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Special Issue "Data-Driven and Intelligent Decision Support Systems in Digitized Construction"

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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 (31 December 2022) | Viewed by 26514

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Special Issue Editors

Dr. Zhen Lei

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Guest Editor

Offsite Construction Research Centre (OCRC), Department of Civil Engineering, University of New Brunswick, Fredericton, NB E3B 5A3, Canada
Interests: offsite construction; prefabricated construction; digital technologies in construction; data analytics and decision making in construction; building information modeling (BIM) and virtual design and construction (VDC)
Special Issues, Collections and Topics in MDPI journals

Dr. SangHyeok Han

E-Mail Website
Guest Editor

Building, Civil and Environmental Engineering, Concordia University, Montreal, QC, Canada
Interests: automation in construction; equipment management; modular construction; BIM

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

Special Issue Information

Dear colleagues,

The construction industry has been stagnant in terms of efficiency and productivity for the past two decades, especially if compared to other industry sectors (e.g., manufacturing). However, the recent development of digital technologies enables the construction industry to make use of data-driven systems to improve its decision-making processes and construction efficiency. In particular, various digital applications have been developed in aiding this digital transformation: the Internet-of-things (IoT), simulation and optimization-based systems, machine learning and artificial intelligence, among others. Potential submission candidates of this Special Issue include: (1) smart data collection systems in construction (e.g., IoT applications, image/vision-based); (2) real-time/near-real-time decision-making systems (e.g., optimization, simulation); (3) hybrid data visualization systems in construction (e.g., dashboard systems, virtual reality/augmented reality/mixed reality/augmented virtuality); and (4) frameworks for construction digitization implementation and strategies.

Dr. Zhen Lei
Dr. SangHyeok Han
Dr. Hexu Liu
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

Internet-of-Things (IoT) in construction
Sensors and wearables
Computer vision and imaging processing
Artificial intelligence (AI) and Machine Learning (ML)
Simulation and optimization in construction
Augmented/virtual/mixed reality and immersive environments
Digital twin and cyber physical systems
Blockchain and smart contracts
Building information modelling (BIM)/Virtual Design and Construction (VDC)
Decision support systems
Roadmaps, frameworks and strategies of digitization in construction industry

Published Papers (11 papers)

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Research

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Open AccessArticle

MODI: A Structured Development Process of Mode-Based Control Algorithms in the Early Design Stage of Building Energy Systems

and

Buildings 2023, 13(2), 267; https://doi.org/10.3390/buildings13020267 - 17 Jan 2023

Cited by 1 | Viewed by 1206

Abstract

The growing share of renewable energy sources in building energy systems leads to more complex energy conversion and distribution systems. The current process of developing appropriate control functions for energy systems is insufficient and consequently error-prone. Regarding this problem, a new method is expected to systematically develop appropriate control functions for buildings and reduce design errors in this process. This paper introduces the MODI method, aiming at a structured development process of mode-based control algorithms to reduce errors in the early design stages of buildings. A complete framework and a standardized application process of the MODI method will be established to systematically design mode-based control algorithms described through signal-interpreted Petri nets. Furthermore, we performed a simulation-assisted evaluation approach to test and improve the performance of the control algorithms generated by MODI. In a case study, we applied MODI to develop a mode-based control strategy for an energy system containing heating and cooling supply networks. The desired control strategy was tested and tuned in a simulation phase. Compared to a reference control, the mode-based control algorithm shows an improvement in system efficiency by 4% in winter and 8% during the transitional season phase. Full article

(This article belongs to the Special Issue Data-Driven and Intelligent Decision Support Systems in Digitized Construction)

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Open AccessArticle

Bayesian Network Models for Evaluating the Impact of Safety Measures Compliance on Reducing Accidents in the Construction Industry

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Buildings 2022, 12(11), 1980; https://doi.org/10.3390/buildings12111980 - 15 Nov 2022

Cited by 2 | Viewed by 1186

Abstract

Construction is one of the most hazardous industries worldwide. Implementing safety regulations is the responsibility of all parties involved in a construction project and must be performed systematically and synergistically to maximize safety performance and reduce accidents. This study aims to examine the level of safety compliance of construction personnel (i.e., top management, frontline supervisors, safety coordinators/managers, and workers) to gain insight into the top safety measures that lead to no major or frequent accidents and to predict the likelihood of having a construction site free of major or frequent accidents. To achieve the objectives, five safety measures subsets were collected and modeled using six combinations of five different Bayesian networks (BNs). The performance of these model classifiers was compared in terms of accuracy, sensitivity, specificity, recall, precision, F-measure, and area under the receiver operating characteristic curve. Then, the best model for each data subset was adopted. The inference was then performed to identify the probability of the commitment to safety measures to reduce major or frequent accidents and recommend enhancement regulations and practices. While the context in this paper is the Jordanian construction industry, the novelty of the work lies in the BN modeling methodology and recommendations that any country can adopt for evaluating the safety performance of its construction industry. This research endeavor is, therefore, a significant step toward providing knowledge about the top safety measures associated with reducing accidents and establishing efficiency comparison benchmarks for improving safety performance. Full article

(This article belongs to the Special Issue Data-Driven and Intelligent Decision Support Systems in Digitized Construction)

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Open AccessFeature PaperArticle

Software Architecture and Non-Fungible Tokens for Digital Twin Decentralized Applications in the Built Environment

Benjamin Teisserenc

and

Samad M. E. Sepasgozar

Buildings 2022, 12(9), 1447; https://doi.org/10.3390/buildings12091447 - 14 Sep 2022

Cited by 4 | Viewed by 2812

Abstract

Blockchain technology (BCT) can enable distributed collaboration, enhance data sharing, and automate back-end processes for digital twin (DT) decentralized applications (dApps) in the construction industry (CI) 4.0. The aim of this paper was to propose a software architecture and to develop a framework of smart contracts for blockchain-based digital twin (BCDT) dApps throughout the lifecycle of projects in CI 4.0. This paper leveraged the existing literature and action research interviews to identify and validate the critical industry problems, functional requirements (FRs), and non-functional requirements (NFRs) to be addressed by BCDT dApps in CI 4.0. Basic use cases were developed to design a framework of smart contracts for BCDT dApps throughout the lifecycle of projects. The analysis of an online survey was used to identify the key requirements and enablers to propose a software architecture for BCDT applications and to validate the requirements for developing the framework of a smart contract for BCDTs. The findings were: (1) The identification of key problems in CI 4.0 for each BIM/BCDT dimension (3D, 4D, 5D, 6D, 7D, 8D, and contractual (cD)) and the related FRs and NFRs for BCDT applications. Additionally, key use cases were designed to address the problems identified. (2) The proposed BCDT architecture permitted us to narrow gaps in the literature on blockchain-based decentralized digital twins. Moreover, the proposed BCDT architecture and smart-contract framework addressed the main requirements in the literature on BCDTs. (3) The study leveraged the non-fungible token (NFT) standard to develop a framework for smart contracts that addressed the key use cases and the related industry problems and functional requirements that were identified. The study also considered the contractual dimension (cD) as an overarching dimension in relation to the other BCDT dimensions. (4) We also compared the costs of several public blockchains for executing the proposed smart-contract framework throughout the lifecycle of a medium-sized building project. The cost analysis permitted the development of criteria to evaluate the suitability of blockchain networks for BCDT applications in CI 4.0 depending on the principal blockchain networks’ properties (security, decentralization, scalability, and interoperability). Finally, this study resulted in a novel framework that included software architecture, smart-contract use cases, and selection criteria among blockchain networks for BCDT dApps in CI 4.0. Full article

(This article belongs to the Special Issue Data-Driven and Intelligent Decision Support Systems in Digitized Construction)

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Open AccessArticle

Integration of Building Information Modeling (BIM) and Virtual Design and Construction (VDC) with Stick-Built Construction to Implement Digital Construction: A Canadian General Contractor’s Perspective

Mohamed Adel

Zhuo Cheng

and

Zhen Lei

Buildings 2022, 12(9), 1337; https://doi.org/10.3390/buildings12091337 - 31 Aug 2022

Cited by 3 | Viewed by 3105

Abstract

Building information modeling (BIM) and virtual design and construction (VDC) are useful management processes and methodologies to enhance project communication and coordination. Over the past few decades, researchers and practitioners have made efforts to promote the adoption of BIM and VDC in the construction industry. However, currently, the promotion and adoption of BIM and VDC are relatively slow in North America. This paper focuses on developing an approach to drive the adoption of the technologies through cooperation among project stakeholders and proposing a method of collaboration through a case study. In this paper, the authors surveyed and interviewed a major Canadian general contractor on its implementation of BIM and VDC in construction projects. The was to benchmark the status of BIM and VDC applications in the Atlantic region of Canada from a general contractor’s perspective. Both surveys and interviews were conducted with various project participants throughout the organization. Based on the results, a “Digital Construction Framework for the Future” is proposed to increase the adoption of BIM and VDC. This research can provide a general understanding of BIM and VDC adoption in this particular general contractor and areas of improvement in transitioning to a more digital working construction environment. Full article

(This article belongs to the Special Issue Data-Driven and Intelligent Decision Support Systems in Digitized Construction)

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Open AccessArticle

A Predictive Analytics Framework for Mobile Crane Configuration Selection in Heavy Industrial Construction Projects

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Buildings 2022, 12(7), 960; https://doi.org/10.3390/buildings12070960 - 05 Jul 2022

Cited by 2 | Viewed by 1231

Abstract

Predictive analytics have been used to improve efficiency and productivity in the construction industry by leveraging the insights from historical data with a variety of applications in project management. In the planning process of heavy industrial construction projects, mobile crane selection plays a critical role in the project’s success, and poor choice of mobile crane configurations can lead to unnecessary cost-overrun and delayed schedules. In this research, the authors propose a predictive analytics framework for crane configuration selection using combined heuristic search and artificial neural network (ANN) approaches for heavy industrial construction projects. The heuristic search allows the practitioners to select the crane configurations based on engineering rules, while the ANN model utilizes the historical project data to help select crane configurations. The K-fold cross-validation is conducted to validate the designed ANN model and improve the accuracy of predictions. The results from the cross-validation test set have shown 70% accuracy. Full article

(This article belongs to the Special Issue Data-Driven and Intelligent Decision Support Systems in Digitized Construction)

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Open AccessFeature PaperArticle

An Off-Site Construction Digital Twin Assessment Framework Using Wood Panelized Construction as a Case Study

Yuxi Wei

Zhen Lei

and

Sadiq Altaf

Buildings 2022, 12(5), 566; https://doi.org/10.3390/buildings12050566 - 28 Apr 2022

Cited by 11 | Viewed by 2168

Abstract

Off-site construction is an innovative type of construction with the philosophy of standardizing the process and deploying the latest technological enablers. Many technologies, such as the Building Information Model (BIM), Internet of Things (IoT), etc., are concerned with virtual representation and manipulation of the physical site. However, a holistic view of the off-site construction processes is lacking in the exploration of the technological advances, resulting in inconsistency when applying these advances in practice. The concept of Digital Twin is useful for addressing this challenge. Digital Twin is a philosophy and a collection of technologies aimed toward seamless physical and virtual connections. Therefore, a holistic Off-site Construction Digital Twin model is necessary for any research concerning this topic, and an assessment framework is useful in helping off-site construction industry companies in approaching systematic Digital Twin. This research first proposes a model for Off-site Construction Digital Twin. To quantify this model, an assessment tool named Off-site Construction Digital Twin Maturity Level is proposed. The validation and evaluation of this assessment framework are conducted through a case study with ACQBuilt, an off-site construction company in Edmonton, Canada. The resulting assessment framework contributes to the body of knowledge in two ways: Firstly, it sets the foundation for an Off-site Construction Digital Twin, which is anticipated to significantly reduce waste and to improve efficiency. Secondly, it enables easier technology application in practice by offering a holistic Digital Twin framework. Full article

(This article belongs to the Special Issue Data-Driven and Intelligent Decision Support Systems in Digitized Construction)

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Open AccessArticle

Towards Automated Construction Quantity Take-Off: An Integrated Approach to Information Extraction from Work Descriptions

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Buildings 2022, 12(3), 354; https://doi.org/10.3390/buildings12030354 - 15 Mar 2022

Cited by 1 | Viewed by 2000

Abstract

Construction-oriented quantity take-off (QTO) refers to the process of determining the quantities for construction items or work packages in accordance with their descriptions. However, the current construction-oriented QTO practice relies on estimators’ manual interpretation of work descriptions and manual processes to look up proper building objects for quantity calculation. Hence, this research aims to develop natural language processing (NLP) and rule-based algorithms to automate the information extraction (IE) from work descriptions for QTO in building construction. Specifically, several named entity recognition (NER) models, including Hidden Markov Model (HMM), Conditional Random Field (CRF), Bidirectional-Long Short-Term Memory (Bi-LSTM), and Bi-LSTM+CRF, were developed to identify construction activities, material, building component, product features, measurement unit, and additional information (e.g., work scope) from work descriptions. Cost items in the RSMeans database are used to evaluate the developed models in terms of F1 scores. HMM was found to achieve a 5% higher F1 score in the NER than the other three algorithms. Then, labeling rules and active learning strategies were applied along with the HMM model, which improved F1 score by 3% and reduced the labeling efforts by 26%. The results showed that the proposed IE method successfully interprets the desired information from the work description for QTO. This research contributed to the body of knowledge by the NLP-based information extraction model integrating HMM and formalized labeling rules that automatically process work descriptions and lay a foundation for automated QTO and cost estimation. Full article

(This article belongs to the Special Issue Data-Driven and Intelligent Decision Support Systems in Digitized Construction)

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Open AccessArticle

A Robust Construction Safety Performance Evaluation Framework for Workers’ Compensation Insurance: A Proposed Alternative to EMR

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Buildings 2021, 11(10), 434; https://doi.org/10.3390/buildings11100434 - 26 Sep 2021

Cited by 5 | Viewed by 2154

Abstract

The construction work environment remains one of the most hazardous among all industries. Construction injuries directly impact the workers and the work itself, including personal suffering, construction delays, productivity losses, higher insurance premiums, and possible liability suits for all parties involved in the project. The costs resulting from personal injuries, combined with the associated financial impact resulting from schedule disruptions, insurance hikes, and workers’ compensation, can impact a project’s profitability. Many of these impacts can be minimized or avoided through the continuous assessment and improvement of safety policies and practices. This paper aims to propose a new safety assessment methodology that equips insurance companies and construction managers with an optimal mechanism for evaluating the safety performance of construction companies. The proposed model consists of 20 evaluation criteria that are used to establish the efficiency benchmarks and provide comparison feedback for improving the company’s safety plans and procedures. These criteria are determined based on leading and lagging safety performance indicators. The data envelopment analysis (DEA) technique is used as the underlying model to assess the relative efficiency of safety practices objectively. Two illustration case studies are provided to demonstrate the dual effectiveness of the DEA model. The presented research contributes to the body of knowledge by formalizing a robust, effective, and consistent safety performance assessment. The model equips the company with the ability to track both the progression and the retrogression over time and provides feedback on ineffective practices that need more attention. Simultaneously, the model gives them more detailed safety performance information that can replace the current experience modification rating (EMR) approach. It provides insurance companies with an objective and robust evaluation model for selecting optimum rates for their clients. In addition, the data comparison utility offered by the DEA model and its criteria can be helpful for insurance companies to provide effective advice to their clients on which safety aspects to improve in their future strategies. Full article

(This article belongs to the Special Issue Data-Driven and Intelligent Decision Support Systems in Digitized Construction)

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Review

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Open AccessReview

Additive Manufacturing in Off-Site Construction: Review and Future Directions

Jubert Pasco

Zhen Lei

and

Clodualdo Aranas, Jr.

Buildings 2022, 12(1), 53; https://doi.org/10.3390/buildings12010053 - 06 Jan 2022

Cited by 11 | Viewed by 4829

Abstract

Additive manufacturing (AM) is one of the pillars of Industry 4.0 to attain a circular economy. The process involves a layer-by-layer deposition of material from a computer-aided-design (CAD) model to form complex shapes. Fast prototyping and waste minimization are the main benefits of employing such a technique. AM technology is presently revolutionizing various industries such as electronics, biomedical, defense, and aerospace. Such technology can be complemented with standardized frameworks to attract industrial acceptance, such as in the construction industry. Off-site construction has the potential to improve construction efficiency by adopting AM. In this paper, the types of additive manufacturing processes were reviewed, with emphasis on applications in off-site construction. This information was complemented with a discussion on the types and mechanical properties of materials that can be produced using AM techniques, particularly metallic components. Strategies to assess cost and material considerations such as Production line Breakdown Structure (PBS) and Value Stream Mapping are highlighted. In addition, a comprehensive approach that evaluates the entire life cycle of the component was suggested when comparing AM techniques and conventional manufacturing options. Full article

(This article belongs to the Special Issue Data-Driven and Intelligent Decision Support Systems in Digitized Construction)

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Other

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Open AccessConcept Paper

Data Cooperatives as a Catalyst for Collaboration, Data Sharing and the Digital Transformation of the Construction Sector

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Buildings 2023, 13(2), 442; https://doi.org/10.3390/buildings13020442 - 05 Feb 2023

Viewed by 1662

Abstract

Digital federated platforms and data cooperatives for secure, trusted and sovereign data exchange will play a central role in the construction industry of the future. With the help of platforms, cooperatives and their novel value creation, the digital transformation and the degree of organization of the construction value chain can be taken to a new level of collaboration. The goal of this research project was to develop an experimental prototype for a federated innovation data platform along with a suitable exemplary use case. The prototype is to serve the construction industry as a demonstrator for further developments and form the basis for an innovation platform. It exemplifies how an overall concept is concretely implemented along one or more use cases that address high-priority industry pain points. This concept will create a blueprint and a framework for further developments, which will then be further established in the market. The research project illuminates the perspective of various governance innovations to increase industry collaboration, productivity and capital project performance and transparency as well as the overall potential of possible platform business models. However, a comprehensive expert survey revealed that there are considerable obstacles to trust-based data exchange between the key stakeholders in the industry value network. The obstacles to cooperation are predominantly not of a technical nature but rather of a competitive, predominantly trust-related nature. To overcome these obstacles and create a pre-competitive space of trust, the authors therefore propose the governance structure of a data cooperative model, which is discussed in detail in this paper. Full article

(This article belongs to the Special Issue Data-Driven and Intelligent Decision Support Systems in Digitized Construction)

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Open AccessPerspective

Digital Technologies in Offsite and Prefabricated Construction: Theories and Applications

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Buildings 2023, 13(1), 163; https://doi.org/10.3390/buildings13010163 - 09 Jan 2023

Cited by 3 | Viewed by 2544

Abstract

Due to its similarity to industrialized products, the offsite construction industry is seen as a focus for the transformation of Construction 4.0. Many digital technologies have been applied or have the potential to be applied to realize the integration of design, manufacturing, and assembly. The main objective of this review was to identify the current stage of applying digital technologies in offsite construction. In this review, 171 related papers from the last 10 years (i.e., 2013–2022) were obtained by collecting and filtering them. They were classified and analyzed according to the digital twin concept, application areas, and specific application directions. The results indicated that there are apparent differences in the utilization and development level of different technologies in different years. Meanwhile, the introduction, current stages, and benefits of different digital technologies are also discussed. Finally, this review summarizes the current popular fields and speculates on future research directions by analyzing article publication trends, which sheds light on future research. Full article

(This article belongs to the Special Issue Data-Driven and Intelligent Decision Support Systems in Digitized Construction)

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