Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
2.5
Journals
Applied Sciences
Special Issues
Advancing Building Information Modeling Through Artificial Intelligence: Methods, Applications, and...
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advancing Building Information Modeling Through Artificial Intelligence: Methods, Applications, and Challenges

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 5111

Special Issue Editors

Prof. Dr. Eric Fabián Forcael Durán

E-Mail Website
Guest Editor
Facultad de Ingeniería y Tecnología, Universidad San Sebastián, Concepción, Chile
Interests: Construction 4.0; simulation; optimization; construction engineering; construction management; engineering education
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Felipe Muñoz-La Rivera

E-Mail Website
Guest Editor
School of Civil Engineering, Pontificia Universidad Católica de Valparaíso, Av. Brasil 2147, Valparaíso, Chile
Interests: virtual and design construction (VDC); building information modeling (BIM); extended reality (XR) in AEC industry; technologies in AEC industry; project management (PM); engineering education (EE)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is rapidly transforming the architecture, engineering, and construction (AEC) industry. From automating routine tasks to enabling smarter, more predictive decision-making, AI is reshaping how built environments are designed, constructed, and maintained. At the same time, building information modeling (BIM) continues to serve as a central platform for managing the digital representations of buildings and infrastructure throughout their lifecycle.

Bringing these two technologies together opens up exciting possibilities. AI can enrich BIM processes with greater efficiency, adaptability, and insight, leading to more sustainable and coordinated projects. This Special Issue aims to explore this evolving synergy by featuring contributions demonstrating how AI is redefining the use and potential of BIM.

We invite original research articles, case studies, reviews, and technical notes that address theoretical, computational, and applied aspects of AI-powered BIM. Submissions may focus on new algorithms, experimental approaches, or real-world applications in various AEC domains.

Relevant topics include, but are not limited to, the following:

  • AI-assisted design and optimization in BIM workflows.
  • Machine learning for automatic clash detection and issue resolution.
  • Use of NLP to extract and interpret information from construction documents.
  • Computer vision techniques for generating BIM models from images or 3D scans.
  • Predictive maintenance using BIM enriched with AI analytics.
  • Generative design enabled by deep learning or heuristic algorithms.
  • AI-driven tools for estimating costs, scheduling, and managing construction risks.
  • Enhancing semantic data and interoperability across BIM and AI systems.
  • Integrating AI-powered BIM into digital twins and smart city infrastructure.
  • Field-based case studies showcasing AI-BIM integration in practice.

We encourage submissions from researchers, industry professionals, and interdisciplinary teams working at the frontier of AI and BIM innovation.

Prof. Dr. Eric Fabián Forcael Durán
Prof. Dr. Felipe Muñoz-La Rivera
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. Applied Sciences 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 2400 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 (AI)
  • building information modeling (BIM)
  • machine learning
  • generative design
  • computer vision in construction
  • digital twins
  • smart construction
  • automation in AEC
  • predictive maintenance
  • data-driven design

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.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

20 pages, 1256 KB  
Article
Semantic Classification of Railway Bridge Drawings Based on OCR and BP Neural Networks
by Wanqi Wang, Ze Guo, Liu Bao, Xing Yang, Yalong Xie, Ruichang Shi and Shuoyang Zhao
Appl. Sci. 2026, 16(9), 4206; https://doi.org/10.3390/app16094206 (registering DOI) - 24 Apr 2026
Viewed by 149
Abstract
Digital management of modern railway bridges, a substantial part of high-speed railway networks, is often hindered by manual interpretation of construction drawings for Building Information Modeling (BIM). While individual technologies like optical character recognition (OCR) and neural networks are well-established, their generic application [...] Read more.
Digital management of modern railway bridges, a substantial part of high-speed railway networks, is often hindered by manual interpretation of construction drawings for Building Information Modeling (BIM). While individual technologies like optical character recognition (OCR) and neural networks are well-established, their generic application often fails on complex engineering documents. To address this, a domain-adaptive automatic recognition and semantic interpretation framework is proposed for railway bridge construction drawings. The novelty of this work lies in a specialized hybrid data fusion strategy that intelligently merges vector CAD file parsing with morphology-denoised OCR, resolving spatial and semantic conflicts. Furthermore, a back-propagation (BP) neural network is explicitly adapted to classify the extracted text into specific engineering categories, overcoming the challenges of dense layouts and overlapping symbols. Finally, the framework achieves end-to-end integration by transforming these semantic entities directly into structured, IFC-compatible BIM parameters. Evaluated on 250 real-world drawings, the framework achieved an average F1-score of 91.0% in semantic classification and improved processing efficiency by 6.5 times compared to manual methods. Moreover, 93.8% of the extracted entities achieved strict BIM parameter correctness, defined as seamless mapping to Revit IFC attributes without manual intervention. Full article
(This article belongs to the Special Issue Advancing Building Information Modeling Through Artificial Intelligence: Methods, Applications, and Challenges)

Review

Jump to: Research

30 pages, 3595 KB  
Review
Integrating Artificial Intelligence and BIM in Construction: Systematic Review and Quantitative Comparative Analysis
by Reinaldo Valdebenito and Eric Forcael
Appl. Sci. 2025, 15(23), 12470; https://doi.org/10.3390/app152312470 - 25 Nov 2025
Cited by 6 | Viewed by 4494
Abstract
In the transition toward a more digital and data-driven construction industry, understanding how Artificial Intelligence (AI) and Building Information Modeling (BIM) are integrated is key to planning, delivering, and operating projects effectively. This review examines recent studies to identify usage patterns of AI [...] Read more.
In the transition toward a more digital and data-driven construction industry, understanding how Artificial Intelligence (AI) and Building Information Modeling (BIM) are integrated is key to planning, delivering, and operating projects effectively. This review examines recent studies to identify usage patterns of AI and BIM. Searches were conducted on the Web of Science Core Collection from 2022 to 2025. After running a reproducible review protocol aligned with PRISMA 2020, which began with 1212 articles, and after a funneling process, 12 studies met the predefined eligibility criteria. In the present study, the synthesis was non-meta-analytic; instead, the information was analyzed by using standardized tabulation with a consistent format and compared using a two-level weighting scheme. The methodological approach combines full-text reading and descriptive coding with a reproducible weighting scheme that accounts for mentions per study and integrates them at the corpus level using open-source tools. The results show a strong focus on Deep Learning (DL), with a greater presence in Digital Twins (DT) and BIM Modeling (BIMM); Multidimensional BIM (4D/5D) appears as a secondary line, while the Common Data Environment (CDE) and Clash detection (CD) are sporadic. The coupling of DL-DT and DL-BIMM predominates. Simultaneously, Machine Learning (ML) provides explainable analysis on structured data, and Generative Adversarial Networks (GAN) and Automated Machine Learning (AutoML) with Machine Learning Operations (MLOps) act as enablers for data generation/adaptation and deployment with traceability. It is concluded that advancing metrics and shared datasets, especially for CDE and CD, along with developing reproducible workflows oriented toward MLOps, are key to scaling AI in real-world environments. Full article
(This article belongs to the Special Issue Advancing Building Information Modeling Through Artificial Intelligence: Methods, Applications, and Challenges)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop