Article
Multidisciplinary Sciences
Wenjun Wang, Chao Su
Summary: Early and timely defect detection is crucial for maintaining the stability of concrete bridges. A novel hybrid network, combining deep learning and Transformer module, is proposed for defect classification, achieving superior performance compared to other methods.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2022)
Article
Engineering, Civil
Mohammad Salehi, Petros Sideris, Reginald DesRoches
Summary: This paper proposes an innovative fiber-based finite element modeling strategy for analyzing repaired flexure-dominant RC bridge columns. The strategy includes time-dependent material models to simulate the changes made during the repair process, as well as versatile bar-slip and bar-buckling models to capture the effects of bar slip and bar buckling.
ENGINEERING STRUCTURES
(2022)
Article
Computer Science, Interdisciplinary Applications
Junjie Chen, Weisheng Lu, Jinfeng Lou
Summary: This study proposes a novel computational approach for detecting and reconstructing concrete defects from geotagged aerial images. By aligning with a building information model (BIM), the approach retrieves material semantics and determines regions of interest for defect detection. It effectively rectifies camera poses and achieves precise defect reconstruction. The experiments demonstrate the effectiveness of the approach and successful 3D reconstruction of defects.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2023)
Article
Construction & Building Technology
Xuan Liu, Xiaochuan Jing, Quan Zhu, Wanru Du, Xiaoyin Wang
Summary: This study proposes an automatic hazard identification method that integrates on-site scene graph generation and domain-specific knowledge extraction. The proposed method achieves strong performance in various metrics on self-built and widely used public datasets, and can precisely extract relational information from visual and text modalities to facilitate on-site hazard identification.
Article
Computer Science, Interdisciplinary Applications
Tatsuro Yamane, Pang-jo Chun, Ji Dang, Riki Honda
Summary: This paper proposes a method that uses deep learning and structure from motion to integrate and record damage detected from multiple images into a 3D model. The method improves bridge inspection efficiency and allows for assessment of the damage scale and inspection omissions.
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING
(2023)
Article
Remote Sensing
Han Liang, Seong-Cheol Lee, Suyoung Seo
Summary: This study proposes a fully automated concrete bridge damage detection system using unmanned aerial vehicle (UAV) remote sensing technology. The system utilizes a Swin Transformer-based backbone network and a multi-scale attention pyramid network to achieve unprecedented speed and accuracy. Comparative analyses show that the proposed system outperforms commonly used target detection models. The robustness of the proposed system in real-world visual inspection reinforces its efficacy, leading to a new paradigm for bridge inspection and maintenance. The study highlights the potential of UAV-based inspection in enhancing efficiency and accuracy in bridge damage detection, emphasizing its pivotal role in ensuring the safety and longevity of vital infrastructure.
Article
Multidisciplinary Sciences
Dan Su, Yisheng Liu, Xintong Li, Zhicheng Cao
Summary: This study focuses on establishing a prediction model and optimizing bridge inspection standards to improve efficiency, enhance the scientific basis of bridge inspection, and increase the effectiveness of inspection through the introduction of multi-index bridge inspection standards, prediction model screening mechanisms, and linkage inspection mechanisms for adjacent traffic assets.
Article
Environmental Sciences
Hajar Zoubir, Mustapha Rguig, Mohamed El Aroussi, Abdellah Chehri, Rachid Saadane, Gwanggil Jeon
Summary: Conventional practices of bridge visual inspection have limitations in analyzing images manually. This paper explores the use of deep convolutional neural networks and presents a dataset for automatically identifying defects in bridge images. It also explores the application of interpretable networks in defect localization.
Article
Construction & Building Technology
Kien Dinh, Nenad Gucunski
Summary: This study aimed to explore factors affecting the detectability of concrete delamination in GPR images, using both synthetic and real data. The analysis revealed that factors such as delamination thickness, material within it, and emitted signal frequency impact the detectability, while the depth of delamination and its position relative to neighboring steel bars may also affect detection results.
CONSTRUCTION AND BUILDING MATERIALS
(2021)
Article
Engineering, Civil
Saif Aldabagh, Faroque Hossain, M. Shahria Alam
Summary: This study aims to establish engineering demand parameter (EDP) limits for concrete cover spalling and bar buckling in circular reinforced concrete bridge columns, in order to bring consistency to the performance-based design (PBD) methodology. By analyzing a database of previously tested bridge columns and using symbolic regression, the study proposes refined EDP limits and equations for predicting damage states and drift ratios at different levels.
EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS
(2023)
Article
Construction & Building Technology
Ahmed Gouda Mohamed, Ahmed Khaled, Ibrahim S. Abotaleb
Summary: This research aims to develop an integrated framework for inspection and maintenance intervention in reinforced concrete bridges (RCB), leveraging the potential of as-is Bridge Information Modeling (BrIM). It achieves this by converting 2D drawings into a 3D as-is BrIM model, creating a comprehensive bridge inventory, acquiring inspection data using advanced sensing technologies, and modeling structural defects on the as-is BrIM model. This framework greatly enhances the administration of bridge inspection and maintenance procedures, providing a thorough and clear picture of the bridge's current state.
Article
Construction & Building Technology
Sanggoo Kang, Yin Chao Wu, Dafnik Sarilkuma David, Suyun Ham
Summary: We demonstrate an automated system for evaluating cracks in concrete structures. By using an automatic impactor and air-coupled sensors, we are able to generate and detect reliable mechanical waves, which overcomes limitations of existing rapid damage assessment methods. Laboratory and field tests show that the proposed system performs well in detecting and characterizing cracks.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Construction & Building Technology
Nageh M. Ali, A. I. B. Farouk, S. I. Haruna, Hani Alanazi, Musa Adamu, Yasser E. Ibrahim
Summary: This study presented a feature selection-based approach for determining the optimal input parameters for classifying reinforced concrete columns failure modes. The Pearson correlation and mutual information techniques were used to test the relevance of input variables to the outputs, and the minimum redundancy maximum relevance algorithm was employed to select the important input variables. The aspect ratio, longitudinal rebar index, transverse rebar index, and axial load ratio were identified as the optimal input parameters.
CASE STUDIES IN CONSTRUCTION MATERIALS
(2022)
Article
Construction & Building Technology
Xinxing Yuan, Alan Smith, Fernando Moreu, Rodrigo Sarlo, Christopher D. Lippitt, Maryam Hojati, Sreenivas Alampalli, Su Zhang
Summary: This paper proposes a framework for automatically quantifying the quality of rebar placement in the field to enhance construction quality and the long-term durability of bridge structures. The authors used a mobile LiDAR scanner to obtain three-dimensional point clouds of a concrete bridge construction site. Two algorithms were developed to automatically extract the bridge rebar locations and quantify the layout at different levels. The framework was found to effectively inform rebar placement quality and provide a permanent record of the bridge deck quality.
AUTOMATION IN CONSTRUCTION
(2023)
Article
Engineering, Civil
Sherif M. S. Osman, Saif Aldabagh, M. Shahria Alam, Shamim A. A. Sheikh
Summary: This study establishes drift ratio limit states and corresponding strengths for hybrid GFRP-steel RC circular bridge columns. The adopted reinforcement layout improves corrosion resistance while maintaining stiffness and ductility. A validated fiber-based model is utilized to predict global as well as local responses of hybrid RC columns.
JOURNAL OF COMPOSITES FOR CONSTRUCTION
(2023)
Article
Engineering, Civil
John Patrick Fitzsimmons, Ruodan Lu, Ying Hong, Ioannis Brilakis
Summary: The UK spends billions of pounds on infrastructure construction works annually, but more than half of them are delayed, causing stakeholders' interests to be compromised. This research introduces a hybrid method to improve the accuracy of risk analysis and prediction of project delays, by combining machine intelligence with a large database of completed infrastructure construction projects in the UK. The results show a 54.4% increase in accuracy in predicting project delays compared to traditional methods.
JOURNAL OF INFORMATION TECHNOLOGY IN CONSTRUCTION
(2022)
Article
Computer Science, Artificial Intelligence
Ying Hong, Haiyan Xie, Vahan Hovhannisyan, Ioannis Brilakis
Summary: Construction schedules are crucial for project success, but they often require experienced schedulers. This study proposes a graph-based method to find the most time-efficient construction sequence from historic projects, improving scheduling productivity and accuracy. Results indicate that earthwork sequences are the least time-efficient and frequent sequences learned from past projects are closer to the actual schedule.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Construction & Building Technology
Haiyan Xie, Ying Hong, Ioannis Brilakis
Summary: This research investigates the obstacles in project execution and proposes a decision tree model to map user needs and trace workflows. The results show that the most urgent needs in time-related risk management are decision support tools, preparation assistance for risk communication, and generation of risk mitigation scenarios.
JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT
(2022)
Article
Computer Science, Artificial Intelligence
Ying Hong, Haiyan Xie, Gary Bhumbra, Ioannis Brilakis
Summary: This study proposes an ontology-based Recurrent Neural Network approach to bi-directionally translate between human written language and machinery ontological language. Experimental results show that the proposed approach has good performance in text generation accuracy, machine readability, and human understandability.
ADVANCED ENGINEERING INFORMATICS
(2022)
Article
Construction & Building Technology
Yuandong Pan, Alexander Braun, Ioannis Brilakis
Summary: This paper introduces a novel method to enrich geometric digital twins of buildings by capturing important entities from the electrical and fire-safety domain. The method fuses laser scanning and photogrammetry to capture relevant objects, recognize them in 2D images, and map them to a 3D space. The resulting digital twin contains geometric information, semantic information, and useful text information, and can be used for condition monitoring, facility maintenance, and management.
AUTOMATION IN CONSTRUCTION
(2022)
Article
Engineering, Electrical & Electronic
Xin Wang, Dharmaraj Veeramani, Zhenhua Zhu
Summary: This article proposes a system for recognizing construction workers' hand gestures using wearable sensors on fingers. The system extracts motion data by synchronizing, normalizing, and smoothing finger motions and uses an enhanced fully convolutional neural network (FCN) for gesture recognition. The system achieved a precision and recall of 85.7% and 93.8% respectively in the system validation test. A pilot study demonstrated the feasibility of using the proposed system to interact with a robotic dump truck. Furthermore, the system was compared with vision-based recognition methods to assess their relative advantages and limitations quantitatively and qualitatively.
IEEE SENSORS JOURNAL
(2023)
Article
Construction & Building Technology
Ying Hong, Haiyan Xie, Eva Agapaki, Ioannis Brilakis
Summary: The construction industry has long struggled with delays and cost overruns. This paper proposes a graph-based automated scheduling (GAS) method to capture, store, and reuse the tacit knowledge in construction schedules. The GAS method was validated on two case studies and proved to be more accurate in generating construction schedules compared to planned schedules.
JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT
(2023)
Article
Engineering, Civil
Xin Wang, Wei Han, Eric Du, Fei Dai, Zhenhua Zhu
Summary: This paper proposes a novel eye gaze-aided virtual tape measure framework that enables hands-free measurements in construction. The framework includes three components: data collection for point of interest, sensor calibration, and distance calculation. The effectiveness of the framework is tested by measuring the dimensions of 15 common objects in laboratory and on-site environments, achieving an average absolute error of 2.4 cm and a relative error of 4.8%. A comparison study demonstrates its superior performance over iPhone's Measure application.
CANADIAN JOURNAL OF CIVIL ENGINEERING
(2023)
Article
Computer Science, Interdisciplinary Applications
Maciej Trzeciak, Kacper Pluta, Yasmin Fathy, Lucio Alcalde, Stanley Chee, Antony Bromley, Ioannis Brilakis, Pierre Alliez
Summary: This paper introduces a periodically collected data set on a construction site, aiming to evaluate the performance of SLAM algorithms used by mobile scanners or autonomous robots. The data set includes ground-truth scans, spatially registered and time-synchronized images, lidar scans, and inertial data. The paper also demonstrates how to measure the accuracy of SLAM algorithms against the ground-truth trajectory using a popular software package. This is the first publicly accessible data set of sequentially collected data on a construction site.
JOURNAL OF COMPUTING IN CIVIL ENGINEERING
(2023)
Review
Construction & Building Technology
Hexu Liu, SangHyeok Han, Zhenhua Zhu
Summary: The construction industry is often criticized for its low productivity, lack of collaboration and information sharing, poor contract administration, and fragmented structure. Recently, blockchain technology has gained attention for its potential benefits. This research conducted a bibliometric-qualitative review to analyze the research trends and needs in the field. The results showed that current research on blockchain in construction focuses on smart contract, Building Information Modeling (BIM), supply chain management, construction contract, construction and project management, digital twin, and smart city. Future research should focus on quantifying cost-benefits, integrating blockchain with project delivery systems, and exploring technology fusion for construction management.
JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT
(2023)
Review
Chemistry, Analytical
Viktor Drobnyi, Zhiqi Hu, Yasmin Fathy, Ioannis Brilakis
Summary: Most existing buildings were built based on 2D drawings, but building information models have become prevalent in recent years. However, it will take a long time for these models to be widely adopted in all existing buildings. This paper reviews the state-of-the-art practice and research for constructing and maintaining geometric digital twins, and proposes a new geometry-based object class hierarchy to prioritize automation.
Article
Engineering, Civil
Yanmo Weng, Subasish Das, Stephanie German Paal
Summary: Pedestrian deaths account for 23% of all road traffic fatalities worldwide. In the United States, pedestrian fatalities have been increasing after declining for three decades, reaching the highest number in more than two decades in 2020. The Pedestrian and Bicycle Crash Analysis Tool (PBCAT) was developed to better understand the factors leading to crashes involving pedestrians. This study assessed the quality of police-reported crash narratives on pedestrian-involved traffic crashes and used advanced machine learning (FSL) to classify crash types with approximately 40% overall accuracy.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Construction & Building Technology
Sean T. Bennett, Wei Han, Dilruba Mahmud, Peter G. Adamczyk, Fei Dai, Michael Wehner, Dharmaraj Veeramani, Zhenhua Zhu
Summary: The labor-intensive construction industry puts workers at risk of musculoskeletal injuries due to physically demanding manual work. Exoskeletons and exosuits, known as EXOs, are designed to reduce exertion and muscle fatigue in order to protect workers. However, the usability of EXOs in construction remains unclear.
Article
Automation & Control Systems
Xin Wang, Dharmaraj Veeramani, Zhenhua Zhu
Summary: The advances in construction robotics have driven construction automation, and user-friendly interfaces, such as hand gestures, are crucial for increasing the adoption of construction robots. This paper proposes a novel framework that combines human gaze-aware hand gesture recognition with visual detection and tracking using an eye tracker for intelligent construction. The framework achieved high precision and recall rates in a validation test and demonstrated effective human-robot collaboration in construction through a pilot study with an excavator and a dump truck.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Computer Science, Interdisciplinary Applications
Maciej Trzeciak, Ioannis Brilakis
Summary: In this paper, a dense 3D reconstruction pipeline is proposed to improve the resolution of point clouds captured by handheld scanners. Time-synchronized and spatially registered images and lidar sweeps are fused using spatial AI methods to generate higher resolution dense scans for progressive reconstruction. The results showed a reduction of 11% in overall point cloud noise and an increase in density by approximately six times.
JOURNAL OF COMPUTING IN CIVIL ENGINEERING
(2023)
Article
Construction & Building Technology
Jia Liang, Qipeng Zhang, Xingyu Gu
Summary: A lightweight PCSNet-based segmentation model is developed to address the issues of insufficient performance in feature extraction and boundary loss information. The introduction of generalized Dice loss improves prediction performance, and the visualization of class activation mapping enhances model interpretability.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Gilsu Jeong, Minhyuk Jung, Seongeun Park, Moonseo Park, Changbum Ryan Ahn
Summary: This study introduces a contextual audio-visual approach to recognize multi-equipment activities in tunnel construction sites, improving monitoring effectiveness. Tested against real-world operation data, the model achieved remarkable results, emphasizing the potential of contextual multimodal models in enhancing operational efficiency in complex construction sites.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Jin Wang, Zhigao Zeng, Pradip Kumar Sharma, Osama Alfarraj, Amr Tolba, Jianming Zhang, Lei Wang
Summary: This study presents a dual-path network for pavement crack segmentation, combining Convolutional Neural Network (CNN) and transformer. A lightweight CNN encoder is used for local feature extraction, while a novel transformer encoder integrates high-low frequency attention mechanism and efficient feedforward network for global feature extraction. Additionally, a complementary fusion module is introduced to aggregate intermediate features extracted from both encoders. Evaluation on three datasets confirms the superior performance of the proposed network.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Pierre Gilibert, Romain Mesnil, Olivier Baverel
Summary: This paper introduces a flexible method for crafting 2D assemblies adaptable to various geometric assumptions in the realm of sustainable construction. By utilizing digital fabrication technologies and optimization approaches, precise control over demountable buildings can be achieved, improving mechanical performance and sustainability.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Jorge Loy-Benitez, Myung Kyu Song, Yo-Hyun Choi, Je-Kyum Lee, Sean Seungwon Lee
Summary: This paper discusses the advancement of tunnel boring machines (TBM) through the application of artificial intelligence. It highlights the significance of AI-based management subsystems for automatic TBM operations and presents recent contributions in this field. The paper evaluates modeling, monitoring, and control subsystems and suggests research paths for integrating existing management subsystems into TBM automation.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Alireza Shamshiri, Kyeong Rok Ryu, June Young Park
Summary: This paper reviews the application of text mining and natural language processing in the construction field, highlighting the need for automation and minimizing manual tasks. The study identifies potential research opportunities in strengthening overlooked construction aspects, coupling diverse data formats, and leveraging pre-trained language models and reinforcement learning.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Zhengyi Chen, Hao Wang, Keyu Chen, Changhao Song, Xiao Zhang, Boyu Wang, Jack C. P. Cheng
Summary: This study proposes an improved coverage path planning system that leverages building information modeling and robotic configurations to optimize coverage performance in indoor environments. Experimental validation shows the effectiveness and applicability of the system. Future research will focus on further enhancing coverage ratio and optimizing computation time.
AUTOMATION IN CONSTRUCTION
(2024)
Review
Construction & Building Technology
Yonglin Fu, Junjie Chen, Weisheng Lu
Summary: This study presents a review of human-robot collaboration (HRC) in modular construction manufacturing (MCM), focusing on tasks, human roles, and interaction levels. The review found that HRC solutions are applicable to various MCM tasks, with a primary focus on timber component production. It also revealed the diverse collaborative roles humans can play and the varying levels of interaction with robots.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Qiong Liu, Shengbo Cheng, Chang Sun, Kailun Chen, Wengui Li, Vivian W. Y. Tam
Summary: This paper presents an approach to enhance the path-following capability of concrete printing by integrating steel cables into the printed mortar strips, and validates the feasibility and effectiveness of this approach through experiments.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Honghu Chu, Lu Deng, Huaqing Yuan, Lizhi Long, Jingjing Guo
Summary: The study proposes a method called Cascade CATransUNet for high-resolution crack image segmentation. This method combines the coordinate attention mechanism and self-cascaded design to accurately segment cracks. Through a customized feature extraction architecture and an optimized boundary loss function, the proposed method achieves impressive segmentation performance on HR images and demonstrates its practicality in UAV crack detection tasks.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Daniel Lamas, Andres Justo, Mario Soilan, Belen Riveiro
Summary: This paper introduces a new method for creating synthetic point clouds of truss bridges and demonstrates the effectiveness of a deep learning approach for semantic and instance segmentation of these point clouds. The proposed methodology has significant implications for the development of automated inspection and monitoring systems for truss bridges.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Kahyun Jeon, Ghang Lee, Seongmin Yang, Yonghan Kim, Seungah Suh
Summary: This study proposes two enhanced unsupervised text classification methods for domain-specific non-English text. The results of the tests show that these methods achieve excellent performance on Korean building defect complaints, outperforming state-of-the-art zero-shot and few-shot text classification methods, with minimal data preparation effort and computing resources.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Yoonhwa Jung, Julia Hockenmaier, Mani Golparvar-Fard
Summary: This study introduces a transformer-based natural language processing model, UNIfORMATBRIDGE, that automatically labels activities in a project schedule with Uniformat classification. Experimental results show that the model performs well in matching unstructured schedule data to Uniformat classifications. Additionally, the study highlights the importance of this method in developing new techniques.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
De-Graft Joe Opoku, Srinath Perera, Robert Osei-Kyei, Maria Rashidi, Keivan Bamdad, Tosin Famakinwa
Summary: This paper introduces a digital twin technology combining Building Information Modelling and the Internet of Things for the construction industry, aiming to optimize building conditions. The technology is implemented in a university library, successfully achieving real-time data capture and visual representation of internal conditions.
AUTOMATION IN CONSTRUCTION
(2024)
Article
Construction & Building Technology
Zaolin Pan, Yantao Yu
Summary: The construction industry faces safety and workforce shortages globally, and worker-robot collaboration is seen as a solution. However, robots face challenges in recognizing worker intentions in construction. This study tackles these challenges by proposing a fusion method and investigating the best granularity for recognizing worker intentions. The results show that the proposed method can recognize multi-granular worker intentions effectively, contributing to seamless worker-robot collaboration in construction.
AUTOMATION IN CONSTRUCTION
(2024)
