam is prone to varying degrees of aging and disease, most of which are structural cracks. If they cannot be discovered and repaired in time, the normal operation of the dam will be affected, and even catastrophic accidents such as dam failure

This paper introduces a deterministic algorithm to solve the discrete network design problem (DNDP) efficiently. This non‐convex bilevel optimization problem is well‐known as an non deterministic polynomial (NP)‐hard problem in strategic transportation planning. The proposed algorithm optimizes budget allocation for large‐scale network improvements deterministically and with computational efficiency

This paper introduces a method for forecasting the arrival of trains by analyzing track vibration signals. The proposed algorithms, based on H‐ranks of track vibration signals, can generate early alerts for approaching trains. These algorithms are robust to additive noise and environmental conditions. The theoretical foundation of the method involves the application of matrix operations to detect significant

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Segmentation of structural components in infrastructure inspection images is crucial for automated and accurate condition assessment. While deep neural networks hold great potential for this task, existing methods typically require fully annotated ground truth masks, which are time‐consuming and labor‐intensive to create. This paper introduces Scribble‐supervised Structural Component Segmentation Network

Aftershocks (ASs) following strong mainshocks (MSs) can exacerbate structural damage or lead to collapse. However, the scarcity of recorded data necessitates reliance on artificial sequences, which have difficulty in characterizing the time‐frequency correlation between MSs and ASs. This study innovatively converts the AS time history prediction into an image translation task, exploiting the invertible

Intelligent compaction, particularly fully autonomous compaction, has emerged as a widely accepted innovative technology for enhancing compaction quality and efficiency. When multiple rollers are concurrently engaged in compaction within the same region, the trajectory planning for each roller and cooperative control become pivotal factors in ensuring efficient and safe compaction. This paper presents

Characterization of mechanical properties of shale constituent minerals (viz., the mechanical genes of shale) has been challenging but of great significance for engineering applications in shale formations. In this study, a phase‐identified nanoindentation is proposed to decode the mechanical genes of shale using a large nanomechanical dataset. With the consideration of uniform prior probability density

Vehicle‐mounted detection methods have been widely applied in the maintenance of high‐speed railways (HSRs), providing feasibility for diagnosing ballastless track arching. However, applying detection data faces several key limitations: (1) The threshold mostly requires manual setting, making recognition accuracy highly subjective; (2) the extensive workload of manual inspections makes it challenging

In the fields of engineering seismology and earthquake engineering, researchers have predominantly focused on ground motion models (GMMs) for intensity measures. However, there has been limited research on power spectral density GMMs (PSD‐GMMs) that characterize spectral characteristics. PSD, being structure‐independent, offers unique advantages. This study aims to construct PSD‐GMMs using non‐parametric

Rapid, accurate, and fully automated estimation of both length and width of asphalt pavement cracks, essential for achieving a proactive asset management, presents a significant challenge, primarily due to limitations in the effectiveness of automatic image segmentation and the accuracy of crack width and length estimation algorithms. To address this challenge, this paper introduces the Branch Growing

Zhou A, Peeta S, Wang J. Cooperative control of a platoon of connected autonomous vehicles and unconnected human-driven vehicles. Computer-Aided Civil and Infrastructure Engineering. 2023;38(18): 2513–2536. In the “Funding Information” section, the text “National Key Research and Development Program of China, Grant/Award Number: 2018YFE0102700.” was incorrect. This should have read: “National Key Research