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Reactive UAV-based automatic tunnel surface defect inspection with a field test Autom. Constr. (IF 10.3) Pub Date : 2024-04-24 Ran Zhang, Guangbo Hao, Kong Zhang, Zili Li
This work addresses the problem of automatic tunnel surface defect inspection using unmanned aerial vehicles (UAVs). The research aims at proposing a robust and efficient monitoring method for image data acquisition and processing in complex and dark tunnel environments. A method, called Proximity Move-Pause-Photo for Surface Defect Inspection (PMPP-SDI), is proposed by combining reactive flying control
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TBM tunneling strata automatic identification and working conditions decision support Autom. Constr. (IF 10.3) Pub Date : 2024-04-23 Kang Fu, Daohong Qiu, Yiguo Xue, Tao Shao, Gonghao Lan
It has become an important research topic to ensure the safe and efficient tunnel boring machine (TBM) tunneling in the construction of long distance and deep buried tunnels. This study aims to construct an assistant decision support system that integrates surrounding rock classification identification and tunneling parameters prediction optimization, providing quantitative decision guidance for driving
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Design of curvilinear sections in vertical alignment of roads and railways using general transition curves Autom. Constr. (IF 10.3) Pub Date : 2024-04-23 Andrzej Kobryń
Creation of road and railway routes in general and also design of vertical alignment in particular constitute an significant engineering problem. The vertical alignment of roads, and especially of railways, is of great importance for passenger comfort under high-speed driving conditions. This requires alignment smoothness to a greater extent. In practice, the dominant tendency is to consider vertical
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Construction metaverse: Application framework and adoption barriers Autom. Constr. (IF 10.3) Pub Date : 2024-04-18 Zhen-Song Chen, Jun-Yang Chen, Yue-Hua Chen, Witold Pedrycz
This paper addresses the limited research on the metaverse's application in the construction industry. It aims to investigate how the metaverse can empower construction, identify adoption barriers, and determine the most significant barriers. We propose a novel application framework of construction metaverse based on cyber-physical-social systems, identify 17 barriers using the political-economic-
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High-resolution infrastructure defect detection dataset sourced by unmanned systems and validated with deep learning Autom. Constr. (IF 10.3) Pub Date : 2024-04-18 Benyun Zhao, Xunkuai Zhou, Guidong Yang, Junjie Wen, Jihan Zhang, Jia Dou, Guang Li, Xi Chen, Ben M. Chen
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Sustainability and building information modelling: Integration, research gaps, and future directions Autom. Constr. (IF 10.3) Pub Date : 2024-04-17 Saeed Akbari, Moslem Sheikhkhoshkar, Farzad Pour Rahimian, Hind Bril El Haouzi, Mina Najafi, Saeed Talebi
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Computer vision in drone imagery for infrastructure management Autom. Constr. (IF 10.3) Pub Date : 2024-04-15 Naveed Ejaz, Salimur Choudhury
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Towards innovative and sustainable buildings: A comprehensive review of 3D printing in construction Autom. Constr. (IF 10.3) Pub Date : 2024-04-15 Habibelrahman Hassan, Edwin Rodriguez-Ubinas, Adil Al Tamimi, Esra Trepci, Abraham Mansouri, Khalfan Almehairbi
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Convolutional neural network-based model for recognizing TBM rock chip gradation Autom. Constr. (IF 10.3) Pub Date : 2024-04-13 Yuan-en Pang, Xu Li, Zi-kai Dong, Qiu-ming Gong
A tunnel boring machine (TBM) generates rock chips during excavation, which are crucial for assessing surrounding rock integrity, enhancing excavation efficiency, and evaluating cutter wear. However, traditional methods struggle to identify small rock chips, chips submerged in soil or water, and chips in stacked states. This paper proposes a convolutional neural network (CNN)-based method for directly
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Cascade refinement extraction network with active boundary loss for segmentation of concrete cracks from high-resolution images Autom. Constr. (IF 10.3) Pub Date : 2024-04-13 Lu Deng, Huaqing Yuan, Lizhi Long, Pang-jo Chun, Weiwei Chen, Honghu Chu
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Utilizing synthetic images to enhance the automated recognition of small-sized construction tools Autom. Constr. (IF 10.3) Pub Date : 2024-04-12 Soeun Han, Wonjun Park, Kyumin Jeong, Taehoon Hong, Choongwan Koo
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Multi-domain adaptive analysis of intelligent compaction measurement value for subgrade construction Autom. Constr. (IF 10.3) Pub Date : 2024-04-11 Xuefei Wang, Wei Lu, Jiale Li, Jianmin Zhang, Guowei Ma
Intelligent Compaction (IC) utilizes the Intelligent Compaction Measurement Value (ICMV) to assess compaction quality in real-time. However, current engineering practices rely on a fixed ICMV for evaluation, overlooking material variations and different compaction stages. The choice of ICMV is often dictated by the manufacturer, lacking rationality. This paper presents a multi-domain ICMV system to
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Automatic deflection measurement for outdoor steel structure based on digital image correlation and three-stage multi-scale clustering algorithm Autom. Constr. (IF 10.3) Pub Date : 2024-04-10 Haobo Sun, Yongqi Huang
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BIM-based automated rule-checking in the AECO industry: Learning from semiconductor manufacturing Autom. Constr. (IF 10.3) Pub Date : 2024-04-06 Wawan Solihin, Ziwen Liu, Yujie Lu, Lai Wei
The Architecture, Engineering, Construction and Owner-operated (AECO) industry faces an array of productivity challenges, in part stemming from limited interoperability and suboptimal management of building information across project lifecycles. This paper investigates the application of rule-based systems from the manufacturing industry, especially as used in the semiconductor manufacturing sector
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Predictive models in machine learning for strength and life cycle assessment of concrete structures Autom. Constr. (IF 10.3) Pub Date : 2024-04-03 A. Dinesh, B. Rahul Prasad
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An unsupervised low-light image enhancement method for improving V-SLAM localization in uneven low-light construction sites Autom. Constr. (IF 10.3) Pub Date : 2024-04-03 Xinyu Chen, Yantao Yu
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Real-time risk assessment of multi-parameter induced fall accidents at construction sites Autom. Constr. (IF 10.3) Pub Date : 2024-04-01 Min-Yuan Cheng, Quoc-Tuan Vu, Ren-Kwei Teng
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IFC data extension for real-time safety monitoring of automated construction in high-rise building projects Autom. Constr. (IF 10.3) Pub Date : 2024-03-30 Ruibo Hu, Ke Chen, Weiguang Jiang, Hanbin Luo
Accessing real-time data from the Automated Construction System (ACS) through diverse field sensors holds paramount importance in effective safety management. However, the challenge lies in overcoming inefficient data exchange and information integration. This study seeks to extend the Industry Foundation Class (IFC) standard to seamlessly integrate real-time safety information with the digital model
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Towards accurate correspondence between BIM and construction using high-dimensional point cloud feature tensor Autom. Constr. (IF 10.3) Pub Date : 2024-03-28 Shoujun Jia, Chun Liu, Hangbin Wu, Zhijian Guo, Xuming Peng
The correspondence between BIM and construction instances is crucial to construction management. However, spatial deviation and geometric heterogeneity between BIM and construction point clouds pose great challenges. This paper establishes high-dimensional point cloud feature tensor to devise a point cloud semantic segmentation network and an incremental point-to-point correspondence estimation strategy
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Simulation of autonomous resource allocation through deep reinforcement learning-based portfolio-project integration Autom. Constr. (IF 10.3) Pub Date : 2024-03-28 Maryam Soleymani, Mahdi Bonyani, Chao Wang
Resource allocation has always been a critical challenge for construction project planning, and it affects the cost, duration, and quality of the projects. However, current methods mainly focus on a single project and lack integrated planning and optimization across a construction company's multiple projects. This paper describes a simulation of an Autonomous Resource Allocation (ARA) model using Deep
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Data-driven excavation trajectory planning for unmanned mining excavator Autom. Constr. (IF 10.3) Pub Date : 2024-03-27 Tianci Zhang, Tao Fu, Tao Ni, Haifeng Yue, Yongpeng Wang, Xueguan Song
In autonomous mining scenarios, excavation trajectory planning plays a significant role since it considerably influences the working performance of the unmanned mining excavator (UME). Aiming at the limited dynamical characterization of traditional theoretical methods that yield unsatisfactory performance in trajectory planning, herein we propose a data-driven excavation trajectory planning framework
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Vision-based real-time process monitoring and problem feedback for productivity-oriented analysis in off-site construction Autom. Constr. (IF 10.3) Pub Date : 2024-03-27 Xue Chen, Yiheng Wang, Jingwen Wang, Ahmed Bouferguene, Mohamed Al-Hussein
The widespread adoption of surveillance cameras in work environments has enabled the direct and non-intrusive detection of productivity-related issues in the field of construction. In this research, a process monitoring and problem feedback framework is developed based on closed-circuit television footage and computer vision analysis to achieve real-time visual control of the work process and facilitate
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An average pooling designed Transformer for robust crack segmentation Autom. Constr. (IF 10.3) Pub Date : 2024-03-27 Zhaohui Chen, Elyas Asadi Shamsabadi, Sheng Jiang, Luming Shen, Daniel Dias-da-Costa
Crack detection in civil infrastructures has seen impressive accuracy achieved by Convolutional Neural Networks (CNNs) and Transformers. However, practical deployments demand models that are not only highly accurate and robust but also efficient. This paper presents PoolingCrack, a novel and efficient Transformer-based model that leverages a hierarchical structure to capture local and global information
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Width estimation of hidden cracks in tunnel lining based on time-frequency analysis of GPR data and back propagation neural network optimized by genetic algorithm Autom. Constr. (IF 10.3) Pub Date : 2024-03-26 Lili Hou, Qian Zhang, Yanliang Du
The width and buried depth of hidden cracks in tunnel lining are important indicators for measuring the development degree of crack propagation, and evaluating the risk of lining block falling caused by the coexisting defects with voids and cracks. This paper proposes a high-precision fitting method for the width and buried depth of hidden cracks in lining that does not rely on wave velocity. Time
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Advancing railway track health monitoring: Integrating GPR, InSAR and machine learning for enhanced asset management Autom. Constr. (IF 10.3) Pub Date : 2024-03-25 Mehdi Koohmishi, Sakdirat Kaewunruen, Ling Chang, Yunlong Guo
Railway track health monitoring and maintenance are crucial stages in railway asset management, aiming to enhance the train operation quality and service life. For this aim, various inspection means (using diverse non-destructive testing techniques) have been applied, however, these means are mostly not able to monitor whole railway track network or track underlying layers (e.g., ballast and subgrade)
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From dense point clouds to semantic digital models: End-to-end AI-based automation procedure for Manhattan-world structures Autom. Constr. (IF 10.3) Pub Date : 2024-03-23 Mansour Mehranfar, Alexander Braun, André Borrmann
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Exploring three pillars of construction robotics via dual-track quantitative analysis Autom. Constr. (IF 10.3) Pub Date : 2024-03-23 Yuming Liu, Aidi Hizami Bin Alias, Nuzul Azam Haron, Nabilah Abu Bakar, Hao Wang
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Enhanced co-design and evaluation of a collective robotic construction system for the assembly of large-scale in-plane timber structures Autom. Constr. (IF 10.3) Pub Date : 2024-03-22 Samuel Leder, HyunGyu Kim, Metin Sitti, Achim Menges
Collective robotic construction (CRC) is an emerging approach to construction automation based on the collaboration among teams of small mobile robots. This paper enhances an existing modular CRC system, showcasing its capability to assemble full-scale in-plane timber structures. Utilizing strategies of co-design, the robotic actuators were updated to accommodate material tolerance in the passive building
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Ethics of artificial intelligence and robotics in the architecture, engineering, and construction industry Autom. Constr. (IF 10.3) Pub Date : 2024-03-22 Ci-Jyun Liang, Thai-Hoa Le, Youngjib Ham, Bharadwaj R.K. Mantha, Marvin H. Cheng, Jacob J. Lin
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Automated component delivery management under uncertainty for prefabricated buildings to minimize cost and harmful emissions Autom. Constr. (IF 10.3) Pub Date : 2024-03-21 Xuan Zhang, Xueqing Zhang
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Human behaviour simulation for promoting usefulness and user-centric values in parametric design Autom. Constr. (IF 10.3) Pub Date : 2024-03-21 Seung Wan Hong, Jin Lee, Jin Kook Lee
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Deep learning-based 3D digital damage map of vertical-type tunnels using unmanned fusion data scanning Autom. Constr. (IF 10.3) Pub Date : 2024-03-21 Keunyoung Jang, Sinzeon Park, Hyunjun Jung, Hoon Yoo, Yun-Kyu An
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Cellular network-based IIoT architecture for time-critical control tasks of building automation Autom. Constr. (IF 10.3) Pub Date : 2024-03-21 Xinyue Li, Shengwei Wang, Jiannong Cao
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Construction supply chain risk management Autom. Constr. (IF 10.3) Pub Date : 2024-03-20 Milad Baghalzadeh Shishehgarkhaneh, Robert C. Moehler, Yihai Fang, Hamed Aboutorab, Amer A. Hijazi
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Network for robust and high-accuracy pavement crack segmentation Autom. Constr. (IF 10.3) Pub Date : 2024-03-20 Yingchao Zhang, Cheng Liu
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Leveraging local neighborhood features in 3D unstructured point cloud data for geometry-based automated damage delineation Autom. Constr. (IF 10.3) Pub Date : 2024-03-18 Madhu Areti, Zohaib Hasnain
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Automated BIM generation for large-scale indoor complex environments based on deep learning Autom. Constr. (IF 10.3) Pub Date : 2024-03-18 Mostafa Mahmoud, Wu Chen, Yang Yang, Yaxin Li
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Digital twins in the built environment: Definition, applications, and challenges Autom. Constr. (IF 10.3) Pub Date : 2024-03-18 Wassim AlBalkhy, Dorra Karmaoui, Laure Ducoulombier, Zoubeir Lafhaj, Thomas Linner
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Automatic assembly of prefabricated components based on vision-guided robot Autom. Constr. (IF 10.3) Pub Date : 2024-03-16 Chenyu Liu, Jing Wu, Xinlang Jiang, Yunfan Gu, Luqi Xie, Zhengrong Huang
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Machine learning in construction and demolition waste management: Progress, challenges, and future directions Autom. Constr. (IF 10.3) Pub Date : 2024-03-16 Yu Gao, Jiayuan Wang, Xiaoxiao Xu
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Advances in formwork automation, structure and materials in concrete construction Autom. Constr. (IF 10.3) Pub Date : 2024-03-16 Peter Gappmaier, Sara Reichenbach, Benjamin Kromoser
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Computer vision-based real-time continuous monitoring of the pose for large-span bridge cable lifting structures Autom. Constr. (IF 10.3) Pub Date : 2024-03-15 Yao Tang, Bo Huang, Shaorui Wang, Jianting Zhou, Zhengsong Xiang, Chengchong Sheng, Chang He, Haizhu Wang, Lingyu Ruan
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Concrete forensic analysis using deep learning-based coarse aggregate segmentation Autom. Constr. (IF 10.3) Pub Date : 2024-03-15 Mati Ullah, Junaid Mir, Syed Sameed Husain, Muhammad Laiq Ur Rahman Shahid, Afaq Ahmad
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Optical see-through augmented reality fire safety training for building occupants Autom. Constr. (IF 10.3) Pub Date : 2024-03-15 Daniel Paes, Zhenan Feng, Maddy King, Hesam Khorrami Shad, Prasanth Sasikumar, Diego Pujoni, Ruggiero Lovreglio
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Multi-skilled resource-constrained multi-project scheduling problem with dexterity improvement of workforce Autom. Constr. (IF 10.3) Pub Date : 2024-03-15 Saleh Mozhdehi, Vahid Baradaran, Amir Hossein Hosseinian
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Omni-Scan2BIM: A ready-to-use Scan2BIM approach based on vision foundation models for MEP scenes Autom. Constr. (IF 10.3) Pub Date : 2024-03-14 Boyu Wang, Zhengyi Chen, Mingkai Li, Qian Wang, Chao Yin, Jack C.P. Cheng
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Residential floor plans: Multi-conditional automatic generation using diffusion models Autom. Constr. (IF 10.3) Pub Date : 2024-03-14 Pengyu Zeng, Wen Gao, Jun Yin, Pengjian Xu, Shuai Lu
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Deep neural networks for corporate misconduct prediction in construction industry using data from social networks Autom. Constr. (IF 10.3) Pub Date : 2024-03-14 Ran Wang, Yanyan Liu, Bin Xue, Bingsheng Liu, Junna Meng
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Enriching BIM models with fire safety equipment using keypoint-based symbol detection in escape plans Autom. Constr. (IF 10.3) Pub Date : 2024-03-13 Phillip Schönfelder, Angelina Aziz, Frédéric Bosché, Markus König
In the context of fire safety inspections, Building Information Modeling (BIM) models enriched with Fire Safety Equipment (FSE) components can be used to complete compliance checks and other analyses. However, BIM models often lack the required FSE information. To address this issue, escape plans are a convenient source of data, as they show the position and type of FSE on floor plans. Therefore, this
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3D object recognition using deep learning for automatically generating semantic BIM data Autom. Constr. (IF 10.3) Pub Date : 2024-03-13 Kay Rogage, Omar Doukari
The successful reuse of Building Information Model (BIM) data is reliant on the use of clearly defined objects. File formats such as the Industry Foundation Classes (IFC) along with classification systems offer approaches to standardising semantic BIM data. Inconsistent application of these standards during BIM authoring results in objects that are unable to be reused across systems. This research
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Comparison of pull-out capacities of robotically and manually drilled and set fasteners Autom. Constr. (IF 10.3) Pub Date : 2024-03-13 Maximilian Ortner, Michael Schwenn, Benjamin Kromoser
The performance of post-installed fasteners in concrete can be affected by the chosen drilling and setting techniques. In the context of the development of a drilling robot for construction site implementation the impact of an automated setting process on the pull-out loads of fastening systems was evaluated. This article investigates the effect of two different fixing systems, drop-in expansion and
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Multiple-type distress detection in asphalt concrete pavement using infrared thermography and deep learning Autom. Constr. (IF 10.3) Pub Date : 2024-03-13 Fangyu Liu, Jian Liu, Linbing Wang, Imad L. Al-Qadi
Artificial intelligence, particularly Convolutional Neural Network (CNN), has emerged as a highly effective methodology for detecting pavement distresses. This study aimed to apply infrared thermography (IRT) and deep learning to multiple-type distress detection. The dataset encompassed five image types (visible images, infrared images, and fusion images with varying infrared ratios) along with five
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Intelligent green retrofitting of existing buildings based on case-based reasoning and random forest Autom. Constr. (IF 10.3) Pub Date : 2024-03-12 Tianyi Liu, Guofeng Ma, Ding Wang, Xinming Pan
The decision-making on green retrofitting of existing buildings relies on both explicit and implicit knowledge, and its efficiency and reliability need improvement. Intelligent approaches that can sufficiently utilize the text information of existing projects are required to provide more suitable strategies for green retrofitting. This paper describes a decision-making approach combining Case-Based
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Computer-aided rotary crane stability assessment Autom. Constr. (IF 10.3) Pub Date : 2024-03-12 Paweł Kwiatoń, Dawid Cekus, Paweł Waryś
This paper introduces a computer-aided approach for evaluating the stability of rotary cranes. Utilizing the Motion add-in within the SolidWorks package, a comprehensive numerical stability analysis was carried out. The interaction dynamics between the crane’s support system and the ground were modeled employing linear springs, while the rope system was characterized by a blend of linear spring and
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Electric shovel trajectory tracking with inversion sliding mode based on Lyapunov functions Autom. Constr. (IF 10.3) Pub Date : 2024-03-12 Zeren Chen, Wei Guan, Jianbo Guo, Duomei Xue, Zhengbin Liu, Guoqiang Wang, Long Quan
The harsh actual working environment and heavy weight of the electric shovel pose a great challenge to its trajectory tracking performance. Taking into account the strong robustness and anti-interference performance of the sliding mode control method and the global stability of the inversion control method, a Lyapunov-based inversion sliding mode trajectory tracking method is proposed in this paper
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Nuclear containment damage detection and visualization positioning based on YOLOv5m-FFC Autom. Constr. (IF 10.3) Pub Date : 2024-03-12 Jian Yu, Yaming Xu, Cheng Xing, Jianguo Zhou, Pai Pan, Peng Yang
This paper designs an acquisition system that automatically captures images of the nuclear containment and proposes a new automatic defect detection technology based on the YOLOv5 model to detect the damage of the nuclear containment. The proposed model combines YOLOv5 and Focal NeXt block to improve the feature extraction ability of the model, adds attention mechanism to suppress the interference
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Review of simultaneous localization and mapping (SLAM) for construction robotics applications Autom. Constr. (IF 10.3) Pub Date : 2024-03-11 Andrew Yarovoi, Yong Kwon Cho
With the increasing affordability of robot technologies and the reduction in size and weight of autonomous systems, the deployment of autonomous robotic systems on construction sites has gained significant attention. One major challenge faced by these systems is the accurate mapping and localization within an environment that constantly evolves on a daily basis. This computational problem, known as
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Automated detection of railway defective fasteners based on YOLOv8-FAM and synthetic data using style transfer Autom. Constr. (IF 10.3) Pub Date : 2024-03-10 Shi Qiu, Benxin Cai, Weidong Wang, Jin Wang, Qasim Zaheer, Xianhua Liu, Wenbo Hu, Jun Peng
Fastener damage detection is an integral component of track safety inspections. The lack of balanced dataset caused by insufficient data on defective fasteners poses a significant challenge to the current development of robust fastener detection models. This study proposes the YOLOv8-FAM detection model algorithm, which combines the enhanced capabilities of YOLOv8, and generates realistic images of
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Deep learning-based structural health monitoring Autom. Constr. (IF 10.3) Pub Date : 2024-03-08 Young-Jin Cha, Rahmat Ali, John Lewis, Oral Büyükӧztürk
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Rutting measurement in asphalt pavements Autom. Constr. (IF 10.3) Pub Date : 2024-03-07 Ali Fares, Tarek Zayed, Sherif Abdelkhalek, Nour Faris, Muhammad Muddassir