Advances in Intelligent Construction, Operation and Maintenance, 2nd Edition

Topic Information

Dear Colleagues,

This topic is a continuation of the previous successful topic “Advances in Intelligent Construction, Operation and Maintenance” (https://www.mdpi.com/topics/intelligent_construction).

Intelligent construction, operation, and maintenance combines modern information technology, the life-cycle concept, and traditional engineering modes, which are the research frontiers in civil engineering. Specifically, intelligent construction, operation, and maintenance utilize information technology, such as digitalization, networks, intelligence, data, calculation power, and advanced algorithms, to realize the collaboration of project planning, design, implementation, operation, and maintenance through digital construction resources, standardized models, information interaction, visual recognition, high-performance computing, and intelligent decision-making. Intelligent construction, operation, and maintenance can maximize the value of the project, minimize the cost of the project, improve the industrial structure, enhance structural safety, reduce the investment in maintenance, and deliver green and sustainable intelligent engineering products and services to users. The full achievements of the above objectives and benefits need well-coordinated interdisciplinary research for the full adaptation of innovative technologies developed in other disciplines to applications in civil engineering. Therefore, intelligent construction, operation, and maintenance involve three major fields: building information model technology, internet of things technology, and artificial intelligence technology, including dozens of research topics such as information monitoring, data mining, structural analysis, automated construction, remote control, optimal operation, safety assessment, remote sensing technologies, and renewable materials. In the past several years, researchers throughout the world have carried out intensive research in this community and made remarkable progress. The purpose of this Special Issue is to attract the latest progress in intelligent construction, operation, and maintenance and to integrate scholars in various fields to discuss the current challenges in this community. The topics of interest for publication include, but are not limited to:

• Building information model technology; 
• Intelligent building technology; 
• Structural health monitoring technology; 
• Intelligent sensing technology; 
• Artificial intelligence technology; 
• Computer vision technology; 
• Structural retrofit technology; 
• Green and recyclable materials; 
• Remote sensing monitoring technologies.

Prof. Dr. Guangdong Zhou
Dr. Songhan Zhang
Dr. Jian Li
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Buildings buildings	3.1	3.4	2011	15.3 Days	CHF 2600	Submit
Eng eng	-	2.1	2020	21.5 Days	CHF 1200	Submit
Infrastructures infrastructures	2.7	5.2	2016	17.8 Days	CHF 1800	Submit
Remote Sensing remotesensing	4.2	8.3	2009	23.9 Days	CHF 2700	Submit
Sustainability sustainability	3.3	6.8	2009	19.7 Days	CHF 2400	Submit

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Published Papers (7 papers)

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All Buildings Eng Infrastructures Remote Sensing Sustainability

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26 pages, 7941 KiB  

Open AccessArticle

An Edge-Computing-Driven Approach for Augmented Detection of Construction Materials: An Example of Scaffold Component Counting

by Xianzhong Zhao, Bo Cheng, Yujie Lu and Zhaoqi Huang

Buildings 2025, 15(7), 1190; https://doi.org/10.3390/buildings15071190 - 5 Apr 2025

Viewed by 64

Abstract

Construction material management is crucial for project progression. Counting massive amounts of scaffold components is a key step for efficient material management. However, traditional counting methods are time-consuming and laborious. Utilizing a vision-based method with edge devices for counting these materials undoubtedly offers [...] Read more.

Construction material management is crucial for project progression. Counting massive amounts of scaffold components is a key step for efficient material management. However, traditional counting methods are time-consuming and laborious. Utilizing a vision-based method with edge devices for counting these materials undoubtedly offers a promising solution. This study proposed an edge-computing-driven approach for detecting and counting scaffold components. Two algorithm refinements of YOLOX, including generalized intersection over union (GIoU) and soft non-maximum suppression (Soft-NMS), were introduced to enhance detection accuracy in conditions of occlusion. An automated pruning method was proposed to compress the model, achieving a 60.2% reduction in computation and a 9.1% increase in inference speed. Two practical case studies demonstrated that the method, when deployed on edge devices, achieved 98.9% accuracy and reduced time consumption for counting tasks by 87.9% compared to the conventional method. This research provides an edge-computing-driven framework for counting massive materials, establishing a comprehensive workflow for intelligent applications in construction management. The paper concludes with limitations of the current study and suggestions for future work. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance, 2nd Edition)

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17 pages, 6351 KiB  

Open AccessArticle

Experimental Study on Direct Shear Behavior of New-to-Old Interface for Recycled Aggregate Concrete with Different Replacement Ratios

by Hua Lei, Chunlong Nie, Deyi Xu, Mengri Zeng and Yuliang Chen

Buildings 2024, 14(11), 3341; https://doi.org/10.3390/buildings14113341 - 22 Oct 2024

Viewed by 705

Abstract

The large amount of construction waste produced by the construction industry brings severe natural resource consumption and environmental pollution, elevating the awareness of sustainable development. Recycled aggregate concrete, crushed from construction waste, is recognized as an eco-friendly material and the current optimal solution [...] Read more.

The large amount of construction waste produced by the construction industry brings severe natural resource consumption and environmental pollution, elevating the awareness of sustainable development. Recycled aggregate concrete, crushed from construction waste, is recognized as an eco-friendly material and the current optimal solution for the environmental problem. The interface shear failure between substrate recycled coarse aggregate concrete (RAC) and overlay natural aggregate concrete becomes vital for structural safety. In order to study the direct shear performance, a total of 19 specimens were designed to carry out the direct shear test with different replacement ratios, interface types and curing ages. The whole process and failure mode were recorded. The relationships between the shear capacity and deformation, ductility, damage evolution, and energy dissipation were evaluated and discussed. The results demonstrate that the RAC component in the interface was the weak line in the shear plane, accelerating the damage evolution. In the early curing stage, the replacement ratio had a slight influence on the shear properties. Increasing the recycled aggregate replacement ratio from 0 to 100% improved the normalized strength ratio by 8.94% and 14.62%, respectively, for curing ages of 3 days and 7 days. A regression equation was proposed to predict the shear strength, accounting for the curing ages. With the increase in the replacement ratio, the ductility and energy dissipation gradually enhanced. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance, 2nd Edition)

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28 pages, 3291 KiB  

Open AccessReview

Digital Twins in Construction: Architecture, Applications, Trends and Challenges

by Zhou Yang, Chao Tang, Tongrui Zhang, Zhongjian Zhang and Dat Tien Doan

Buildings 2024, 14(9), 2616; https://doi.org/10.3390/buildings14092616 - 23 Aug 2024

Cited by 3 | Viewed by 3995

Abstract

The construction field currently suffers from low productivity, a lack of expertise among practitioners, weak innovation, and lack of predictability. The digital twin, an advanced digital technology, empowers the construction sector to advance towards intelligent construction and digital transformation. It ultimately aims for [...] Read more.

The construction field currently suffers from low productivity, a lack of expertise among practitioners, weak innovation, and lack of predictability. The digital twin, an advanced digital technology, empowers the construction sector to advance towards intelligent construction and digital transformation. It ultimately aims for highly accurate digital simulation to achieve comprehensive optimization of all phases of a construction project. Currently, the process of digital twin applications is facing challenges such as poor data quality, the inability to harmonize types that are difficult to integrate, and insufficient data security. Further research on the application of digital twins in the construction domain is still needed to accelerate the development of digital twins and promote their practical application. This paper analyzes the commonly used architectures for digital twins in the construction domain in the literature and summarizes the commonly used technologies to implement the architectures, including artificial intelligence, machine learning, data mining, cyber–physical systems, internet of things, virtual reality, augmented reality applications, and considers their advantages and limitations. The focus of this paper is centered on the application of digital twins in the entire lifecycle of a construction project, which includes the design, construction, operation, maintenance, demolition and restoration phases. Digital twins are mainly moving towards the integration of data and information, model automation, intelligent system control, and data security and privacy. Digital twins present data management and integration challenges, privacy and security protection, technical manpower development, and transformation needs. Future research should address these challenges by improving data quality, developing robust integration methodologies, and strengthening data security measures. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance, 2nd Edition)

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24 pages, 10959 KiB  

Open AccessArticle

Automated Concrete Bridge Deck Inspection Using Unmanned Aerial System (UAS)-Collected Data: A Machine Learning (ML) Approach

by Rojal Pokhrel, Reihaneh Samsami, Sayda Elmi and Colin N. Brooks

Eng 2024, 5(3), 1937-1960; https://doi.org/10.3390/eng5030103 - 15 Aug 2024

Cited by 1 | Viewed by 1498

Abstract

Bridges are crucial components of infrastructure networks that facilitate national connectivity and development. According to the National Bridge Inventory (NBI) and the Federal Highway Administration (FHWA), the cost to repair U.S. bridges was recently estimated at approximately USD 164 billion. Traditionally, bridge inspections [...] Read more.

Bridges are crucial components of infrastructure networks that facilitate national connectivity and development. According to the National Bridge Inventory (NBI) and the Federal Highway Administration (FHWA), the cost to repair U.S. bridges was recently estimated at approximately USD 164 billion. Traditionally, bridge inspections are performed manually, which poses several challenges in terms of safety, efficiency, and accessibility. To address these issues, this research study introduces a method using Unmanned Aerial Systems (UASs) to help automate the inspection process. This methodology employs UASs to capture visual images of a concrete bridge deck, which are then analyzed using advanced machine learning techniques of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to detect damage and delamination. A case study on the Beyer Road Concrete Bridge in Michigan is used to demonstrate the developed methodology. The findings demonstrate that the ViT model outperforms the CNN in detecting bridge deck damage, with an accuracy of 97%, compared to 92% for the CNN. Additionally, the ViT model showed a precision of 96% and a recall of 97%, while the CNN model achieved a precision of 93% and a recall of 61%. This technology not only enhances the maintenance of bridges but also significantly reduces the risks associated with traditional inspection methods. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance, 2nd Edition)

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14 pages, 5926 KiB  

Open AccessArticle

Simulation Test of an Intelligent Vibration System for Concrete under Reinforcing Steel Mesh

by Hongyu Liang, Zhigang Wu, Jifeng Hu, Yuannan Gan and Sheng Qiang

Buildings 2024, 14(8), 2277; https://doi.org/10.3390/buildings14082277 - 23 Jul 2024

Cited by 2 | Viewed by 991

Abstract

Concrete vibration construction sustains high labor intensity, a poor working environment, difficulties in quality control, and other problems. Current research on concrete vibration focuses on monitoring vibration quality, evaluating vibration processes quantitatively, and assessing mechanical vibration of unreinforced mesh concrete (plain concrete). Standardizing [...] Read more.

Concrete vibration construction sustains high labor intensity, a poor working environment, difficulties in quality control, and other problems. Current research on concrete vibration focuses on monitoring vibration quality, evaluating vibration processes quantitatively, and assessing mechanical vibration of unreinforced mesh concrete (plain concrete). Standardizing concrete vibration under reinforcing steel mesh remains difficult. There is still a lag in the evaluation of the quality of rework and the consumption of human and material resources. To tackle these issues, a vibrating robotic arm system based on automation control technology, machine vision, and kinematic modeling is proposed. Research and simulation tests on intelligent concrete vibration under reinforcing steel mesh aim to enhance construction efficiency and quality. A five-degree-of-freedom robotic arm with a vision module identifies each rebar grid center in the image, extracts the pixel coordinates, and converts them to the mechanical coordinates by the integration of machine vision algorithms. A vibrator point screening algorithm is introduced to determine actual vibrator point locations based on specific insertion spacing, alongside a vibro-module for vertical movement. Real-time assessment of vibration quality is achieved using the YOLOv5 target detection model. Simulation tests confirm the feasibility of automated concrete vibration control under reinforcing steel mesh by a vibrating robot arm system. This research offers a new approach for unmanned vibration technology in concrete under reinforcing steel mesh, supporting future related technological advancements with practical value. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance, 2nd Edition)

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18 pages, 5268 KiB  

Open AccessArticle

Research on Intelligent Prefabricated Reinforced Concrete Staircase Lifting Point Setting Method Considering Multidimensional Spatial Constraint Characteristics

by Yang Yang, Xiaodong Cai, Gang Yao, Meng Wang, Canwei Zhou, Ting Lei and Yating Zhang

Sustainability 2024, 16(14), 5843; https://doi.org/10.3390/su16145843 - 9 Jul 2024

Viewed by 1658

Abstract

Prefabricated reinforced concrete staircases (PC staircases) are prefabricated components that are widely used in prefabricated buildings and are used in large quantities. During the production and construction of a PC staircase, the lifting point setting directly affects the construction safety, construction efficiency, and [...] Read more.

Prefabricated reinforced concrete staircases (PC staircases) are prefabricated components that are widely used in prefabricated buildings and are used in large quantities. During the production and construction of a PC staircase, the lifting point setting directly affects the construction safety, construction efficiency, and construction quality. In this paper, we analyze the quality problems and safety risks in the design, production, and construction of PC staircases under the constraints of multidimensional spatial characteristics, clarify the key technical difficulties of prefabricated staircase lifting under the multidimensional spatial and temporal constraints, and analyze the factors that should be considered in the setting of lifting points. In this paper, a prefabricated staircase lifting point setting database is established and a thin-plate spline interpolation algorithm is introduced to expand it. Based on the support vector machine algorithm, the process of optimization is carried out for the kernel function scale parameter and penalty factor, and it is concluded that for every increase of two in the number of cross-validation folds, the percentage reduction in minimum RMSE is 9.4%, 17.8%, and 4.2%, respectively, the percentage increase in the optimization time is 39.7%, 61.8%, and 27.3%, respectively, and a PC staircase lifting point setup method based on the small-sample database is proposed. The number of lifting points and lifting point locations of the PC staircase satisfying the multidimensional spatial feature constraints can be obtained by inputting the five design parameters of the PC staircase, namely, the number of treads, the height of the treads, the width of the treads, the width of the staircase, and the weight of the staircase, into the lifting point setup method proposed in this paper. The reliability of the precast reinforced concrete staircase lifting point setting method proposed in this paper when considering the multidimensional spatial constraint characteristics is verified by the precast staircases in deep shafts for assembly construction at the Chongqing metro station. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance, 2nd Edition)

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21 pages, 3247 KiB  

Open AccessArticle

Quantitative Carbon Emission Prediction Model to Limit Embodied Carbon from Major Building Materials in Multi-Story Buildings

by Qimiao Xie, Qidi Jiang, Jarek Kurnitski, Jiahang Yang, Zihao Lin and Shiqi Ye

Sustainability 2024, 16(13), 5575; https://doi.org/10.3390/su16135575 - 29 Jun 2024

Cited by 2 | Viewed by 1525

Abstract

As the largest contributor of carbon emissions in China, the building sector currently relies mostly on enterprises’ own efforts to report carbon emissions, which usually results in challenges related to information transparency and workload for regulatory bodies, who play an otherwise vital role [...] Read more.

As the largest contributor of carbon emissions in China, the building sector currently relies mostly on enterprises’ own efforts to report carbon emissions, which usually results in challenges related to information transparency and workload for regulatory bodies, who play an otherwise vital role in controlling the building sector’s carbon footprint. In this study, we established a novel regulatory model known as QCEPM (Quantitative Carbon Emission Prediction Model) by conducting multiple linear regression analysis using the quantities of concrete, rebar, and masonry structures as independent variables and the embodied carbon emissions of a building as the dependent variable. We processed the data in the detailed quantity list of 20 multi-story frame structure buildings and fed them to the QCEPM for the solution. Comparison of the QCEPM-calculated results against the time-consuming and error-prone manual calculation results suggested a mean absolute percentage error (MAPE) of 2.36%. Using this simplified model, regulatory bodies can efficiently supervise the embodied carbon emissions in multi-story frame structures by setting up a carbon quota for a project in its approval stage, allowing the construction enterprise to carry out dynamic control over the three most important audited building materials throughout a project’s planning and implementation phase. Full article

(This article belongs to the Topic Advances in Intelligent Construction, Operation and Maintenance, 2nd Edition)

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