Bridge wear and deterioration occur over time under typical operations. Inspections can be resource intensive, infrequent, and sometimes require bridge closure. Instrumented passing vehicles may be used to record vibration responses and extract damage-sensitive bridge frequency response characteristics. This paper presents a methodology for extracting bridge frequencies from crowdsourced dynamic response

Recent advances in robotics have enabled robots to collaborate with workers in shared, fenceless workplaces in construction and civil engineering, which can improve productivity and address labor shortages. However, this collaboration may lead to collisions between workers and robots. Targeting safe collaboration, this study proposes an intention-aware motion planning method for robots to avoid collisions

With the introduction of connected and automated vehicles (CAVs), the integrated control of traffic signals, lane assignments, and vehicle trajectories becomes feasible, offering notable benefits for enhancing intersection operations. However, during the prolonged transition to an entirely CAV environment, how to fully leverage the advantage of CAVs while considering the characteristics of human-driven

Timely and accurate extraction of pavement crack information is crucial to maintain service conditions and structural safety for infrastructures and reduce further road maintenance costs. Currently, deep learning techniques for automated pavement crack detection are far superior to traditional manual approaches in both speed and accuracy. However, existing deep learning models may easily lose crack

Railway alignment optimization in earthquake-prone mountainous (EPM) regions should quantify and trade off construction investments and seismic risks. Unfortunately, slight attention has been previously devoted to this trade-off. To this end, based on the FEMA-P58 methodology, a net present value (NPV) model of risk avoidance is presented and solved. In the model, alignment alternatives are first segmented

Automatic inspection of concrete surface defects based on visual elements is crucial for the timely detection of security risks in infrastructure. Moreover, accurate determination of the geographical location of the detected defects is critical for subsequent maintenance and reinforcement tasks. This study employed convolutional neural network (CNN) training methods for detection and localization.

This paper presents a novel Bayesian method for backcalculation of pavement dynamic modulus, stiffness, thickness, and damping using falling weight deflectometer (FWD) data. The backcalculation procedure yields estimates and uncertainties for each pavement property of interest. As a by-product of the Bayesian procedure, information about measurement error is recovered. The Bayesian method is tested

This study aims to classify seafloor sediments using physics-inspired and data-driven soil models combined with machine learning algorithms and oversampling techniques. The field data used for the input variables include porosity, S- and P-wave velocities and depth. The soil information reported in the original literature and the “six reference sediments” and effective stress-versus-depth models proposed

This paper combines a hyper-reduction procedure, the proper orthogonal decomposition unassembled discrete empirical interpolation method, with a non-iterative α-operator splitting (α-OS) time-integration scheme for accelerating parametric analyses on damageable civil engineering structures subjected to earthquakes. Applications on a two-story reinforced concrete frame building modeled by multi-fiber

Bridge inspection ensures that in-service bridges are managed and maintained in conformity. To enhance the accuracy and efficiency of bridge inspection, an automatic hierarchical model is proposed, which enables the classification and correlation of bridge surface images at three levels, namely, at the structure, component, and defect type level. Thus, the impact of both the defect types and the affected

Urban infrastructure plays a crucial role in determining the quality of life for citizens. However, given the increasing number of aging infrastructures, regular inspections are essential to prevent accidents. Deep learning studies have been conducted to detect structural damage and ensure high accuracy and reliability of these inspections. However, these detection algorithms often face challenges

Sketch-based architectural design tasks require considerable time and effort. However, developing an artificial intelligence agent to color and fuse architectural sketches is challenging due to the imaginative nature of the task and technical limitations. Here, we first introduce Y-shaped generative adversarial networks (GANs), which can color sketches on the basis of temporarily specified reference

Dynamic rerouting has been touted as a solution for urban traffic congestion. However, its implementation is stymied by the complexity of urban traffic. To address this, recent studies suggest the efficacy of novel technologies like fog computing and deep reinforcement learning. However, there exist significant challenges in this regard: (1) sorting massive amounts of data associated with large urban

With the rapid development of deep learning and machine automation technology, as well as workforce aging, increasing labor costs, and other issues, an increasing number of scholars have paid attention to the use of these techniques to solve problems in civil engineering. Although progress has been made in applying deep learning to damage detection, many subfields in civil engineering are still in

Data on infrastructure project costs are often unstructured and lack consistency. To enable costs to be compared within and between organizations, large amounts of data must be classified to a common standard, typically a manual process. This is time-consuming, error-prone, inconsistent, and subjective, as it is based on human judgment. This paper describes a novel approach for automating the process

High-resolution (HR) crack images offer more detailed information for assessing structural conditions compared to low-resolution (LR) images. This wealth of detail proves indispensable in bolstering the safety of unmanned aerial vehicle (UAV)-based inspection procedures and elevating the precision of small crack segmentation. Nonetheless, achieving a balance between segmentation accuracy and GPU memory

The segmentation accuracy of bridge crack images is influenced by high-frequency light, complex scenes, and tiny cracks. Therefore, an integration–competition network (complex crack segmentation network [CCSNet]) is proposed to address these problems. First, a grayscale-oriented adjustment algorithm is proposed to solve the high-frequency light problem. Second, an integration–competition mechanism

Despite notable progress made in predicting the drift capacity of reinforced columns with steel bars, these techniques and methods are proven inapplicable for accurately predicting the drift capacity of RC columns reinforced with shape memory alloy (SMA) bars. This study employed machine learning (ML) to predict and design the drift limit state of concrete columns using SMA bars. To this end, a total

The use of automated equipment for surface crack detection based on digital image acquisition is becoming increasingly popular in the inspection industry. While researchers typically focus on improving the accuracy of recognition methods, the image quality is essential to the effectiveness of the algorithm. However, evaluating the quality of crack images has received little attention in computer-aided

During the regular service life of high-speed railway (HSR), there might be serious defects in the concrete slabs of the infrastructure systems, which may further significantly affect public transportation safety. To address these serious issues and fulfill the regular functions of HSR, the traditional methods for railway engineers involve carrying out regular on-site inspections manually or by semi-automatic

Visual tracking of road cracks in unstructured road environment was, is, and remains a crucial and challenging task, which plays a vital role in accurate crack sealing for automated road cracks repair. However, many problems have not been well solved in existing automated road cracks repair, such as the low automation due to partial dependence on manual and the interrupted traffic flow caused by the

Cracks are the most common damage type on the pavement surface. Usually, pavement cracks, especially small cracks, are difficult to be accurately identified due to background interference. Accurate and fast automatic road crack detection play a vital role in assessing pavement conditions. Thus, this paper proposes an efficient lightweight encoder–decoder network for automatically detecting pavement

The optimization of urban traffic efficiency and reduction of pollution through minimizing the number of taxis has become a topic of increasing interest. However, the problem of determining the minimum fleet that considers both time and distance efficiency has received limited attention. Furthermore, little research has been done on how this problem is influenced by factors such as city size and travel

Infusing deep learning with structural engineering has received widespread attention for both forward problems (structural simulation) and inverse problems (structural health monitoring). Based on Fourier neural operator, this study proposes VINO (Vehicle–Bridge Interaction Neural Operator) to serve as a surrogate model of bridge structures. VINO learns mappings between structural response fields and

Vertical alignment reconstruction obtains alignment parameters by fitting geometric components to a set of measured points representing the profile of an existing road or railroad, which is essential in alignment consistency analysis and maintenance to ensure safety and comfort. The neural dynamics model of Adeli and Park is explored and improved for reconstructing vertical alignments with constraints

This paper proposes a new deep-learning framework for drive-by bridge condition monitoring. The proposed approach represents a bridge monitoring regime that enables the presence, type, location, and severity of bridge damage to be identified purely from measurements taken on a passing vehicle, without needing any pre-measured training data. The computational framework adopts a numerical vehicle–bridge

A precise parallel laser line scanning system has been developed to assess the depth of out-of-plane structural defects on concrete surfaces. This system comprises a digital camera, dual line laser diodes, and positioning rigid arms that create a triangulation-based setup. Laser lines are distorted when projected onto an out-of-plane defect. A new image processing algorithm has been devised to extract

A quantitative understanding of dynamic lane-changing interaction patterns is indispensable for improving the decision-making of autonomous vehicles (AVs), especially in mixed traffic with human-driven vehicles. This paper develops a novel framework combining the hidden Markov model (HMM) and graph structure to identify the difference in dynamic interaction patterns between mandatory lane changes (MLC)

To address the challenges of low recognition accuracy, low robustness, and low detection efficiency in existing tunnel face joint and fissure recognition methods, we present a deep learning recognition segmentation algorithm called the mask region convolutional neural network (Mask R-CNN) that is enhanced by an advanced Transformer attention mechanism and deformable convolution network (Mask R-CNN-TD)

Current automated structural topology design methods can only deal with limited design spaces or simplified architectural layouts for lack of data or a proper representation of structure topology. To address this, the abundant information of manually designed architectural and structural layouts should be exploited to guide the topology design. To achieve automatic generation of structural topologies

The construction material quantity (CMQ) is widely concerned in the structural design of reinforced concrete buildings and is often included among the objective functions of computer-aided optimization design techniques. To minimize construction cost and carbon emissions, an accurate and efficient CMQ estimation method is timely required. In this study, a novel graph neural network (GNN) is proposed

Creating a high-fidelity railroad station model to match the physical details of hundreds of tracks and switches is never a trivial task. The manual modeling approach often costs engineers significant efforts and constrains the generality and extensivity of many advanced methods. Taking advantage that many stations are drawn proportionally into two-dimensional drawing exchange format (DXF) files via

Numerous experimental studies have shown the type and gradation of coarse aggregates effect on the mechanical properties of concrete. The type and gradation of coarse aggregates have not been taken into account in the available machine learning prediction models. In this study, a two-dimensional concrete microscopic image was generated by using a random aggregate model (RAM), and the coarse aggregate

1 INTRODUCTION The authors appreciate the discusser's comments on our manuscript. We believe that careful feedback enhances the impact of our paper (Pak et al, 2023) to researchers and practitioners in the structural engineering domain. The issues raised by the discusser pertain to the scope and significance, black boxes in machine learning, and engineering applicability. This document delivers our

A novel seismic robustness computational methodology is proposed for community building portfolio (CBP) coupled with lifeline networks based on probability-cloud model. This method consists of three interrelated submodels, respectively, the building fragility analysis model of the main subsystem CPB, the water supply network (WSN) hydraulic simulation analysis model of the secondary subsystem WSN,

Rail surface defects are potential danger factors for railway systems, and visual inspection of surface defects plays a vital role in rail maintenance. Recently, the methods based on deep learning have been widely used in rail inspection systems, but such systems often face the problem of a lack of defect samples for training deep learning models at start-up, which is called the cold-start problem

This paper explores the application of visual question answering (VQA) in bridge inspection using recent advancements in multimodal artificial intelligence (AI) systems. VQA involves an AI model providing natural language answers to questions about the content of an input image. However, applying VQA to bridge inspection poses challenges due to the high cost of creating training data that requires

When an impact load is sufficiently small, its influence on the pavement structure is mainly from the surface layer material. To explore the influence depth of an impact load and back-calculation of the pavement surface modulus, both numerical calculation and experimental testing were conducted, and the results are presented in this paper. The numerical calculation was performed through a DEM-FDM-coupled

This study presents a novel, deep-learning-based model for the automated reconstruction of a cross-sectional drawing from stereo photographs. Targeted cross-sections captured in stereo photographs are detected and translated into sectional drawings using faster region-based convolutional neural networks and Pix2Pix generative adversarial network. To address the challenge of perspective correction in

The traditional visual inspection technique for damage assessment of buildings immediately after an earthquake can be time-consuming, labor-intensive, and risky. Numerous studies have been carried out using deep learning techniques, particularly convolutional neural network (CNN), to evaluate the damage to building structures after an earthquake using buildings’ damage images. Quantum computing, on

This study is the first in the literature to examine the Braess paradox considering parking behavior in the autonomous vehicle (AV) environment, based on which the network design problem for the autonomous transportation system (NDP-ATS) is modeled. First, we introduce the AV commuting pattern considering the self-driving process for the parking purpose. We then illustrate the existence of two distinct

Detecting prestressed wire breakage in concrete bridges is essential for ensuring safety and longevity and preventing catastrophic failures. This study proposes a novel approach for wire breakage detection using Mel-frequency cepstral coefficients (MFCCs) and back-propagation neural network (BPNN). Experimental data from two bridges in Italy were acquired to train and test the models. To overcome the

Machine vision technologies have the potential to revolutionize hazard inspection, but training machine learning models requires large labeled datasets and is susceptible to biases. The lack of robust perception capabilities in machine vision systems for construction hazard inspection poses significant safety concerns. To address this, we propose a novel method that leverages human knowledge extracted

The train operations of large stations are critical in determining the efficiency of the railway network. Large high-speed railway stations often have more than two adjacent stations running in multiple directions and must address highly complex train operation patterns. To develop a more efficient operation plan than the existing route-based representations for modeling train conflicts, a more systematic

River ice breakups carry the potential for high flows and flooding and are of great interest to accurately predict. A challenge in forecasting these events is the management of the massive amounts of data associated with an ice season. This study couples ontological and machine learning models in a new hybrid modeling framework to predict spring breakup on a national scale. The Ice Season Ontology

Point cloud can provide rich 3-D geometric information of structures, and it has been widely investigated for generating digital functional models, such as finite element (FE) model and building information model (BIM). However, for the existing digital models, how to maintain and update the new local geometric changes on the existing digital models has not been sufficiently studied. Therefore, this

Mountain railway alignment optimization has always been a challenge for designers and researchers in this field. It is extremely difficult for existing methods that optimize alignments before major structures to generate a better alignment than the best one provided by human designers when the terrain is drastically undulating between the start and endpoints. To fill this gap, a “structures before

To honor Professor Hojjat Adeli's dedicated service and leadership as Editor-in-Chief of Computer-Aided Civil and Infrastructure Engineering for decades and contributions as a distinguished researcher, prolific scholar, and illustrious contributor to a large number of journals, and in commemoration of the journal's Silver Anniversary, Wiley-Blackwell established the Hojjat Adeli Award for Innovation

Road cracks are a major concern for administrators. Visual inspection is labor-intensive. The accuracy of previous algorithms for detecting cracks in images requires improvement. Further, the length and thickness of cracks must be estimated. Light detection and ranging (Lidar), a standard smartphone feature is used to develop a method for the completely automatic, accurate, and quantitative evaluation

Ground-penetrating radar (GPR) is widely used to determine the location of buried pipes without excavation, and machine learning has been researched to automatically identify the location of buried pipes from the reflected wave images obtained by GPR. In object detection using machine learning, the accuracy of detection is affected by the quantity and quality of training data, so it is important to

Using deep learning (DL) to recognize building and infrastructure damage via images is becoming popular in vision-based structural health monitoring (SHM). However, many previous studies solely work on the existence of damage in the images and directly treat the problem as a single-attribute classification or separately focus on finding the location or area of the damage as a localization or segmentation