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Artificial Intelligence for Sustainable Construction and Infrastructure Management
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Artificial Intelligence for Sustainable Construction and Infrastructure Management

A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Construction Management, and Computers & Digitization".

Deadline for manuscript submissions: closed (10 May 2023) | Viewed by 44848

Special Issue Editors

Dr. Yuan Chen

E-Mail Website
Guest Editor
College of Management and Economics, Tianjin University, Tianjin, China
Interests: artificial intelligence; infrastructure management; sustainable construction management
Dr. Hexu Liu

E-Mail Website
Guest Editor
Department of Civil and Construction Engineering, Western Michigan University, Kalamazoo, MI, USA
Interests: off-site construction; Building Information Modelling (BIM); BIM-GIS asset management; human-centered AR/VR/AI application
Special Issues, Collections and Topics in MDPI journals
Dr. Xianfei Yin

E-Mail Website
Guest Editor
Department of Construction Management and Engineering, University of Twente, Enschede, The Netherlands
Interests: construction information technology; infrastructure management; machine learning
Dr. Bo Xiao

E-Mail Website
Guest Editor
Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong
Interests: automation in construction; construction informatics; image processing
Dr. Yinghua Shen

E-Mail Website
Guest Editor
School of Economics and Business Administration, Chongqing University, Chongqing 400030, China
Interests: data-driven construction management; human-centric construction management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The construction industry, as one of the largest sectors, can not only drive long-term national development and create substantial job opportunities, but it also needs to consume a considerable amount of resources during an entire project life cycle, e.g., design, construction, and maintenance, with consequent adverse impacts on socioeconomic and environmental conditions. In the context of climate change and population growth, sustainability is of particular importance and interest with respect to the construction industry. Hence, it makes the most sense to research sustainable construction and infrastructure management in order to realize the purpose of sustainability.

Artificial intelligence (AI) can act as a backbone that provides a new and more efficient way for facilitating sustainable construction and infrastructure management. Therefore, this Special Issue intends to encourage researchers and practitioners to implement AI in construction and infrastructure management in order to seize the valuable opportunity of digital evolution for improved project performance and sustainability. Research papers related to AI for sustainable construction and infrastructure management are welcomed, including but not limited to knowledge representation and reasoning, computer vision, machine learning, deep learning, natural language processing, intelligent optimization, information fusion, and process mining.

We look forward to receiving your submissions.

Dr. Yuan Chen
Dr. Hexu Liu
Dr. Xianfei Yin
Dr. Bo Xiao
Dr. Yinghua Shen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • automation in construction
  • construction management
  • construction informatics
  • machine learning
  • sustainability

Published Papers (16 papers)

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16 pages, 1369 KiB  
Article
Investigation of the Critical Factors Influencing Multi-Stakeholders’ Participation in Design Optimization of EPC Projects
by Yuan Chen, Zichen Ren, Bingyue Hu and Hemin Zheng
Buildings 2023, 13(7), 1654; https://doi.org/10.3390/buildings13071654 - 28 Jun 2023
Cited by 1 | Viewed by 1079
Abstract
Design optimization can influence the achievement of management goals and the sustainable development of EPC (engineering–procurement–construction) projects. Current research regarding engineering design optimization mainly focuses on the technology aspect, while lacking extensive attention regarding the factors influencing stakeholders’ participation in design optimization of [...] Read more.
Design optimization can influence the achievement of management goals and the sustainable development of EPC (engineering–procurement–construction) projects. Current research regarding engineering design optimization mainly focuses on the technology aspect, while lacking extensive attention regarding the factors influencing stakeholders’ participation in design optimization of EPC projects. Based on the existing literature and expert opinions, this study identifies 33 critical influencing factors and adopts the DEMATEL (decision-making trial and evaluation laboratory) and ISM (interpretive structural model) method to analyze the hierarchical structure and interrelationships among these factors. The results show that the factors, including subcontractors’ participation during the design, design management level, performance evaluation mechanism, technological development, owners’ attitude towards disputes, and sensitivity to project cost growth, play critical roles in multi-stakeholders’ participation in design optimization of EPC projects. All these factors can be divided into causal factors (13) and result factors (20) and a hierarchical structure model is developed for the whole system, composed of three types of influencing factors, that is, the surface direct factor, intermediate indirect factor, and deep-rooted factor. The findings of this study can help managers to have a better understanding of design optimization of EPC projects from the stakeholder perspective and help managers to take effective measures to improve the status quo as well as facilitate the sustainable development of this kind of project. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sustainable Construction and Infrastructure Management)
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17 pages, 3798 KiB  
Article
A Hybrid Deep Learning Approach for Real-Time Estimation of Passenger Traffic Flow in Urban Railway Systems
by Xianlei Fu, Maozhi Wu, Sasthikapreeya Ponnarasu and Limao Zhang
Buildings 2023, 13(6), 1514; https://doi.org/10.3390/buildings13061514 - 12 Jun 2023
Cited by 2 | Viewed by 1006
Abstract
This research introduces a hybrid deep learning approach to perform real-time forecasting of passenger traffic flow for the metro railway system (MRS). By integrating long short-term memory (LSTM) and the graph convolutional network (GCN), a hybrid deep learning neural network named the graph [...] Read more.
This research introduces a hybrid deep learning approach to perform real-time forecasting of passenger traffic flow for the metro railway system (MRS). By integrating long short-term memory (LSTM) and the graph convolutional network (GCN), a hybrid deep learning neural network named the graph convolutional memory network (GCMN) was constructed and trained for accurate real-time prediction of passenger traffic flow for the MRS. Data collected of the traffic flow in Delhi’s metro rail network system in the period from October 2012 to May 2017 were utilized to demonstrate the effectiveness of the developed model. The results indicate that (1) the developed method provides accurate predictions of the traffic flow with an average coefficient of determination (R2) of 0.920, RMSE of 368.364, and MAE of 549.527, and (2) the GCMN model outperforms state-of-the-art methods, including LSTM and the light gradient boosting machine (LightGBM). This study contributes to the state of practice in proposing a novel framework that provides reliable estimations of passenger traffic flow. The developed model can also be used as a benchmark for planning and upgrading works of the MRS by metro owners and architects. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sustainable Construction and Infrastructure Management)
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19 pages, 3938 KiB  
Article
Environment-Aware Worker Trajectory Prediction Using Surveillance Camera in Modular Construction Facilities
by Qiuling Yang, Qipei Mei, Chao Fan, Meng Ma and Xinming Li
Buildings 2023, 13(6), 1502; https://doi.org/10.3390/buildings13061502 - 10 Jun 2023
Viewed by 924
Abstract
The safety of workers in modular construction remains a concern due to the dynamic hazardous work environments and unawareness of the potential proximity of equipment. To avoid potential contact collisions and to provide a safe workplace, workers’ trajectory prediction is required. With recent [...] Read more.
The safety of workers in modular construction remains a concern due to the dynamic hazardous work environments and unawareness of the potential proximity of equipment. To avoid potential contact collisions and to provide a safe workplace, workers’ trajectory prediction is required. With recent technology advancements, the study in the area of trajectory prediction has benefited from various data-driven approaches. However, existing data-driven approaches are mostly limited to considering only the movement information of workers in the workplace, resulting in poor estimation accuracy. In this study, we propose an environment-aware worker trajectory prediction framework based on long short-term memory (LSTM) network to not only take the individual movement into account but also the surrounding information to fully exploit the context in the modular construction facilities. By incorporating worker-to-worker interactions as well as environment-to-worker interactions into our prediction model, a sequence of the worker’s future positions can be predicted. Extensive numerical tests on synthetic as well as modular construction datasets show the improved prediction performance of the proposed approach in comparison to several state-of-the-art alternatives. This study offers a systematic and flexible framework to incorporate rich contextual information into the prediction model in modular construction. The observation of how to integrate construction data analytics into a single framework could be inspiring for further future research to support robust construction safety practices. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sustainable Construction and Infrastructure Management)
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22 pages, 4647 KiB  
Article
Forecasting the Final Contract Cost on the Basis of the Owner’s Cost Estimation Using an Artificial Neural Network
by Abdulah M. Alsugair, Naif M. Alsanabani and Khalid S. Al-Gahtani
Buildings 2023, 13(3), 786; https://doi.org/10.3390/buildings13030786 - 16 Mar 2023
Cited by 1 | Viewed by 1852
Abstract
Raising the final contract cost (FCC) is a significant risk for project owners. This study hypothesizes that the factors that cause owner’s cost estimation (OCE) accuracy and FCC changes share the same causes, and a case study confirmed that [...] Read more.
Raising the final contract cost (FCC) is a significant risk for project owners. This study hypothesizes that the factors that cause owner’s cost estimation (OCE) accuracy and FCC changes share the same causes, and a case study confirmed that the two variables (OCE and FCC) could be correlated. Accordingly, this study aims to develop a forecast model to predict FCC on the basis of the initial OCE, which has not been studied previously. This study utilized data from 34 Saudi Arabian projects. Two linear regression models developed the data, and the square root function transformed the data. Moreover, the artificial neural network (ANN) model was developed after data standardization using Zavadskas and Turskis’ logarithmic method. The results showed that the ANN model had a MAPE smaller than the two linear regression models. Using Zavadskas and Turskis’ logarithmic standardization method and elimination of data that had an absolute percentage error (APE) of more than 35% led to an increase in ANN model accuracy and provided a MAPE value of less than 8.5%. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sustainable Construction and Infrastructure Management)
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15 pages, 5515 KiB  
Article
Generative Design Methodology and Framework Exploiting Designer-Algorithm Synergies
by Luka Gradišar, Robert Klinc, Žiga Turk and Matevž Dolenc
Buildings 2022, 12(12), 2194; https://doi.org/10.3390/buildings12122194 - 12 Dec 2022
Cited by 6 | Viewed by 6252
Abstract
Designing is a problem-solving activity. The process is usually iterative: a solution is proposed, then analysed and tested until it satisfies all constraints and best fulfils the criteria. Usually, a designer proposes a solution based on intuition, experience, and knowledge. However, this does [...] Read more.
Designing is a problem-solving activity. The process is usually iterative: a solution is proposed, then analysed and tested until it satisfies all constraints and best fulfils the criteria. Usually, a designer proposes a solution based on intuition, experience, and knowledge. However, this does not work for problems they are facing for the first time. An alternative approach is generative design, where the designer focuses on iteratively defining a problem with its constraints and criteria in the form of a parametric computational model, and then leaves the search for the solution to the algorithms and their ability to rapidly generate and test several alternatives. The result of this approach is not only a set of solutions embedding implicitly the knowledge but also a model where problem-defining knowledge is quite explicit. The idea of the proposed approach is the exploitation of synergies between the designer and the algorithms. The designer focuses on problem definition and the algorithm focuses on finding a solution, showing that the capacity of the generative approach to replace the designer is limited. In the paper, we first present the framework of generative design, then apply the process to a case study of designing an efficient shading solution, and in the end, we present the results and compare them with the traditional approach. The approach is general and can be applied in other areas of engineering. It is relevant both to designers as well as software developers who are expected to take this approach further. More theoretical work is needed to study problem definitions as a form of knowledge representation in engineering. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sustainable Construction and Infrastructure Management)
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12 pages, 4346 KiB  
Article
Investigation of Edge Computing in Computer Vision-Based Construction Resource Detection
by Chen Chen, Hao Gu, Shenghao Lian, Yiru Zhao and Bo Xiao
Buildings 2022, 12(12), 2167; https://doi.org/10.3390/buildings12122167 - 08 Dec 2022
Cited by 4 | Viewed by 3097
Abstract
The Internet of Things (IoT), including sensors, computer vision (CV), robotics, and visual reality technologies, is widely used in the construction industry to facilitate construction management in productivity and safety control. The application of such technologies in real construction projects requires high-quality computing [...] Read more.
The Internet of Things (IoT), including sensors, computer vision (CV), robotics, and visual reality technologies, is widely used in the construction industry to facilitate construction management in productivity and safety control. The application of such technologies in real construction projects requires high-quality computing resources, the network for data transferring, a near real-time response, geographical closeness to the smart environments, etc. Most existing research has focused on the first step of method development and has neglected the further deployment step. For example, when using CV-based methods for construction site monitoring, internet-connected cameras must transmit large quantities of high-quality data to the central office, which may be located thousands of miles away. Not only the quality may suffer due to latency, but the wideband cost can be astronomical. Edge computing devices and systems help solve this problem by providing a local source to process the data. The goal of this study is to embed the CV-based method into devices and thus to develop a practical edge computing system for vision-based construction resource detection, which can provide automatic construction with high-quality and more applicable service. Specifically, this study first developed a CV-based hardhat color detection model to manage workers in different tasks. Then, the model was embedded into a Raspberry Pi microcomputer mainboard for video data processing, and the performance was compared with the local computer to validate the feasibility of the proposed method. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sustainable Construction and Infrastructure Management)
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16 pages, 2191 KiB  
Article
Assessing Accessibility and Social Equity of Tertiary Hospitals for Older Adults: A City-Wide Study of Tianjin, China
by Yuan Chen, Qiushi Ding and Yinghua Shen
Buildings 2022, 12(12), 2107; https://doi.org/10.3390/buildings12122107 - 01 Dec 2022
Cited by 1 | Viewed by 1250
Abstract
Building age-friendly cities with good accessibility and social equity can help improve older adults’ well-being and quality of life. However, current accessibility analysis of service facilities tends to target most general users, while few studies have been conducted regarding hospitals from an age-friendly [...] Read more.
Building age-friendly cities with good accessibility and social equity can help improve older adults’ well-being and quality of life. However, current accessibility analysis of service facilities tends to target most general users, while few studies have been conducted regarding hospitals from an age-friendly perspective. This study aims to measure accessibility to tertiary hospitals and conduct its equity analysis for older adults aged 65 years or over. First, the gravity-based model and geographic information system are utilized to measure accessibility to tertiary hospitals within regions and across regions, and the overall accessibility of a region. Second, coefficient of variation and global Moran’s I are adopted to investigate differences in accessibility to tertiary hospitals by type among regions. Third, Lorenz curves and Gini coefficients are employed to analyze social equity of access to medical services for the elderly. Taking Tianjin, China as the case study, the results show that there exist spatial clusters in terms of accessibility to tertiary hospitals within districts, across districts, and of the whole district. Most districts in the city center have better access to these hospitals than the peripheral and suburban districts. The social equity of accessibility to tertiary hospitals is slightly better in the senior population than in the total population. This study can help the governments improve the spatial distribution and allocation of urban health care resources in a more equitable manner and promote the development of age-friendly cities in future. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sustainable Construction and Infrastructure Management)
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19 pages, 336 KiB  
Article
Critical Factors Influencing Cost Overrun in Construction Projects: A Fuzzy Synthetic Evaluation
by Wenwen Xie, Binchao Deng, Yilin Yin, Xindong Lv and Zhenhua Deng
Buildings 2022, 12(11), 2028; https://doi.org/10.3390/buildings12112028 - 19 Nov 2022
Cited by 6 | Viewed by 6686
Abstract
Construction industries have poor cost performance in terms of finishing projects within a budget. A fuzzy model for evaluating the critical factors of cost overrun for construction projects in China is developed by identifying, classifying and ranking cost overrun factors of the construction [...] Read more.
Construction industries have poor cost performance in terms of finishing projects within a budget. A fuzzy model for evaluating the critical factors of cost overrun for construction projects in China is developed by identifying, classifying and ranking cost overrun factors of the construction industries. Sixty-five cost overrun factors are identified and classified into four clusters (project macro, project management, project environment, and core stakeholders) through a detailed literature review process and a discussion with experts from the Chinese construction industry. A questionnaire survey was conducted for data collection to calculate an index of the project-influenced factors and clusters in the construction industry in China. With the help of the proposed model, it is possible to guide project managers and decision makers to make better informative decisions such as project macro, project management, project environment, and core stakeholders. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sustainable Construction and Infrastructure Management)
26 pages, 6057 KiB  
Article
Multi-Information Fusion Based on BIM and Intuitionistic Fuzzy D-S Evidence Theory for Safety Risk Assessment of Undersea Tunnel Construction Projects
by Xiaolin Xun, Jun Zhang and Yongbo Yuan
Buildings 2022, 12(11), 1802; https://doi.org/10.3390/buildings12111802 - 27 Oct 2022
Cited by 11 | Viewed by 1721
Abstract
Safety risk assessment is essential in ensuring the smooth construction of undersea tunnels. Obtaining reasonable safety risk assessment results requires multi-source information that enjoys static and dynamic attributes. However, acquiring and utilizing such uncertain information creates difficulties in the decision-making process. Therefore, this [...] Read more.
Safety risk assessment is essential in ensuring the smooth construction of undersea tunnels. Obtaining reasonable safety risk assessment results requires multi-source information that enjoys static and dynamic attributes. However, acquiring and utilizing such uncertain information creates difficulties in the decision-making process. Therefore, this paper proposes a safety risk assessment approach based on building information modeling (BIM), intuitionistic fuzzy set (IFS) theory, and Dempster–Shafer (D-S) evidence theory. Firstly, an undersea tunnel construction collapse risk evaluation index system is established to clarify the information requirements of the pre-construction and construction stages. The semantic information of the BIM geometric model is then enriched through industry foundation classes (IFC) extension to match the multi-criteria decision-making (MCDM) process, with BIM technology used to assist in information acquisition and risk visualization. Finally, based on the intuitionistic fuzzy D-S evidence theory, multi-information fusion is performed to dynamically determine safety risk levels. Specifically, IFS theory is utilized for basic probability assignments (BPAs) determination before applying D-S evidence theory. The conflicting evidence is dealt with by reliability calculation based on the normalized Hamming distance between pairs of IFSs, while safety risk levels are accomplished with score functions of intuitionistic fuzzy values (IFVs). The proposed method is applied to collapse risk assessment in the karst developed area of a shield tunnel construction project in Dalian, China, and the feasibility and effectiveness are verified. The novelty of the proposed method lies in: (1) information collaboration between the BIM model and the dynamic safety risk assessment process being realized through IFC-based semantic enrichment and Dynamo programming to enhance the decision-making process and (2) the introduction of IFS theory to improve the applicability of D-S evidence theory in expressing fuzziness and hesitation during multi-information fusion. With the proposed method, dynamic safety risk assessment of undersea tunnel construction projects can be performed under uncertainty, fuzziness, and a conflicting environment, while the safety risk perception can be enhanced through visualization. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sustainable Construction and Infrastructure Management)
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18 pages, 6861 KiB  
Article
Critical Factors Affecting the Promotion of Emerging Information Technology in Prefabricated Building Projects: A Hybrid Evaluation Model
by Haiying Luan, Long Li, Peng Jiang and Jian Zhou
Buildings 2022, 12(10), 1577; https://doi.org/10.3390/buildings12101577 - 30 Sep 2022
Cited by 2 | Viewed by 1563
Abstract
Emerging information technology (EIT), characterized by intelligence, digitization, and automation, can facilitate activities such as stakeholder cooperation, information management, and construction management to enhance the overall performance in prefabricated building projects (PBPs). A variety of EITs are currently being used in PBPs, but [...] Read more.
Emerging information technology (EIT), characterized by intelligence, digitization, and automation, can facilitate activities such as stakeholder cooperation, information management, and construction management to enhance the overall performance in prefabricated building projects (PBPs). A variety of EITs are currently being used in PBPs, but their development is relatively sluggish and still in the infancy stage. Previous studies have explored the challenges and barriers of EIT in PBPs; however, the correlations between these factors have not been thoroughly examined. Therefore, the goal of this study is to pinpoint the characteristics and connections between EIT-affecting elements. Based on the technology–organization–environment (TOE) framework, this study firstly summarizes 20 influencing factors of EIT adoption and promotion in PBPs mentioned in the previous literature through a literature review. Then, EIT experts were invited to conduct semi-structured interviews to evaluate the relationship and the degree of influence among 20 influencing factors. Finally, the DEMATEL-ISM approach was used to assess the characteristics of each factor and the hierarchy between them. The results demonstrated that the influencing degree of the environmental dimension was more significant and had a greater influence on the whole network of influencing factors. The factors of the organizational dimension have a higher influenced degree and are easily influenced by other factors. Due to the current lack of awareness of EIT, the majority of the technology-related influencing factors have a less significant effect on adopting and promoting EIT. In summary, this study assists in analyzing the characteristics and correlations of the factors that influence EIT adoption and promotion in PBPs and identifies critical influencing factors. It also aids the government and stakeholders in developing a deeper understanding and knowledge of EIT, thereby promoting the development of EIT in PBPs. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sustainable Construction and Infrastructure Management)
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25 pages, 5826 KiB  
Article
A Design for Safety (DFS) Semantic Framework Development Based on Natural Language Processing (NLP) for Automated Compliance Checking Using BIM: The Case of China
by Yilun Zhou, Jianjun She, Yixuan Huang, Lingzhi Li, Lei Zhang and Jiashu Zhang
Buildings 2022, 12(6), 780; https://doi.org/10.3390/buildings12060780 - 07 Jun 2022
Cited by 7 | Viewed by 3685
Abstract
For design for safety (DFS), automated compliance checking methods have received extensive attention. Although many research efforts have indicated the potential of BIM and ontology for automated compliance checking, an efficient methodology is still required for the interoperability and semantic representation of data [...] Read more.
For design for safety (DFS), automated compliance checking methods have received extensive attention. Although many research efforts have indicated the potential of BIM and ontology for automated compliance checking, an efficient methodology is still required for the interoperability and semantic representation of data from different sources. Therefore, a natural language processing (NLP)-based semantic framework is proposed in this paper, which implements rules-based automated compliance checking for building information modeling (BIM) at the design stage. Semantic-rich information can be extracted from safety regulations by NLP methods, which were analyzed to generate conceptual classes and individuals of ontology and provide a corpus basis for rule classification. The data on BIM was extracted from Revit to a spreadsheet using the Dynamo tool and then mapped to the ontology using the Cellfie tool. The interoperability of different source data was well improved through the isomorphism of information in the framework of semantic integration, causing data processed by the semantic web rule language to be transformed from safety regulations to achieve the purpose that automated compliance checking is implemented in the design documents. The practicability and scientific feasibility of the proposed framework was verified through a 95.21% recall and a 90.63% precision in compliance checking of a case study in China. Compared with traditional compliance checking methods, the proposed framework had high efficiency, response speed, data interoperability, and interaction. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sustainable Construction and Infrastructure Management)
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22 pages, 5064 KiB  
Article
Automatic Classification and Coding of Prefabricated Components Using IFC and the Random Forest Algorithm
by Zhao Xu, Zheng Xie, Xuerong Wang and Mi Niu
Buildings 2022, 12(5), 688; https://doi.org/10.3390/buildings12050688 - 20 May 2022
Cited by 3 | Viewed by 2424
Abstract
The management of prefabricated component staging and turnover lacks the effective integration of informatization and complexity, as relevant information is stored in the heterogeneous systems of various stakeholders. BIM and its underlying data schema, IFC, provide for information collaboration and sharing. In this [...] Read more.
The management of prefabricated component staging and turnover lacks the effective integration of informatization and complexity, as relevant information is stored in the heterogeneous systems of various stakeholders. BIM and its underlying data schema, IFC, provide for information collaboration and sharing. In this paper, an automatic classification and coding system for prefabricated building, based on BIM technology and Random Forest, is developed so as to enable the unique representation of components. The proposed approach starts with classifying and coding information regarding the overall design of the components. With the classification criteria, the required attributes of the components are extracted, and the process of attribute extraction is illustrated in detail using wall components as an example. The Random Forest model is then employed for IFC building component classification training and testing, which includes the selection of the datasets, the construction of CART, and the voting of the component classification results. The experiment results illustrate that the approach can automate the uniform and unique coding of each component on a Python basis, while also reducing the workload of designers. Finally, based on the IFC physical file, an extended implementation process for component encoding information is designed to achieve information integrity for prefabricated component descriptions. Additionally, in the subsequent research, it can be further combined with Internet-of-Things technology to achieve the real-time collection of construction process information and the real-time control of building components. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sustainable Construction and Infrastructure Management)
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17 pages, 637 KiB  
Article
Critical Obstacles in the Implementation of Value Management of Construction Projects
by Xiaoyu Li, Binchao Deng, Yilin Yin and Yu Jia
Buildings 2022, 12(5), 680; https://doi.org/10.3390/buildings12050680 - 19 May 2022
Cited by 3 | Viewed by 3124
Abstract
At present, the construction industry in China has problems such as low production efficiency, low technical efficiency, low management efficiency of the construction project, delayed delivery, budget overruns, and unreasonable risk allocation. Value management can address these issues by enhancing the value of [...] Read more.
At present, the construction industry in China has problems such as low production efficiency, low technical efficiency, low management efficiency of the construction project, delayed delivery, budget overruns, and unreasonable risk allocation. Value management can address these issues by enhancing the value of construction projects in China, reducing construction costs, and ensuring significant investment returns. This study uses literature analysis to identify the critical obstacles to adopting value management and uses questionnaires and surveys, structural equation modeling, and factor analysis to prioritize the critical obstacles to adopting value management. What is more, the main contribution of this research is to identify the critical obstacles to the adoption of value management, which provides a new perspective for related research and has specific positive significance for practice summary and reform direction. The research was limited to the region of Tianjin and its surrounding cities. The critical survey respondents for this study are architects, quantity surveyors, contractors, civil engineers, and service engineers with rich experience in construction management. The research results show that the key obstacles to implementing value management in the construction industry in China are mainly divided into four categories: Environmental Factors; Stakeholder and Management Factors; Technological Factors; Information Factors. In addition, the researchers found that the level of the adoption of value management in the construction industry in China is deficient. Value management was not used in most of the organizations surveyed, and project teams did not practice its concept. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sustainable Construction and Infrastructure Management)
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23 pages, 1008 KiB  
Article
Interface Management Performance Assessment Framework for Sustainable Prefabricated Construction
by Shengxi Zhang, Zhongfu Li, Long Li and Mengqi Yuan
Buildings 2022, 12(5), 631; https://doi.org/10.3390/buildings12050631 - 09 May 2022
Cited by 3 | Viewed by 1936
Abstract
Prefabricated construction (PC) has been regarded as a sustainable construction method for its inherent advantages such as energy savings, emissions reductions, and cleaner and safer working environments. However, PC development has been hindered by its inherent weaknesses of fragmentation and discontinuity. Effective interface [...] Read more.
Prefabricated construction (PC) has been regarded as a sustainable construction method for its inherent advantages such as energy savings, emissions reductions, and cleaner and safer working environments. However, PC development has been hindered by its inherent weaknesses of fragmentation and discontinuity. Effective interface management (IM) is regarded as integral to PC project success for its appropriate management of numerous interfaces with high complexity and uncertainty among the organization, information, and logistics. Although some researchers mentioned the effectiveness of IM for PC projects, systematic assessment methods for IM performance are missing. This study aims to systematically develop a framework to assess the IM performance of PC projects to address this gap. Through a comprehensive literature review, nineteen indicators of IM performance were identified and grouped into four categories. By combining the objective weighting method of an ordered weighted averaging (OWA) operator with the set pair analysis (SPA) method of uncertainty assessment, a nineteen-indicator assessment model was developed. Finally, a case study was constructed using the proposed framework, and the feasibility and applicability of the OWA-SPA model were proved. The assessment results provided by the assessment model could guide project managers for better IM and serve as a valuable reference for researchers in the construction industry. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sustainable Construction and Infrastructure Management)
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18 pages, 2435 KiB  
Article
Factors Affecting Residential End-Use Energy: Multiple Regression Analysis Based on Buildings, Households, Lifestyles, and Equipment
by Yixuan Xie and Azlin Iryani Mohd Noor
Buildings 2022, 12(5), 538; https://doi.org/10.3390/buildings12050538 - 23 Apr 2022
Cited by 9 | Viewed by 2621
Abstract
Building characteristics, household compositions, lifestyles, and home equipment are recognized as the main factors influencing residential energy consumption, which has been a subject of extensive exploration for many years now. However, the quantitative correlation models between the above factors and residential end-use energy [...] Read more.
Building characteristics, household compositions, lifestyles, and home equipment are recognized as the main factors influencing residential energy consumption, which has been a subject of extensive exploration for many years now. However, the quantitative correlation models between the above factors and residential end-use energy have not been fully studied. This paper aims to explore the determinants of residential end-use energy consumption by a comprehensive analysis based on the factors of building characteristics, household compositions, lifestyles, and home equipment. For this purpose, we investigated and collected the building information of 66 households and obtained the data through an installed measurement system of the annual residential end-use energy from July 2019 to June 2020. Subsequently, six multiple regression models were used to quantitatively analyze the valid determinants of each end-use energy. The main results were as follows: for cooling energy consumption, the greatest effective variable was FM_no (22–59, number of family members aged 22 to 59); the most influential variable was found to be FM_no (number of family members) for DHW and appliances energy consumption; for lighting and cooking energy consumption, the most effective variables were AREA (floor area) and Cooking (average daily cooking hours), respectively. Moreover, the order of influence of building characteristics, household compositions, lifestyles, and home equipment over each end-use energy consumption is as follows: households > equipment > lifestyles for cooling and DHW, households > buildings > equipment for lighting, equipment > lifestyles for appliances and cooking. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sustainable Construction and Infrastructure Management)
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17 pages, 3037 KiB  
Review
Science Mapping for Recent Research Regarding Urban Underground Infrastructure
by Xianfei Yin and Mingzhu Wang
Buildings 2022, 12(11), 2031; https://doi.org/10.3390/buildings12112031 - 21 Nov 2022
Cited by 1 | Viewed by 1743
Abstract
The presented research conducted a bibliometric analysis regarding academic publications, especially journal publications, in the area of urban underground infrastructure (UI) systems (which include sewer pipes, drinking water pipes, cables, tunnels, etc.). In total, 547 journal papers published from 2002 to July 2022 [...] Read more.
The presented research conducted a bibliometric analysis regarding academic publications, especially journal publications, in the area of urban underground infrastructure (UI) systems (which include sewer pipes, drinking water pipes, cables, tunnels, etc.). In total, 547 journal papers published from 2002 to July 2022 (around 20 years period) were retrieved from Scopus using the proposed data collection method. Bibliometric analysis was conducted to extract and map the hidden information from retrieved papers. As a result, networks regarding co-citation, co-authorship, and keywords co-occurrence were generated to visualise and analyse the knowledge domain, patterns, and relationships. The eight most investigated topics in the UI research are identified and discussed, which provides an overview of the research history and focuses. Further, five potential research directions are suggested for researchers in the UI research area. The main contribution of this research is on revealing the knowledge domain of UI research in a quantitative manner as well as identifying the possible research directions. Full article
(This article belongs to the Special Issue Artificial Intelligence for Sustainable Construction and Infrastructure Management)
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