Special Issues
Table of Content

AI and Machine Learning Modeling in Civil and Building Engineering

Submission Deadline: 31 December 2022 (closed) View: 13

Guest Editors

Dr. Ke Yan, National University of Singapore, Singapore
Dr. Xiaokang Zhou, Shiga University, Japan
Dr. Haidong Shao, Hunan University, China

Summary

Artificial Intelligence (AI), machine learning (ML) and data analytics technologies are slowly changing the traditional ways of handling civil and building engineering problems. Examples of AI-empower civil and building engineering applications include autonomous vehicle/robots, smart building/city design, internet of things (IoT), smart facility management, smart building maintenance, etc., where the AI and ML techniques are involved in different stages of building life-cycle, including design, construction and maintenance stages.


Although automation and robotics have their great capability in civil and infrastructure constructions, there are still obstacles adopting modern AI and machine learning technologies for civil and building engineering due to the uncertainty and unclear performance for fully-automated construction and management. On one hand, it is well known that a well-implemented AI-enabled sensing system is a hidden key factor for a successful and efficient building project. On the other hand, it is difficult to address the automation issues completely from multiple perspectives. While a significant amount of works and research studies have already been undertaken to address various AI and automation issues in the current civil and building engineering stage, these concerns continue to change in response to new AI technology developments and clients’ demand trends.


This special issue intends to provide an international forum for researchers to exchange up-to-date outcomes on AI, ML and data analytics with their various applications to address the automation concerns in civil and building engineering. These three exciting research areas (AI, ML and data analytics) have attracted extensive research interests over the last decades, both from the AI methodology research community and the civil engineering research groups. With the emergence of novel methods and systems, recent progresses of these three areas remain to be investigated and studied. Therefore, a special issue is proposed to satisfy this requirement, which will have a great significance and profound impact on the societies, including machine learning enhanced civil engineering solutions in smart cities, automation and robotics solutions in the infrastructure management design, sensing and big data systems, computer and networks, human-computer interactions and so on.


The main purposes of this special issue target to:

(1) bring together the research efforts in multiple related disciplines, such as artificial intelligence, machine learning, big data, edge computing, smart building, smart city, civil engineering, infrastructure management, Internet of things, hybrid human-machine computing, and smart environment to find and develop new AI and machine learning technologies for the AI-empowered civil engineering; 

(2) address the real-world challenges of the automation issues behind the fast development of AI-empowered civil engineering, and propose machine learning enhanced solutions to enhance the existing automation and robotics technologies; 

(3) look for emerging topics of ML enhanced civil engineering, AI, data analytics in civil engineering, smart building and smart city design, which in consequence attract more people to participate and enjoy the culture of AI-empowered civil and building engineering.


In summary, gathering novel research works related to AI and ML is the main purpose of this special issue. We encourage the submission of papers with new results, methods, applications and solutions in multiple related disciplines, such as edge computing, Internet of things, big data analytics, artificial intelligence, hybrid human-machine computing, infrastructure management design, sensing and big data systems, computer and networks, etc.


In addition to regular submissions for this special issue, authors of a few selected best papers from the conferences, including the 7th IEEE Cyber Science and Technology Congress (CyberSciTech 2022), Sept. 12-15, 2022, in Calabria, Italy, http://cyber-science.org/2022/cyberscitech/, and the 20th IEEE International Conference on Smart City (SmartCity 2022), date and venue to be determined, which are the high-quality international conferences related to computer modeling and smart city. Organizing members of CyberSciTech 2022 and SmartCity 2022, including a few senior famous members world-wide, will help promotion of this special issue, and selected members will be invited to join the review process.


Keywords

Sensing data for infrastructures;
Information modeling for smart city;
Automatic/robotic device;
Human-computer interactions in civil engineering;
AI in construction management technologies;
AI in service & business applications;
AI in entrepreneurship;
Architecture, planning and Engineering;
Recent developments of Internet of things (IoTs) technology;
Big data analysis for building and facility management;
Artificial intelligence methods in building operation and maintenance;
Smart and sustainable cities;
AI technologies for the smart environment;
AI in infrastructure and civil constructions.

Published Papers


  • Open Access

    ARTICLE

    Tool Wear State Recognition with Deep Transfer Learning Based on Spindle Vibration for Milling Process

    Qixin Lan, Binqiang Chen, Bin Yao, Wangpeng He
    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2825-2844, 2024, DOI:10.32604/cmes.2023.030378
    (This article belongs to the Special Issue: AI and Machine Learning Modeling in Civil and Building Engineering)
    Abstract The wear of metal cutting tools will progressively rise as the cutting time goes on. Wearing heavily on the tool will generate significant noise and vibration, negatively impacting the accuracy of the forming and the surface integrity of the workpiece. Hence, during the cutting process, it is imperative to continually monitor the tool wear state and promptly replace any heavily worn tools to guarantee the quality of the cutting. The conventional tool wear monitoring models, which are based on machine learning, are specifically built for the intended cutting conditions. However, these models require retraining when… More >

  • Open Access

    ARTICLE

    Investigation of the Severity of Modular Construction Adoption Barriers with Large-Scale Group Decision Making in an Organization from Internal and External Stakeholder Perspectives

    Muzi Li
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2465-2493, 2023, DOI:10.32604/cmes.2023.026827
    (This article belongs to the Special Issue: AI and Machine Learning Modeling in Civil and Building Engineering)
    Abstract Modular construction as an innovative method aids the construction industry in transforming to off-site construction production with high efficiency and environmental friendliness. Despite the obvious advantages, the uptake of modular construction is not booming as expected. However, previous studies have investigated and summarized the barriers to the adoption of modular construction. In this research, a Large-Scale Group Decision Making (LSGDM)- based analysis is first made of the severity of barriers to modular construction adoption from the perspective of construction stakeholders. In addition, the Technology-Organization-Environment (TOE) framework is utilized to identify the barriers based on three More >

  • Open Access

    ARTICLE

    Sparsity-Enhanced Model-Based Method for Intelligent Fault Detection of Mechanical Transmission Chain in Electrical Vehicle

    Wangpeng He, Yue Zhou, Xiaoya Guo, Deshun Hu, Junjie Ye
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2495-2511, 2023, DOI:10.32604/cmes.2023.027896
    (This article belongs to the Special Issue: AI and Machine Learning Modeling in Civil and Building Engineering)
    Abstract In today’s world, smart electric vehicles are deeply integrated with smart energy, smart transportation and smart cities. In electric vehicles (EVs), owing to the harsh working conditions, mechanical parts are prone to fatigue damages, which endanger the driving safety of EVs. The practice has proved that the identification of periodic impact characteristics (PICs) can effectively indicate mechanical faults. This paper proposes a novel model-based approach for intelligent fault diagnosis of mechanical transmission train in EVs. The essential idea of this approach lies in the fusion of statistical information and model information from a dynamic process.… More >

  • Open Access

    ARTICLE

    Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with Incomplete Data for Smart Grid in Smart Cities

    Zeyu Wu, Bo Sun, Qiang Feng, Zili Wang, Junlin Pan
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 527-554, 2023, DOI:10.32604/cmes.2023.027124
    (This article belongs to the Special Issue: AI and Machine Learning Modeling in Civil and Building Engineering)
    Abstract Due to the high inherent uncertainty of renewable energy, probabilistic day-ahead wind power forecasting is crucial for modeling and controlling the uncertainty of renewable energy smart grids in smart cities. However, the accuracy and reliability of high-resolution day-ahead wind power forecasting are constrained by unreliable local weather prediction and incomplete power generation data. This article proposes a physics-informed artificial intelligence (AI) surrogates method to augment the incomplete dataset and quantify its uncertainty to improve wind power forecasting performance. The incomplete dataset, built with numerical weather prediction data, historical wind power generation, and weather factors data,… More >

    Graphic Abstract

    Physics-Informed AI Surrogates for Day-Ahead Wind Power Probabilistic Forecasting with Incomplete Data for Smart Grid in Smart Cities

  • Open Access

    ARTICLE

    ISHD: Intelligent Standing Human Detection of Video Surveillance for the Smart Examination Environment

    Wu Song, Yayuan Tang, Wenxue Tan, Sheng Ren
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 509-526, 2023, DOI:10.32604/cmes.2023.026933
    (This article belongs to the Special Issue: AI and Machine Learning Modeling in Civil and Building Engineering)
    Abstract In the environment of smart examination rooms, it is important to quickly and accurately detect abnormal behavior (human standing) for the construction of a smart campus. Based on deep learning, we propose an intelligent standing human detection (ISHD) method based on an improved single shot multibox detector to detect the target of standing human posture in the scene frame of exam room video surveillance at a specific examination stage. ISHD combines the MobileNet network in a single shot multibox detector network, improves the posture feature extractor of a standing person, merges prior knowledge, and introduces More >

  • Open Access

    ARTICLE

    A Shifting Strategy for Electric Commercial Vehicles Considering Mass and Gradient Estimation

    Weiguang Zheng, Junzhu Zhang, Shanchao Wang, Gaoshan Feng, Xiaohong Xu, Qiuxiang Ma
    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 489-508, 2023, DOI:10.32604/cmes.2023.025169
    (This article belongs to the Special Issue: AI and Machine Learning Modeling in Civil and Building Engineering)
    Abstract The extended Kalman filter (EKF) algorithm and acceleration sensor measurements were used to identify vehicle mass and road gradient in the work. Four different states of fixed mass, variable mass, fixed slope and variable slope were set to simulate real-time working conditions, respectively. A comprehensive electric commercial vehicle shifting strategy was formulated according to the identification results. The co-simulation results showed that, compared with the recursive least square (RLS) algorithm, the proposed algorithm could identify the real-time vehicle mass and road gradient quickly and accurately. The comprehensive shifting strategy formulated had the following advantages, e.g., More >

    Graphic Abstract

    A Shifting Strategy for Electric Commercial Vehicles Considering Mass and Gradient Estimation

  • Open Access

    ARTICLE

    A Novel Motor Fault Diagnosis Method Based on Generative Adversarial Learning with Distribution Fusion of Discrete Working Conditions

    Qixin Lan, Binqiang Chen, Bin Yao
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 2017-2037, 2023, DOI:10.32604/cmes.2023.025307
    (This article belongs to the Special Issue: AI and Machine Learning Modeling in Civil and Building Engineering)
    Abstract Many kinds of electrical equipment are used in civil and building engineering. The motor is one of the main power components of this electrical equipment, which can provide stable power output. During the long-term use of motors, various motor faults may occur, which affects the normal use of electrical equipment and even causes accidents. It is significant to apply fault diagnosis for the motors at the construction site. Aiming at the problem that signal data of faulty motor lack diversity, this research designs a multi-layer perceptron Wasserstein generative adversarial network, which is used to enhance… More >

  • Open Access

    ARTICLE

    Lightweight Design of Commercial Vehicle Cab Based on Fatigue Durability

    Donghai Li, Jiawei Tian, Shengwen Shi, Shanchao Wang, Jucai Deng, Shuilong He
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 421-445, 2023, DOI:10.32604/cmes.2023.024133
    (This article belongs to the Special Issue: AI and Machine Learning Modeling in Civil and Building Engineering)
    Abstract To better improve the lightweight and fatigue durability performance of the tractor cab, a multi-objective lightweight design of the cab was carried out in this study. First, the finite element model of the cab with counterweight loading was established and then confirmed by the physical testing, and use the inertial relief method to obtain stress distribution under unit load. The cab-frame rigid-flexible coupling multi-body dynamics model was built by Adams/car software. Taking the cab airbag mount displacement and acceleration signals acquired on the proving ground as the desired signals and obtaining the fatigue analysis load… More >

    Graphic Abstract

    Lightweight Design of Commercial Vehicle Cab Based on Fatigue Durability

  • Open Access

    ARTICLE

    A Convolutional Autoencoder Based Fault Detection Method for Metro Railway Turnout

    Chen Chen, Xingqiu Li, Kai Huang, Zhongwei Xu, Meng Mei
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 471-485, 2023, DOI:10.32604/cmes.2023.024033
    (This article belongs to the Special Issue: AI and Machine Learning Modeling in Civil and Building Engineering)
    Abstract Railway turnout is one of the critical equipment of Switch & Crossing (S&C) Systems in railway, related to the train’s safety and operation efficiency. With the advancement of intelligent sensors, data-driven fault detection technology for railway turnout has become an important research topic. However, little research in the literature has investigated the capability of data-driven fault detection technology for metro railway turnout. This paper presents a convolutional autoencoder-based fault detection method for the metro railway turnout considering human field inspection scenarios. First, the one-dimensional original time-series signal is converted into a two-dimensional image by data More >

  • Open Access

    ARTICLE

    Structural Damage Identification System Suitable for Old Arch Bridge in Rural Regions: Random Forest Approach

    Yu Zhang, Zhihua Xiong, Zhuoxi Liang, Jiachen She, Chicheng Ma
    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 447-469, 2023, DOI:10.32604/cmes.2023.022699
    (This article belongs to the Special Issue: AI and Machine Learning Modeling in Civil and Building Engineering)
    Abstract A huge number of old arch bridges located in rural regions are at the peak of maintenance. The health monitoring technology of the long-span bridge is hardly applicable to the small-span bridge, owing to the absence of technical resources and sufficient funds in rural regions. There is an urgent need for an economical, fast, and accurate damage identification solution. The authors proposed a damage identification system of an old arch bridge implemented with a machine learning algorithm, which took the vehicle-induced response as the excitation. A damage index was defined based on wavelet packet theory,… More >

Share Link