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Shape meets Euclid: Integrating shape computation with ruler and compass procedures Autom. Constr. (IF 9.6) Pub Date : 2024-06-28 Athanassios Economou, Tzu-Chieh Kurt Hong, Russell Newton
The recent success of a class of mechanical applications of shape rules in CAD shape grammar interpreters has produced a renewed interest in the design and support of shape embedding and shape rewrite technologies. Despite this, some fundamental constructions in rule-based modeling, namely, any of Euclid's straightedge and compass constructions, appear awkward to simulate within the fuse-embed cycle
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Integrated representation of technical systems with BIM and linked data: TUBES system ontology Autom. Constr. (IF 9.6) Pub Date : 2024-06-28 Nicolas Pauen, Jérôme Frisch, Christoph van Treeck
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Impact of aggregate gradation and asphalt-aggregate ratio on pavement performance during construction using back propagation neural network Autom. Constr. (IF 9.6) Pub Date : 2024-06-27 Ziyao Wei, Kun Hou, Yanshun Jia, Shaoquan Wang, Yingsong Li, Zeqi Chen, Ziyue Zhou, Ying Gao
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Applications of blockchain for construction project procurement Autom. Constr. (IF 9.6) Pub Date : 2024-06-27 Minju Kim, Yong-Woo Kim
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Time series anomaly detection using generative adversarial network discriminators and density estimation for infrastructure systems Autom. Constr. (IF 9.6) Pub Date : 2024-06-27 Yueyan Gu, Farrokh Jazizadeh
Efficient data-driven defect detection techniques are crucial for maintaining service quality and providing early warnings for infrastructure systems. To this end, we proposed an effective unsupervised anomaly detection framework (DEGAN) using Generative Adversarial Networks (GANs). The framework relies solely on normal time series data as input to train well-configured discriminators into standalone
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Two-stage task allocation for multiple construction robots using an improved genetic algorithm Autom. Constr. (IF 9.6) Pub Date : 2024-06-26 Xiaotian Ye, Hongling Guo, Zhubang Luo
The construction industry is facing serious challenges, such as labor shortage and safety hazards. Construction robots can perform dangerous and repetitive tasks instead of workers. However, complex construction tasks require multi-robot collaboration, which needs efficient task allocation. This paper describes a two-stage task allocation method using an improved Genetic Algorithm (GA) for multiple
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Hybrid knowledge and data driven approach for prioritizing sewer sediment cleaning Autom. Constr. (IF 9.6) Pub Date : 2024-06-25 Chen Li, Ke Chen, Zhikang Bao, S. Thomas Ng
The efficient assessment of sewer sediment condition is important for municipalities to formulate prioritization strategies for cleaning initiatives. However, manual assessment methods are plagued by inherent subjective and inaccuracy. To address these deficiencies, this paper introduces a hybrid approach integrating both knowledge-based principles and data-driven techniques for Sewer Sediment Cleaning
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Buildability of complex 3D-printed concrete geometries using Peridynamics Autom. Constr. (IF 9.6) Pub Date : 2024-06-25 Jinggao Zhu, Miguel Cervera, Xiaodan Ren
As a formwork-free construction method, 3D–printed concrete (3DPC) shows great advantages in forming complex structures; but encounters buildability problems. To address this, this paper presents a fluid-solid integrated peridynamic (PD) model to describe the fluid-to-solid transition of 3DPC. On this basis, an automatic strategy is proposed to deal with complex geometries. Programming by Python, PD
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Using Eye-Tracking to Measure Worker Situation Awareness in Augmented Reality Autom. Constr. (IF 9.6) Pub Date : 2024-06-24 Shaoze Wu, Haosen Chen, Lei Hou, Guomin (Kevin) Zhang, Chun-Qing Li
Augmented Reality (AR) technology has emerged as a promising tool for enhancing safety in the construction industry by improving the Situation Awareness (SA) of onsite workers. However, there is a lack of methods to quantify the impact of real-time AR visual warnings on developing and updating SA. To address this gap, this paper presents an eye-tracking-based method that quantifies the impact using
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Training strategy and intelligent model for in-situ rapid measurement of subgrade compactness Autom. Constr. (IF 9.6) Pub Date : 2024-06-24 Xuefei Wang, Xiangdong Li, Jiale Li, Jianmin Zhang, Guowei Ma
Current methods for inspecting subgrade compaction quality are time-consuming and destructive. This paper presents an in-situ measurement methodology for rapid detection of subgrade compaction quality using Ultrasonic Pulse Velocity (UPV) and Intelligent Compaction (IC). Field compaction tests, field UPV test, and laboratory tests are performed to construct the heterogeneous datasets. A set of intelligent
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Impact of simulation fidelity on identifying swing-over hazards in virtual environments for novice crane operators Autom. Constr. (IF 9.6) Pub Date : 2024-06-24 Tanghan Jiang, Yihai Fang, Jiantsen Goh, Songbo Hu
Crane-related accidents often occur due to failure in timely and accurate hazard identification. Virtual Reality (VR) technology facilitates immersive environments for improving hazard identification without real-world consequences. However, the impact of simulation fidelity on hazard identification performance, particularly for novice crane operators, remains underexplored. This paper examines how
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Advancements in digital twin modeling for underground spaces and lightweight geometric modeling technologies Autom. Constr. (IF 9.6) Pub Date : 2024-06-24 Haofeng Gong, Dong Su, Shiqi Zeng, Xiangsheng Chen
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Optimizing MEP design in early AEC projects through generative design Autom. Constr. (IF 9.6) Pub Date : 2024-06-24 Edgar Pestana, Andrew Paice, Shaun West
The digital transformation of the AEC industry through BIM has improved productivity during detailed design and construction planning phases. Early design choices influence a project's success but have yet to benefit from BIM-based approaches. This paper investigates the feasibility and acceptance of employing Generative Design (GD) to optimize early Mechanical, Electrical, and Plumbing (MEP) designs
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Enhancing cyber risk identification in the construction industry using language models Autom. Constr. (IF 9.6) Pub Date : 2024-06-24 Dongchi Yao, Borja García de Soto
Modern construction projects are vulnerable to cyber-attacks due to insufficient attention to cybersecurity. Cyber risks in construction projects are not fully recognized, and the relevant literature is limited. To address this gap, the capabilities of a language model were leveraged to analyze extensive text, tailored to identify cyber risks. The model was trained using a curated corpus related to
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Parallel camera network: Motion-compensation vision measurement method and system for structural displacement Autom. Constr. (IF 9.6) Pub Date : 2024-06-24 Xiaolin Liu, Biao Hu, Yihe Yin, Yueqiang Zhang, Wenjun Chen, Qifeng Yu, Xiaohua Ding, Linhai Han
This study proposes a motion-compensation vision measurement method and system for structural displacement, termed the parallel camera network (PCN). Based on the fixedly connected calibration cameras and measurement cameras, the PCN method effectively address the optical measurement problems of the unstable observation platform. Unlike existing ego-motion compensation methods, the PCN method allows
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Hypermedia-driven RESTful API for digital twins of the built environment Autom. Constr. (IF 9.6) Pub Date : 2024-06-24 Stefan Herlé, Jörg Blankenbach
Core domains to establish digital twins of the built environment are Building Information Modeling (BIM), Geographic Information System (GIS) and Internet of Things (IoT). RESTful Application Programming Interfaces (APIs) of these domains allow easy access to the digital twin. While multiple information models are reasonable since the focus of these domains is distinct, interoperability becomes a challenge
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3D laser scanning for automated structural modeling and deviation monitoring of multi-section prefabricated cable domes Autom. Constr. (IF 9.6) Pub Date : 2024-06-22 Ailin Zhang, Hao Ma, Xi Zhao, Yanxia Zhang, Jie Wang, Meini Su
This paper presents a multi-member automatic structural modeling (MASM) method for high-thrust deviation monitoring of prefabricated cable domes. Point cloud data generated by three-dimensional (3D) laser scanning were segmented into structural modules to effectively reduce the method's computational complexity. A multimember central shrinkage algorithm was developed for skeleton-point recognition
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Integrated reinforcement and imitation learning for tower crane lift path planning Autom. Constr. (IF 9.6) Pub Date : 2024-06-22 Zikang Wang, Chun Huang, Boqiang Yao, Xin Li
Reinforcement learning (RL) has emerged as a promising solution method for crane-lift path planning. However, designing appropriate reward functions for tower crane (TC) operations remains particularly challenging. Poor design of reward functions can lead to non-executable lifting paths. This paper presents a framework combining imitation learning (IL) and RL to address the challenge. The framework
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Preventing falls from floor openings using quadrilateral detection and construction worker pose-estimation Autom. Constr. (IF 9.6) Pub Date : 2024-06-22 Minsoo Park, Almo Senja Kulinan, Tran Quoc Dai, Jinyeong Bak, Seunghee Park
This paper addressed safety risks in the construction industry, emphasizing the prevalent and fatal risk of falls from heights due to floor openings. Although advancements in computer vision and deep learning offer opportunities for automated safety monitoring, challenges such as inaccuracies in object localization and measuring distances to unsafe zones persist. To overcome these issues, a detection
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4D point cloud-based spatial-temporal semantic registration for monitoring mobile crane construction activities Autom. Constr. (IF 9.6) Pub Date : 2024-06-21 Dong Liang, Sou-Han Chen, Zhe Chen, Yijie Wu, Louis Y.L. Chu, Fan Xue
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Recognizing temporary construction site objects using CLIP-based few-shot learning and multi-modal prototypes Autom. Constr. (IF 9.6) Pub Date : 2024-06-21 Yuanchang Liang, Prahlad Vadakkepat PhD, David Kim Huat Chua PhD, Shuyi Wang, Zhigang Li, Shuxiang Zhang
Visual understanding of temporary on-site objects is essential for robots and project management in construction. Implementation of deep learning algorithms is challenging on construction sites due to high data annotation cost, demanding computational power, and lack of large-scale training datasets. Recognizing on-site temporary objects demands the algorithms to learn in a data-efficient way. To fill
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Semantic model-based large-scale deployment of AI-driven building management applications Autom. Constr. (IF 9.6) Pub Date : 2024-06-20 Kan Xu, Zhe Chen, Fu Xiao, Jing Zhang, Hanbei Zhang, Tianyou Ma
Digitalization and Artificial Intelligent (AI) are revolutionizing building operation management. The abundance of data generated with the digitalization of buildings in the whole lifecycle can be harnessed to enhance building operational efficiency through data-driven control and optimization applications. However, the heterogeneity of data across building datasets hampers data interactivity and interoperability
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Domain knowledge-enhanced region growing framework for semantic segmentation of bridge point clouds Autom. Constr. (IF 9.6) Pub Date : 2024-06-20 Tao Yang, Yang Zou, Xiaofei Yang, Enrique del Rey Castillo
Utilising domain knowledge (DK) to semantically segment bridge point clouds has attracted growing research interest. However, current approaches are often tailored to specific bridges, limiting their general applicability. To address this problem, this paper introduces a DK-enhanced Region Growing (DKRG) framework for point cloud semantic segmentation of reinforced concrete (RC) girder bridges. Inspired
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Adaptive VMD and multi-stage stabilized transformer-based long-distance forecasting for multiple shield machine tunneling parameters Autom. Constr. (IF 9.6) Pub Date : 2024-06-20 Chengjin Qin, Guoqiang Huang, Honggan Yu, Zhinan Zhang, Jianfeng Tao, Chengliang Liu
Achieving multivariate long-distance forecasting of shield machine tunneling parameters remains a challenge due to the huge number of tunneling parameters and the complexity of the variation pattern. To solve this problem, a long-distance forecasting method called Adaptive Variational Mode Decomposition and Multi-Stage Stabilized Transformer-based (AVMD-MST) for multiple tunneling parameters is proposed
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Data-driven ergonomic assessment of construction workers Autom. Constr. (IF 9.6) Pub Date : 2024-06-20 Zirui Li, Yantao Yu, Jikang Xia, Xinyu Chen, Xun Lu, Qiming Li
Automating ergonomic assessment improves both objectivity and cost-effectiveness. However, existing ergonomic scales remain susceptible to inaccuracy and insensitivity when assessing diversified construction activities. This susceptibility stems from the unreliable ranges, sharp boundaries, and oversimplified binary rules within conventional joint-level assessment rules, coupled with the restricted
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Pre-processing and analysis of building information models for automated geometric quality control Autom. Constr. (IF 9.6) Pub Date : 2024-06-20 Martín Bueno, Frédéric Bosché
Research in Quality Control (QC) process digitalisation has principally focused on novel technologies for data acquisition and processing during construction. In contrast, this manuscript focuses on the planning phase and proposes a method that analyses the as-planned 4D Building Information Model (‘BIM model’) to obtain: (1) an exhaustive list of all geometric QC instances to be checked during construction;
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Monitoring and evaluating the status and behaviour of construction workers using wearable sensing technologies Autom. Constr. (IF 9.6) Pub Date : 2024-06-20 Mingzhu Wang, Jiayu Chen, Jun Ma
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Deployable scissor structures: Classification of modifications and applications Autom. Constr. (IF 9.6) Pub Date : 2024-06-20 Yuan Liao, Sudarshan Krishnan
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Decoding a decade: The evolution of artificial intelligence in security, communication, and maintenance within the construction industry Autom. Constr. (IF 9.6) Pub Date : 2024-06-19 Thu Giang Mai, Minh Nguyen, Akbar Ghobakhlou, Wei Qi Yan, Bunleng Chhun, Hoa Nguyen
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Recognizing sitting activities of excavator operators using multi-sensor data fusion with machine learning and deep learning algorithms Autom. Constr. (IF 9.6) Pub Date : 2024-06-17 Jue Li, Gaotong Chen, Maxwell Fordjour Antwi-Afari
Recognizing excavator operators' sitting activities is crucial for improving their health, safety, and productivity. Moreover, it provides essential information for comprehending operators' behavior patterns and their interaction with construction equipment. However, limited research has been conducted on recognizing excavator operators' sitting activities. This paper presents a method for recognizing
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Agent-based post-earthquake evacuation simulation to enhance early-stage architectural layout and non-structural design Autom. Constr. (IF 9.6) Pub Date : 2024-06-17 Sajjad Hassanpour, Vicente A. González, Yang Zou, Jiamou Liu, Guillermo Cabrera-Guerrero
In the indoor design process, architects make crucial decisions regarding architectural layout and the selection of non-structural elements. However, there is a lack of comprehensive consideration for human evacuation behavior, specifically in the event of earthquake evacuation, during the design process. This paper bridges this gap by presenting the application of Agent-Based Building Earthquake Evacuation
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Distributed collaborative inspections through smart infrastructure metaverse Autom. Constr. (IF 9.6) Pub Date : 2024-06-16 Zaid Abbas Al-Sabbag, Chul Min Yeum, Sriram Narasimhan
We propose a real-time distributed collaborative system where on- and off-site inspectors perform synchronous structural inspections; called . (MR) headsets are equipped with a holographic display that allows on-site inspectors to visualize digitized information overlaid on the structure. On the other hand, (VR) headsets allow remote inspectors to visualize and interact with a pre-built 3D point cloud
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Context-aware semantic segmentation network for tunnel face feature identification Autom. Constr. (IF 9.6) Pub Date : 2024-06-14 Liang Zhao, Shuya Hao, Zhanping Song
The automated interpretation of tunnel face geological information is significant to the construction decision-making of rock mass engineering. An intelligent recognition algorithm, named the Transformer and Convolution neural networks Semantic segmentation Network (TCSeNet), is introduced to overcome the low interpretation accuracy caused by certain limitations of existing automated interpretation
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Decentralized adaptive work package learning for personalized and privacy-preserving occupational health and safety monitoring in construction Autom. Constr. (IF 9.6) Pub Date : 2024-06-14 Xiao Li, Jianhuan Zeng, Chen Chen, Teng Li, Jun Ma
Precision construction occupational health and safety (COHS) is a prerequisite for project success. Work package-based distributed monitoring shows a high capability for this purpose. However, a theoretical dilemma exists between larger work packages with greater technical efficiency and smaller ones with greater data privacy. This paper develops a (DAWP) learning model and blockchain for personalized
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Generative adversarial network for optimization of operational parameters based on shield posture requirements Autom. Constr. (IF 9.6) Pub Date : 2024-06-14 Peinan Li, Zeyu Dai, Yi Rui, Jiaxin Ling, Jun Liu, Yixin Zhai, Jie Fan
To mitigate the potential hazards of shield tunneling misalignment (STM) caused by tunneling posture deviation, a method for optimizing operational parameters tailored to tunneling posture adjustment is developed. This paper presents a generative adversarial network (GAN) framework that incorporate a conditional generative adversarial network (CGAN) and two distinct discriminators (WGAN and Path GAN)
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Pixel-level automatic detection and quantification of running bands on rail surfaces Autom. Constr. (IF 9.6) Pub Date : 2024-06-14 Mingjing Yue, Xiancai Yang, Yao Qian, Ping Wang, Jingmang Xu, Allen A. Zhang
The running band on the rail surface serves as a crucial indicator of the wheel-rail contact relationship, making quantitative detection of the running band vital for railroad inspection and maintenance. However, current rail running band detection methods remain manual, and leveraging state-of-the-art deep learning technology can significantly enhance detection efficiency and minimize the reliance
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Automated geometric reconstruction and cable force inference for cable-net structures using 3D point clouds Autom. Constr. (IF 9.6) Pub Date : 2024-06-14 Siwei Lin, Liping Duan, Jiming Liu, Xiao Xiao, Ji Miao, Jincheng Zhao
Laser scanning provides an efficient solution to digital twin construction in civil engineering. The complexity and redundancy of large-scale point clouds substantially prolong the labor-intensive model reconstruction process. This paper presents an automated and high-precision geometric reconstruction approach for cable-net structures with a complete workflow from raw points to the extraction of cable
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Synchronized path planning and tracking for front and rear axles in articulated wheel loaders Autom. Constr. (IF 9.6) Pub Date : 2024-06-14 Yong Wang, Xinhui Liu, Zhankui Ren, Zongwei Yao, Xiaodan Tan
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From classification to segmentation with explainable AI: A study on crack detection and growth monitoring Autom. Constr. (IF 9.6) Pub Date : 2024-06-14 Florent Forest, Hugo Porta, Devis Tuia, Olga Fink
Monitoring surface cracks in infrastructure is crucial for structural health monitoring. Automatic visual inspection offers an effective solution, especially in hard-to-reach areas. Machine learning approaches have proven their effectiveness but typically require large annotated datasets for supervised training. Once a crack is detected, monitoring its severity often demands precise segmentation of
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A blockchain-based engineering design review service trading scheme for digital building permits Autom. Constr. (IF 9.6) Pub Date : 2024-06-14 Han Gao, Botao Zhong, Lieyun Ding
Engineering design review, which is a time-consuming task in the building permit process, faces challenges such as a scarcity of design review specialists, vulnerability to manipulation and corruption, and a lack of traceability and transparency. This paper explores the integration of blockchain and online service trading in addressing these challenges. This paper proposes a platform-based design review
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Deep learning-based object detection for dynamic construction site management Autom. Constr. (IF 9.6) Pub Date : 2024-06-14 Jiayi Xu, Wei Pan
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Point cloud and machine learning-based automated recognition and measurement of corrugated pipes and rebars for large precast concrete beams Autom. Constr. (IF 9.6) Pub Date : 2024-06-14 Jiangpeng Shu, Xiaowu Zhang, Wenhao Li, Ziyue Zeng, He Zhang, Yuanfeng Duan
It is important for quality inspection to quickly identify the correctness of the installation position of corrugated pipes and rebars on construction site. A point clouds and machine learning-based automated recognition and measurement method was developed for long reinforcement cages with corrugated pipes of concrete beams. A segmentation framework combining radius nearest-neighbor covariance characteristics
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Robust object detection in extreme construction conditions Autom. Constr. (IF 9.6) Pub Date : 2024-06-14 Yuexiong Ding, Ming Zhang, Jia Pan, Jinxing Hu, Xiaowei Luo
Current construction object detection models are vulnerable in complex conditions, as they are trained on conventional data and lack robustness in extreme situations. The lack of extreme data with relevant annotations worsens this situation. A new end-to-end unified image adaptation You-Only-Look-Once-v5 (UIA-YOLOv5) model is presented for robust object detection in five extreme conditions: low/intense
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Fusing infrastructure health monitoring data in point cloud Autom. Constr. (IF 9.6) Pub Date : 2024-06-13 Furkan Luleci, Jiapeng Chi, Carolina Cruz-Neira, Dirk Reiners, F. Necati Catbas
This paper presents a unique approach to data fusion in the condition assessment of civil infrastructure systems. The approach fuses dynamic monitoring sensor data with the visual data collected from a bridge structure. Such a data fusion method, named ModeShapeFuser, enables the visualization of mode shapes of engineering structures in their point cloud, offering a higher spatial resolution model
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Optimizing heterogeneous multi-robot team composition for long-horizon construction tasks: Time- and utilization-guided simulation Autom. Constr. (IF 9.6) Pub Date : 2024-06-13 Zaolin Pan, Yantao Yu
To boost productivity and efficiency, construction robots are anticipated to become increasingly prevalent in future construction workplaces. In such multi-robot environments, forming a well-composed robotic team is crucial for efficient task allocation and execution. However, the complexity of multi-robot dynamics, arising from the heterogeneity and varying scales of robotic teams, coupled with long-horizon
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A physics-based dimension reduction and modeling method for monitoring data and its application to tunnel engineering Autom. Constr. (IF 9.6) Pub Date : 2024-06-13 Minghui Ma, Siyang Zhou, Shanglin Liu, Yilan Kang, Qian Zhang
The promotion of intelligent control of engineering equipment requires extracting vital information from numerous original features in the monitoring data. Data-driven methods solely rely on data distribution to extract dimensional information, facing difficulties in comprehensively understanding and scrutinizing system behavior. To satisfy the control and optimization requirements of intelligent systems
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Knowledge graph of building information modelling (BIM) for facilities management (FM) Autom. Constr. (IF 9.6) Pub Date : 2024-06-13 Yan Peng, Cheong Peng Au-Yong, Nik Elyna Myeda
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Deep learning-based acoustic emission data clustering for crack evaluation of welded joints in field bridges Autom. Constr. (IF 9.6) Pub Date : 2024-06-12 Dan Li, Qingfeng Chen, Hao Wang, Peng Shen, Zibing Li, Wenyu He
To advance the intelligent operation and maintenance of bridges, a deep learning-based acoustic emission (AE) data clustering framework was developed for evaluating fatigue cracks in welded joints under conditions of operational noise interference and complex damage mechanisms. Specifically, a convolutional autoencoder (CAE) model was implemented to extract damage-sensitive features from AE wavelet
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Towards reinforcement learning - driven TBM cutter changing policies Autom. Constr. (IF 9.6) Pub Date : 2024-06-12 Tom F. Hansen, Georg H. Erharter, Thomas Marcher
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Generalized vision-based framework for construction productivity analysis using a standard classification system Autom. Constr. (IF 9.6) Pub Date : 2024-06-11 Junghoon Kim, Jeongbin Hwang, Insoo Jeong, Seokho Chi, JoonOh Seo, Jinwoo Kim
Enhancing construction productivity is paramount, and numerous researchers have utilized computer vision techniques to perform productivity analysis. However, previous approaches are often limited in their scalability and practical implementation as they can only be applied to specific construction works. Additionally, comprehensive training image datasets featuring varied scene compositions are essential
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Risk score inference for bridge maintenance projects using genetic fuzzy weighted pyramid operation tree Autom. Constr. (IF 9.6) Pub Date : 2024-06-11 Min-Yuan Cheng, Akhmad F.K. Khitam, Yi-Boon Kueh
In bridge maintenance, risk assessment is critical to prioritizing project work to minimize related risks and costs. However, the conventional method of risk assessment relies heavily on subjective judgments. The Genetic Fuzzy Weighted Pyramid Operation Tree (GFWPOT) was developed in this study to build a formula to solve the uncertainty problem and provide accurate prediction to bridge maintenance
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Monitoring and controlling engineering projects with blockchain-based critical chain project management Autom. Constr. (IF 9.6) Pub Date : 2024-06-11 Nermeen Bahnas, Kareem Adel, Rana Khallaf, Ahmed Elhakeem
Projects in the AEC industry often experience significant schedule delays due to complexity, inadequate planning, design inconsistencies, resource unavailability, poor resource utilization, or scope changes. This requires the use of project management techniques to control these challenges. This paper focuses on buffer monitoring and management in Critical Chain Project Management (CCPM) schedules
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Autoencoder-based Photoplethysmography (PPG) signal reliability enhancement in construction health monitoring Autom. Constr. (IF 9.6) Pub Date : 2024-06-10 Yogesh Gautam, Houtan Jebelli
Prior research has validated Photoplethysmography (PPG) as a promising biomarker for assessing stress factors in construction workers, including physical fatigue, mental stress, and heat stress. However, the reliability of PPG as a stress biomarker in construction workers is hindered by motion artifacts (MA) - distortions in blood volume pulse measurements caused by sensor movement. This paper develops
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Mortar spraying and plastering integrated robot for wall construction Autom. Constr. (IF 9.6) Pub Date : 2024-06-08 Yue Wang, Liangxi Xie, Jin Chen, Mengmeng Chen, Teng Hu, Hongyu Liao, Shibin Sun, Jian Chen
The mortar spraying and plastering integrated robot is designed to automate wall mortar construction, cost-effectively replacing manual labor. Firstly, the wall mortar construction process including spraying, micro-plastering, and monolithic plastering is proposed to enhance the efficiency and solidity of mortar application. Secondly, the robot posture model is established, and sources of error are
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Bridge damage description using adaptive attention-based image captioning Autom. Constr. (IF 9.6) Pub Date : 2024-06-08 Shunlong Li, Minghao Dang, Yang Xu, Andong Wang, Yapeng Guo
Current many vision-based research uses various classification, detection, and segmentation methods to identify bridge damage. Instead of these numerical results, a highly abstract natural language description is a more suitable method to summarize and transmit bridge inspection processes and results to humans. This paper presents an end-to-end image captioning-based bridge damage comprehensive description
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Integrated workflow for cooperative robotic fabrication of natural tree fork structures Autom. Constr. (IF 9.6) Pub Date : 2024-06-08 Hua Chai, Xinjie Zhou, Xiaofan Gao, Qinhui Yang, Yanmin Zhou, Philip F. Yuan
Natural wood, renowned for its low carbon emissions, renewability, and impressive durability, is gaining prominence in the construction industry as a sustainable solution. However, the irregularities in size and shape of natural wood pose challenges in precise measurement and fabrication. Although innovative methods have emerged for materials like curved logs and small-diameter wood, tree forks have
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Hybrid-driven autonomous excavator trajectory generation combining empirical driver skills and optimization Autom. Constr. (IF 9.6) Pub Date : 2024-06-08 Yuying Shen, Jixin Wang, Chenlong Feng, Qi Wang
Autonomous excavator trajectory generation faces greater challenges when dealing with complex scenarios and continuous repetitive operations. A hybrid-driven method is presented for generating excavator trajectories that incorporate empirical driver skills and optimization. Empirical position trajectories are modeled using generalized cylinders, enabling their generalization to reproduce trajectories
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Disassembly calculation criteria and methods for circular construction Autom. Constr. (IF 9.6) Pub Date : 2024-06-08 Shady Attia, Muheeb Al-Obaidy, Maxime Mori, Clémentine Campain, Enola Giannasi, Mike van Vliet, Eugenia Gasparri
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Predicting existing tunnel deformation from adjacent foundation pit construction using hybrid machine learning Autom. Constr. (IF 9.6) Pub Date : 2024-06-08 Xianguo Wu, Zongbao Feng, Jun Liu, Hongyu Chen, Yang Liu
To accurately predict the existing tunnel deformation from adjacent foundation pit construction (AFPC), a hybrid prediction framework based on random forest recursive feature elimination and the Bayesian optimization natural gradient boosting algorithm (RF-RFE-BO-NGBoost) is presented in this paper. The key findings from this study include the following: (1) RF-RFE effectively screens out crucial parameters
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Reducing redundancy in large MEP building information models for facility management using oriented bounding boxes and hash sets Autom. Constr. (IF 9.6) Pub Date : 2024-06-08 Zeng Guo, Qianyao Li, Qiankun Wang, Shi Qiao, Tingting Mei, Weiwei Zuo
The building information models of mechanical, electrical, and plumbing (MEP) systems often contain numerous identical elements, resulting in significant data redundancy. Current redundancy-removal techniques often involve intricate computational procedures and data conversions, which is time-consuming and error-prone. To overcome this limitation, this paper introduces an easy but efficient approach