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Identifition of the Status of Safety Facilities Based on Computer Vision technology

Journal of the Chinese Institute of civil and Hydraulic engineering 2022年第2期34卷 109-120页

作者： Yu, Wen-Der Chang, Hsien-Kuan Tsai, Wan-Ying Department of Civil and Construction Engineering Chaoyang University of Technology Taichung413310 Taiwan

The construction managers tend to overlook the safety issue due to the dynamic and open site environment and the tight schedule. Despite the tremendous efforts spent by the Government labor safety agencies;the construction accidents are hardly eliminated completely. Falls from height due to temporary construction openness has been the most common accidents on site. As a result, this research aims at developing a fall prevention method by integrating a deep learning neural networks-based computer visualization technique and a specially designed tag to identify the openness status of fall protection facilities on site. The Faster Region based Convolutional Neural Networks (Faster R-CNN) is adopted and a specially designed Star-shape tag is selected for implementation of the proposed method. Such a method can determine the openness status of the safety facility images obtained from a regular Closed-Circuit Television (CCTV) camera set up on site. After tuning with various network parameters, the field-testing results show that the proposed method can achieve False Positive Rate with 2.1% ~ 8.4% for three types of safety facility openings, i.e., elevator shaft openness, floor openness, and safety fence openness. Such an outstanding performance implies profound potentials for practical application in monitoring the status of safety protection facilities on construction sites. © 2022, Chinese Institute of civil and Hydraulic engineering. All right reserved.

关键词： Accident prevention

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Predictability performance enhancement for suspended sediment in rivers: Inspection of newly developed hybrid adaptive neuro-fuzzy system model

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International Journal of Sediment Research 2022年第3期37卷 383-398页

作者： Rana Muhammad Adnan Zaher Mundher Yaseen Salim Heddam Shamsuddin Shahid Aboalghasem Sadeghi-Niaraki Ozgur Kisi State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering Hohai University Department of Urban Planning Engineering Networks and Systems Institute of Architecture and Construction South Ural State University New Era and Development in Civil Engineering Research Group Scientific Research Center Al-Ayen University Faculty of Science Agronomy Department Hydraulics Division University School of Civil Engineering Faculty of Engineering Universiti Teknologi Malaysia (UTM) Geoinformation Tech.Center of Excellence Faculty of Geomatics Engineering K.N.Toosi University of Technology Department of Computer Science and Engineering Sejong University Civil Engineering Department Ilia State University

Reliable modeling of river sediments transport is important as it is a defining factor of the economic viability of dams, the durability of hydroelectric-equipment, river susceptibility to pollution, suitability for navigation, and potential for aesthetics and fish habitat. The capability of a new machine learning model, fuzzy c-means based neuro-fuzzy system calibrated using the hybrid particle swarm optimization-gravitational search algorithm（ANFIS-FCM-PSOGSA） in improving the estimation accuracy of river suspended sediment loads（SSLs） is investigated in the current study. The outcomes of the proposed method were compared with those obtained using the fuzzy c-means based neuro-fuzzy system calibrated using particle swarm optimization（ANFIS-FCM-PSO）, ANFIS-FCM, and sediment rating curve（SRC） models. Various input combinations involving lagged river flow（Q） and suspended sediment（S） values were used for model development. The effect of Q and S on the model's accuracy also was assessed by including the difference between lagged Q and S values as inputs. The model performance was assessed using the root mean square error（RMSE）, mean absolute error（MAE）, Nash-Sutcliffe Efficiency（NSE）, and coefficient of determination（R2） and several graphical comparison methods. The results showed that the proposed model enhanced the prediction performance of the ANFIS-FCM-PSO（or ANFIS-FCM） models by 8.14%（1.72%）, 14.7%（5.71%）, 12.5%（2.27%）, and 25.6%（1.86%）,in terms of the RMSE, MAE, NSE and R2, respectively. The current study established the potential of the proposed ANFIS-FCM-PSOGSA model for simulation of the cumulative sediment load. The modeling results revealed the potential effects of the river flow lags on the sediment transport quantification.

关键词： Suspended sediment load Adaptive neuro-fuzzy system Particle swarm optimization Gravitational search algorithm

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Detection and modeling of streamflow variations in the Abay (upper Blue Nile) basin, Ethiopia

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Water Supply 2025年第5期25卷 933-949页

作者： Kassa, Dagne Almaw Ho, Chia-Chun Belete, Mulugeta Azeze Abate, Yeshigeta Nurie Department of Civil and Construction Engineering National Taiwan University of Science and Technology Taipei106335 Taiwan Faculty of Civil and Water Resources Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar6000 Ethiopia Department of Hydraulic and Water Resource Engineering University of Gondar Gondar196 Ethiopia

Streamflow studies support water resource management and flood/drought analysis, yet single-station or single-method approaches are often inadequate. This study analyzed trends and shifts in mean annual, annual maximum, and 7-day average minimum streamflow at 29 stations in the Abay Basin, using Mann–Kendall and Pettit tests. The Kessie watershed, showing significant change, was modeled using the HBV-Light model. Twelve years (2002–2013) were used for calibration and validation, including a 1-year warm-up. Simulations under 2002 and 2013 land use and land cover (LULC) conditions yielded average streamflows of 384.57 and 397.05 mm/year, respectively, compared to an observed average of 315.16 mm/year. LULC changes led to a 141.69 mm/year streamflow increase. These findings provide valuable insight for water management planning. © 2025 The Authors.

关键词： Water management

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Optimizing 3D-Printed Concrete Mixtures for Extraterrestrial Habitats: A Machine Learning Framework 19

Optimizing 3D-Printed Concrete Mixtures for Extraterrestrial...

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19th Biennial International Conference on engineering, Science, construction, and Operations in Challenging Environments, Earth and Space 2024

作者： Hoang, Pham Duy Moon, Hyosoo Ahn, Yonghan Center for AI Technology in Construction Hanyang Univ. ERICA Ansan Korea Republic of Dept. of Civil Architectural and Environmental Engineering North Carolina A&T State Univ United States Dept. of Architectural Engineering Hanyang Univ. ERICA Ansan Korea Republic of

ISBN: (数字)9780784485736

ISBN: (纸本)9780784485736

The utilization of 3D printing technology for the construction of habitats and infrastructure on celestial bodies such as the Moon and Mars presents an increasingly fascinating prospect in space construction research. The success of 3D printing constructions heavily depends on the rheological and mechanical properties of 3D-printed concrete influenced by several factors such as nozzle speed, interlayer interval time, and environmental conditions. However, existing studies have not addressed the challenge of real-time optimization of concrete mixtures under constantly changing conditions, including temperature, humidity, pressure, and gravity on other planets. Potentially, machine learning (ML) offers advantages in terms of data-driven optimization, flexibility, cost, and time savings and handling complex relationships on optimization tasks. This study aims to identify and propose a framework for applying ML in real-time optimization of concrete mixture and printing parameters. The framework involves identifying influential factors to optimizing a 3D-printed concrete mix and printing parameters, thereby developing an optimization framework specifically tailored to the extraterrestrial environment. The study result explored the feasibility of using machine learning techniques for real-time optimizing concrete mixtures in 3D printing construction on celestial bodies. The expected contribution of the study lies in introducing data-driven optimization techniques that provide adaptability to changing environmental conditions, improving the efficiency and reliability of construction in space. © ASCE.

关键词： Concrete mixers

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The PSO optimisation SVM prediction model for the asphalt pavement environment and service fatigue life

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International Journal of Information and Communication technology 2022年第4期20卷 355-366页

作者： Sun, Yu He, Dongpo Li, Jun Road and Railway Engineering Civil Engineering College Northeast Forestry University No. 26 Wo Hing Road Xiangfang District Heilongjiang Province Harbin City150040 China Department of Municipal and the Environmental Engineering Heilongjiang Institute of Construction Technology College Road Limin Development Zone Harbin150000 China

In order to improve the accuracy of prediction by support vector machine (SVM), parameter optimisation of SVM is an important part of asphalt pavement life prediction. In this paper, a particle swarm optimisation support vector machine (PSO_SVM) method was proposed to predict the fatigue life of SBS modified asphalt mixture. This method combines SVM with particle swarm optimisation (PSO), makes full use of SVM's unique advantages in dealing with small sample regression problems and PSO global search optimisation, improves convergence speed, and achieves depth and breadth optimisation. Experimental results show that this method improves the parameter selection efficiency of SVM, and the prediction results are more accurate than those of ANN and SVM. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Support vector machines

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Application of Smart construction Site in construction Practice 7th

Application of Smart Construction Site in Construction Pract...

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7th EAI International Conference on Management of Manufacturing Systems, MMS 2022

作者： Smetanková, Jana Krajníková, Katarína Ručinský, Rastislav Mésároš, Peter Behúnová, Annamária National Infrastructure for Technology Transfer Support in Slovakia II – NITT SK II Bratislava Slovakia Technical University of Kosice Faculty of Civil Engineering Expert’s Institute in Construction Kosice Slovakia Technical University of Kosice Faculty of Civil Engineering Institute of Construction Technology Economics and Management Kosice Slovakia Technical University of Kosice Faculty of Mining Ecology Process Control and Geotechnologies Institute of Earth Resources Kosice Slovakia

ISBN: (纸本)9783031227189

Recent studies show that intelligence planning and construction management, so the use of intelligence technologies and tools for interconnection, communication, and real-time interaction, are promising objects of research for many researchers and experts in the field of construction, architecture, and construction. By using intelligent tools, it is possible to contribute to increasing efficiency and effectiveness, high-quality management, and comprehensive health, safety, and environmental management. Based on these facts, the paper analyzes the current state of use of smart construction site tools in the Slovak construction industry. The research analyzed of current state of knowledge and use of smart tools in construction sector industry. The research examined the following aspects in detail: the degree of implementation, the main obstacles and benefits of implementing the principles of smart construction site, degree of use of tools ensuring protection of property, health and safety on site and the rate of use of construction robots and use of applications for the management and application of intelligence tools in the processes of planning and management of construction production. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Environmental management

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Automatic settlement assessment of urban road from 3D terrestrial laser scan data

Journal of Infrastructure Intelligence and Resilience

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Journal of Infrastructure Intelligence and Resilience 2025年第1期4卷

作者： Zhang, Xinchen Wang, Qian Fang, Hai Ying, Guogang College of Civil Engineering Nanjing Tech University Nanjing211816 China Department of Construction and Real Estate School of Civil Engineering Southeast University Nanjing211189 China School of Civil Engineering Ningbotech University Ningbo315104 China Ningbo Langda Technology Co. Ltd Ningbo315000 China Nanjing Gongda Construction Technology Co. Ltd Nanjing211800 China

Tunnel construction in urban environments often requires passing beneath existing roads, where excessive soil excavation can lead to road cracking, settlement, or heaving, posing risks to road safety. Traditional road settlement monitoring methods rely on manual measurements, which are time-consuming, labor-intensive, and costly. Some existing approaches also require extensive sensor deployment, complicating installation and maintenance. To address these challenges, this study introduces a LiDAR-based method for efficient and accurate road settlement assessment. The impact of various LiDAR measurement parameters on assessment accuracy and efficiency was analyzed under typical urban road conditions. A comprehensive workflow was developed, incorporating both rough and fine alignment processes. Key steps in the workflow, such as automated identification of matching planes between point clouds, directional alignment, and angle fine-tuning, were automated using advanced algorithms. The proposed method was applied and validated in a region undergoing tunneling works in Singapore. Results demonstrated that the partially automated LiDAR-based approach achieved comparable accuracy to manual point cloud alignment methods while significantly improving efficiency and reducing labor costs. Furthermore, when compared to traditional total station methods, the LiDAR-based technique maintained errors within acceptable limits and enabled broader spatial coverage. Overall, this study highlights the feasibility and potential of LiDAR technology to enhance road settlement monitoring in engineering practice, offering a cost-effective and scalable alternative to traditional methods. © 2025 The Authors

关键词： Tunneling (excavation)

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Neural radiance fields (nerf) for multi-scale 3d modeling of cultural heritage artifacts 10

Neural radiance fields (nerf) for multi-scale 3d modeling of...

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10th International Workshop 3D-ARCH "3D Virtual Reconstruction and Visualization of Complex Architectures"

作者： Croce, V. Forleo, G. Billi, D. Bevilacqua, M.G. Piemonte, A. Caroti, G. LISPEN EA 7515 Arts et Métiers Institute of Technology Aix-en-Provence13100 France Dept. of Energy Systems Territory and Construction Engineering University of Pisa Pisa56122 Italy Dept. of Civil and Industrial Engineering University of Pisa Pisa56122 Italy

This research aims to assess the adaptability of Neural Radiance Fields (NeRF) for the digital documentation of cultural heritage objects of varying size and complexity. We discuss the influence of object size, desired scale of representation, and level of detail on the choice to use NeRF for cultural heritage documentation, providing insights for practitioners in the field. Case studies range from historic pavements to architectural elements or buildings, representing diverse and multi-scale scenarios encountered in heritage documentation procedures. The findings suggest that NeRFs perform well in scenarios with homogeneous textures, variable lighting conditions, reflective surfaces, and fine details. However, they exhibit higher noise and lower texture quality compared to other consolidated image-based techniques as photogrammetry, especially in case of small-scale artifacts. © 2024 International Society for Photogrammetry and Remote Sensing. All rights reserved.

关键词： 3D modeling

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A Socio-Technological Framework for Optimizing Water Utility Strategies and Resilience to Pandemic Changes and Contamination Events

A Socio-Technological Framework for Optimizing Water Utility...

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World Environmental and Water Resources Congress 2022: Adaptive Planning and Design in an Age of Risk and Uncertainty

作者： Kadinski, Leonid Vizanko, Brent Berglund, Emily Ostfeld, Avi Faculty of Civil and Environmental Engineering Technion-Israel Institute of Technology Haifa Israel Dept. of Civil Construction and Environmental Engineering North Carolina State Univ. RaleighNC United States

ISBN: (数字)9780784484258

ISBN: (纸本)9780784484258

Water distribution systems are critical infrastructure that deliver high quality drinking water to its consumers. Contamination events in water distribution systems (WDS) are emergencies that can cause distress in the population and require quick response from the responsible utility manager. While regular water quality parameters are monitored at water treatment facilities, it is still a challenge to monitor water quality in the WDS itself. Various models have been developed to explore the reactions and interactions of relevant stakeholders during a contamination event including agent-based modelling. Furthermore, recent research has shown that water demands have significantly changed during the COVID-19 pandemic, and these changes can affect the operation and management of water infrastructure. In this study, an agent-based modelling framework is developed to explore social dynamics and reactions of water consumers and a utility manager during a contamination event, while considering a pandemic demand scenario. Furthermore, innovative response and recovery methods to a contamination event are explored for rehabilitating the water network after a water quality deterioration. Graph theory algorithms are used to place mobile sensor equipment for surveying the water quality in specific network parts, and the distribution system is clustered by the status of endangerment. The Bayesian Belief Network (BBN) was developed using survey data around risk perceptions and social distancing behaviour that were collected during the COVID-19 pandemic. The agent-based model (ABM) was developed using output from the BBN and water use data that were collected during the COVID-19 pandemic. The ABM is coupled with hydraulic simulation of the water infrastructure to evaluate changes in hydraulic performance. The model can be used to explore long and short-term consequences of the pandemic on water distribution systems' management, design, and operations;develop and optimize strategies

关键词： Water distribution systems

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A Step Towards Decarbonization of Building Sector

A Step Towards Decarbonization of Building Sector

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International Conference on Advancing Sustainable Futures, ICASF 2023

作者： Lanjewar, Bhagyashri A. Wankhede, Manoj Kewalramani, Manish Sawhney, Anil Ralegaonkar, Rahul V. Civil Engineering Department Visvesvaraya National Institute of Technology Nagpur440010 India Civil Engineering Department College of Engineering Abu Dhabi University Abu Dhabi United Arab Emirates Construction Sector Lead RICS New York United States Chair ICMS Standard Setting Committee New York United States

ISBN: (纸本)9783031909627

Globally, the significance of embodied carbon (EC) and greenhouse gases (GHG) has been acknowledged due to the increased risk of catastrophic climate change. The temperature rise had already reached 1 ℃ in 2017 due to the rising atmospheric CO2 concentration, and it is predicted to increase by 1.5 ℃ in the coming decades. In order to reduce greenhouse gas (GHG) emissions, the Paris Agreement on Climate Change asks for immediate transformational actions. The built environment sector produces around 40% of energy-related global greenhouse gas (GHG) emissions. To meet the global agenda of reducing GHG emissions, decarbonization of the building and construction sector is critical. This study aims to present the key concepts needed for the reduction of CO2 in moving towards net zero carbon emissions. Despite the widespread use of the term net zero carbon emissions, the process of achieving it remains unclear. This study seeks to describe the factors and strategies that should be considered to help the process. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Carbon emissions

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