Submit to Special Issue Submit Abstract to Special Issue Review for Buildings Propose a Special Issue

Journal Menu

Journal Browser

Intelligence and Automation in Construction—2nd Edition

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Related Special Issue
Published Papers

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

Deadline for manuscript submissions: 10 October 2026 | Viewed by 5575

Share This Special Issue

Special Issue Editors

Dr. Jingjing Guo

E-Mail Website
Guest Editor

College of Civil Engineering, Hunan University, Changsha 410012, China
Interests: smart construction; defect detection; quality inspection; computer vision; construction management
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Qian Wang

E-Mail Website
Guest Editor

Department of Construction and Real Estate, School of Civil Engineering, Southeast University, Nanjing 211189, China
Interests: BIM; LiDAR; quality inspection; 3D reconstruction
Special Issues, Collections and Topics in MDPI journals

Dr. Weiwei Chen

E-Mail Website
Guest Editor

Bartlett School of Sustainable Construction, University College London, 1-19 Torrington Pl, London WC1E 7HB, UK
Interests: HBIM; point cloud; immersive technologies; data management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The construction industry is undergoing a new wave of digital transformation, driven by advances in artificial intelligence, robotics, and automation. While productivity gains in this sector have historically lagged behind other industries, emerging technologies are enabling safer worksites, cost reductions, and improved efficiency and quality.

Building on the success of the first edition, the second edition of this Special Issue broadens its focus to include not only robotics, computer vision, and smart management systems, but also recent developments such as generative AI, large language models (LLMs), digital twins, multimodal sensing, and human–robot collaboration. By showcasing innovative methods and practical applications, the Reprint aims to highlight how automation is evolving from task-specific tools to integrated, intelligent ecosystems that support decision-making and lifecycle management.

We invite contributions that explore novel algorithms, experimental validations, case studies, and review studies. Topics of interest include:

Robotics for construction
AI-based inspection and evaluation
Data-driven planning and decision support
Smart management
Multimodal fusion
Strategies for advancing intelligent
Automated solutions in construction and maintenance

Dr. Jingjing Guo
Prof. Dr. Qian Wang
Dr. Weiwei Chen
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 250 words) can be sent to the Editorial Office for assessment.

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 semimonthly 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
robotics
computer vision
human–machine collaboration
human–robot interaction
digital twins
multimodal sensing and data fusion
large language models (LLMs)
knowledge-graph-enhanced reasoning

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Intelligence and Automation in Construction Industry in Buildings (10 articles)

Published Papers (8 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

Jump to: Review

24 pages, 1924 KB

Open AccessArticle

BIM-SeL: Building Information Modelling Data-Adaptive Natural-Language Sequence Labeling Using Machine Learning

by Qi Qiu, Xiaoping Zhou, Yukang Wang, Jichao Zhao, Maozu Guo and Xin Zhang

Buildings 2026, 16(9), 1731; https://doi.org/10.3390/buildings16091731 - 27 Apr 2026

Viewed by 230

Abstract

Building Information Modelling has become a common paradigm in the construction industry. To bridge the gap between end users and BIM data, some studies have adopted Natural Language Processing (NLP) in the BIM applications. Due to the incorrect segmentation of users’ natural language, most NLP-based BIM applications usually provide users with redundant or inaccurate BIM data. Sequence labeling has been widely studied in the area of NLP to find correct segments of a natural language sequence. However, the existing sequence labeling schemes perform poorly for specific BIM models. To address this issue, this study proposed a BIM model of an adaptive natural-language Sequence Labeling scheme using Machine learning, termed BIM-SeL. We first presented the problem definition of sequence labeling and the overall framework of the BIM-SeL. The BIM-SeL employs Conditional Random Field (CRF) to model the sequence labeling problem and Machine learning to train a sequence labeling model using a corpus of millions of data from the news and web domains. Then, a BIM dictionary extraction algorithm is developed to collect the exclusive vocabularies from the BIM models. A BIM dictionary-enhanced sequence labeling scheme is proposed to achieve the BIM model adaptive sequence labeling, by jointly utilizing the trained sequence labeling model and the BIM dictionary. To further enhance contextual representation and compare with state-of-the-art deep learning methods, we extend BIM-SeL with an advanced BERT*-BiLSTM-CRF model under the same framework. The effectiveness of the BIM-SeL was verified through two real-world projects, the BUCEA Library and a water pump house. The experiment results showed that the sequence accuracies of BIM-SeL in the BUCEA Library and the water pump house projects achieved 92.61% and 93.41%, respectively, and the vocabulary accuracies reach 96.77% and 97.32%, respectively. Compared with the original CRF-based sequence labeling algorithm, the BIM-SeL improved the sequence accuracies by 7.05 and 18.50 times, and the vocabulary accuracies by 1.33 and 2.48 times, in the two projects. Meanwhile, the BERT-BiLSTM-CRF variant obtains up to 99.93% vocabulary accuracy on real BIM test sequences, further validating the generality and advancement of the proposed framework. These observations proved that the BIM-SeL contributed to the natural language understanding of BIM applications using BIM data and could bridge the gap between users and BIM data. Full article

(This article belongs to the Special Issue Intelligence and Automation in Construction—2nd Edition)

► Show Figures

Figure 1

25 pages, 3298 KB

Open AccessArticle

A Comparison of Human Capabilities and Large Language Models for Knowledge Representation with Ontologies of Non-Destructive Testing in Bridge Engineering

by Jan-Iwo Jäkel, Eva Heinlein, Joy Sengupta, Hongjo Kim and Katharina Klemt-Albert

Buildings 2026, 16(7), 1395; https://doi.org/10.3390/buildings16071395 - 1 Apr 2026

Viewed by 517

Abstract

Bridge structures are considered complex and significant. Accordingly, the knowledge of the engineering domain of bridge construction and related specialist areas is multidimensional and highly specific. Sometimes this knowledge is explicitly documented in standards, technical regulations, or information sheets. At other times, it resides implicitly in the expertise of the specialists involved. Ontologies are used to structure and formalize such domain knowledge, but creating them is resource-intensive and requires specialized expertise. Large language models (LLMs) offer one way to automate ontology creation through their natural language processing capabilities. This article examines LLMs’ ability to generate ontologies in the specialized field of structural non-destructive testing (NDT) in bridge construction. Four different LLM-based approaches are employed. The results are compared with a previously created human-generated ontology and subsequently evaluated by external experts. Experts rate the human-developed SODIA ontology highest, with an average score of 3.44 out of 5 points. Only the ChatGPT 4.0-created ontology performed similarly well, with a score of 3.3 out of 5.00. All other LLM-based ontologies with ratings below 3.0 are of minor quality. These results underscore the potential and constraints of using LLMs to structure and formalize engineering domain knowledge into ontologies. Full article

(This article belongs to the Special Issue Intelligence and Automation in Construction—2nd Edition)

► Show Figures

Figure 1

32 pages, 5809 KB

Open AccessArticle

Ontology-Driven Automatic Scoring of Mechanization Rate in Power Grid Construction Projects Using Large Language Models

by Jiawei Chen, Xin Xu, Jun Liu, Yunyun Gao, Jingjing Guo, Zhuqing Ding, Mao Zhang, Juncheng Zhu and Yifan He

Buildings 2026, 16(5), 1010; https://doi.org/10.3390/buildings16051010 - 4 Mar 2026

Viewed by 527

Abstract

Driven by the global energy transition, mechanized construction—characterized by enhanced safety, efficiency, and quality—is becoming the mainstream approach in power grid development. Mechanization assessment serves as a critical tool for guiding and optimizing this process, yet current practices remain largely manual, resulting in inefficiency, time-consuming operations, and a lack of real-time insights, which severely limit its practical utility for dynamic project guidance. To address these challenges, this study proposes a novel framework that integrates semantic technology (i.e., ontology) and large language models (LLMs). The framework first constructs a semantic model of the power grid construction domain using ontology. An LLM is then employed to convert multi-source project data into structured ontological instances. Building on this, mechanization assessment criteria are formalized into machine-executable Semantic Web Rule Language (SWRL) rules, which enable automated reasoning and scoring through an ontological reasoner. Furthermore, the LLM is utilized to generate comprehensive and intelligible assessment reports based on the reasoning outputs. To validate the proposed method, 126 real-world project cases were applied to the system. The results demonstrate a 96% accuracy rate in mechanization assessment outcomes compared to expert evaluations. The approach facilitates an objective, standardized, and dynamic evaluation of construction mechanization levels, providing a foundation for intelligent and scalable management models in power grid construction. Full article

(This article belongs to the Special Issue Intelligence and Automation in Construction—2nd Edition)

► Show Figures

Figure 1

30 pages, 1874 KB

Open AccessArticle

Identifying and Prioritizing Barriers to Modular Construction Adoption in China: A Multi-Method Stakeholder Analysis

by Chenxi Yu and Guoqiang Zhang

Buildings 2026, 16(2), 432; https://doi.org/10.3390/buildings16020432 - 20 Jan 2026

Viewed by 1230

Abstract

Modular construction (MC) offers significant environmental and efficiency advantages yet maintains low market penetration in China despite substantial government support. This study addresses the critical knowledge gap by systematically analyzing complex barrier interrelationships across project phases and stakeholder groups (university, construction authority, supplier/manufacturer company) to develop a comprehensive MC promotion framework. A four-phase mixed method approach was employed. (1) Grounded theory analysis of MC policy frameworks was performed in Singapore, the United States, and Hong Kong to extract best practice insights. (2) A systematic literature review and multi-round Delphi expert consultations were used to identify 21 core barriers across six project stages (decision-making, procurement, design, production, transportation, and construction acceptance). (3) The DEMATEL analysis reveals causal relationships among barriers based on experts’ perceived influence between factors. (4) Integrated ISM-MICMAC methodology was used to establish hierarchical structures and barrier classifications. Institutional barriers emerged as the primary impediment to MC diffusion, with unclear authority distribution between government administrations and design organizations identified as the most critical factor. The MICMAC analysis categorized the 21 barriers into four distinct groups based on their driving power and dependence characteristics, revealing complex causal relationships among barriers across the six project stages while highlighting the emergent role of higher education institutions in industrial transformation. Successful MC implementation requires market-oriented, context-specific strategies prioritizing institutional framework development, with the findings providing actionable insights for policymakers to address regulatory ambiguities and practical guidance for industry practitioners developing targeted MC promotion strategies in emerging markets. Full article

(This article belongs to the Special Issue Intelligence and Automation in Construction—2nd Edition)

► Show Figures

Figure 1

32 pages, 7960 KB

Open AccessArticle

Quality Inspection of Automated Rebar Sleeve Connections Using Point Cloud Semantic Filtering and Geometry-Prior Segmentation

by Haidong Wang, Youyu Shi, Jingjing Guo and Dachuan Chen

Buildings 2026, 16(2), 338; https://doi.org/10.3390/buildings16020338 - 13 Jan 2026

Viewed by 474

Abstract

In reinforced concrete structures, the quality of rebar sleeve connections directly impacts the structure’s safety reserve and durability. However, quality inspection is complicated by the periodic distribution of stirrups, concrete obstruction, and noise interference, presenting challenges for assessing sleeve connection integrity. This paper proposes a training-free, interpretable framework for automated rebar sleeve connection quality inspection, leveraging point cloud semantic filtering and geometric a priori segmentation. The method constructs a polar-cylindrical framework, employing hierarchical semantic filtering to eliminate stirrup layers. Geometric a priori instance segmentation techniques are then applied, integrating θ histograms, Kasa circle fitting, and axial bridging domain constraints to reconstruct each longitudinal rebar. Sleeve detection occurs within the rebar coordinate system via radial profile analysis of length, angular coverage, and stability tests, subsequently stratified into two layers and parameterised. Sleeve projections onto column axes calculate spacing and overlap area percentages. Experiments using 18 BIM-TLS paired datasets demonstrate that this method achieves zero residual error in stirrup detection, with sleeve parameter accuracy reaching 98.9% in TLS data and recall at 57.5%, alongside stable runtime transferability. All TLS datasets meet the quality requirements of rebar sleeve connection spacing ≥35d and percentage of overlap area ≤50%. This framework enhances on-site quality inspection efficiency and consistency, providing a viable pathway for digital verification of rebar sleeve connection quality. Full article

(This article belongs to the Special Issue Intelligence and Automation in Construction—2nd Edition)

► Show Figures

Figure 1

24 pages, 4417 KB

Open AccessArticle

Safety Helmet-Based Scale Recovery for Low-Cost Monocular 3D Reconstruction on Construction Sites

by Jianyu Ren, Lingling Wang, Xuanxuan Liu and Linghong Zeng

Buildings 2025, 15(23), 4291; https://doi.org/10.3390/buildings15234291 - 26 Nov 2025

Viewed by 723

Abstract

Three-dimensional (3D) reconstruction is increasingly being adopted in construction site management. While most existing studies rely on auxiliary equipment such as LiDAR and depth cameras, monocular depth estimation offers broader applicability under typical site conditions, yet it has received limited attention due to the inherent scale ambiguity in monocular vision. To address this limitation, this study proposes a safety helmet-based scale recovery framework that enables low-cost, monocular 3D reconstruction for construction site monitoring. The method utilizes safety helmets as exemplary scale carriers due to their standardized dimensions and frequent appearance in construction scenes. A Standard Template Library (STL) comprising multi-angle safety helmet masks and dimensional information is established and linked to site imagery through template matching. Following three-dimensional scale recovery, multi-frame fusion is applied to optimize the scale factors. Experimental results on multiple real construction videos demonstrate that the proposed method achieves high reconstruction accuracy, with a mean relative error below 10% and outliers not exceeding 5%, across diverse construction environments without site-specific calibration. This work aims to contribute to the practical application of monocular vision in engineering management by leveraging ubiquitous site objects as metrological references. Full article

(This article belongs to the Special Issue Intelligence and Automation in Construction—2nd Edition)

► Show Figures

Figure 1

34 pages, 12155 KB

Open AccessArticle

Data-Driven Simulation of Near-Fault Ground Motions Using Stationary Wavelet Transform and Hilbert Analysis

by Weikun He, Zexin Guo, Chaobin Li, Wei Wang, Biao Wei, Ping Shao and Yongping Zeng

Buildings 2025, 15(23), 4219; https://doi.org/10.3390/buildings15234219 - 21 Nov 2025

Cited by 1 | Viewed by 812

Abstract

Near-fault ground motions exhibit significant characteristics such as velocity pulses, rupture directivity, and strong vertical components, which pose serious threats to structural safety. However, near-fault ground motion records remain scarce and have not been adequately accounted for in current seismic design codes. This paper proposes a data-driven simulation method for non-stationary near-fault ground motions based on Stationary Wavelet Transform (SWT) combined with Hilbert’s instantaneous frequency estimation. First, to address the baseline drift issue commonly observed in measured seismic motions, a baseline correction technique combining the least squares method and the Iwan method is proposed to enhance the reliability of seismic time histories. Subsequently, statistical distributions of velocity pulses and vertical-to-horizontal (V/H) acceleration ratios, along with their relationships with fault distance and magnitude, are analyzed based on more than 900 ground motion records. The results show that these near-fault motions generally contain pronounced long-period components, which will have significant implications for the seismic response of long-period structures. Additionally, unidirectional pulses dominate in near-fault records. Among the 107 selected long-period pulse records, unidirectional pulses account for 69.2%. Based on this, seismic motions are decomposed using SWT, and stochastic reconstruction is performed, combined with multivariate response spectrum matching to optimize the generation of near-fault time histories consistent with the target spectrum. Compared with the results obtained without optimization, the proposed method reduces the mean square error by about 40% or more, demonstrating a clear improvement in accuracy and reliability. This method provides reliable seismic input support for seismic analysis and performance-based design of bridges in near-fault regions. Full article

(This article belongs to the Special Issue Intelligence and Automation in Construction—2nd Edition)

► Show Figures

Figure 1

Review

Jump to: Research

39 pages, 4133 KB

Open AccessReview

Algorithms Without Foundations—Quantifying the Technocentric Bias in Construction AI Research Against Practitioner-Identified Adoption Barriers

by Janusz Sobieraj and Dominik Metelski

Buildings 2026, 16(9), 1720; https://doi.org/10.3390/buildings16091720 - 27 Apr 2026

Viewed by 400

Abstract

The construction industry accounts for approximately 13% of global GDP but suffers from chronic productivity stagnation. Although artificial intelligence (AI) offers transformative potential, its adoption is constrained by three key barriers: data integrity issues (H1), socio-technical challenges (H2), and system integration problems (H3). This study investigates whether academic research attention aligns with these practitioner-identified barriers through a bibliometric analysis of 4668 publications from OpenAlex (1990–2025), applying a five-pillar analytical framework synthesized into composite scores (0–100 scale) via min-max normalization, weighted summation, and bootstrap validation. H3 achieved a nominal 15.9% prevalence rate (adjusted to ~13.0% after correcting for an 18.2% false positive rate in keyword classification), robust growth (R² = 0.654), significant overrepresentation in top-cited works (risk ratio = 1.31, p = 0.003), and received a composite score of 62/100 (confirmed). H1 (2.7%, score: 17/100) and H2 (4.6%, score: 13/100) were both rejected. The rank ordering by prevalence (H3 > H2 > H1) remains robust under all adjustment scenarios. These findings contrast notably with the RICS Global Construction Monitor (2025, n = 2200+), where practitioners most frequently reported socio-technical barriers (46%), followed by system integration (37%) and data quality (30%), yielding practitioner-to-publication ratios of 4.7:1, 5.2:1, and 1.1:1, respectively. This apparent research–practice paradox appears primarily volume-driven rather than clearly quality-driven: H1/H2 publications receive citation attention broadly comparable to the baseline, though this comparison is limited by control group heterogeneity. We call for rebalanced research agendas addressing data governance frameworks, competency development, and organizational change management. Full article

(This article belongs to the Special Issue Intelligence and Automation in Construction—2nd Edition)

► Show Figures

Journal Menu

Journal Browser

Intelligence and Automation in Construction—2nd Edition

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Related Special Issue

Published Papers (8 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI