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Applied Computer Methods in Building Engineering

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 February 2026 | Viewed by 348

Special Issue Editors


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Guest Editor
Department of Architecture, Mokwon University, Daejeon 353499, Republic of Korea
Interests: deep learning; artificial intelligence in construction; smart construction and management; modular construction; off-site construction

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Guest Editor
Department of Smart Convergence Engineering, Hanyang University ERICA, Ansan 15588, Republic of Korea
Interests: construction management; smart construction; drones; artificial intelligence in construction; digital twin; digital diagnosis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Digital technologies are changing the way that we design, build, and manage buildings. As part of the ongoing shift toward Industry 4.0, tools such as building information modeling (BIM), automation, artificial intelligence (AI), and the Internet of Things (IoT) are being widely adopted in the construction industry. These technologies are helping to improve productivity, enhance safety, reduce costs, and support sustainable practices across all phases of a building’s life cycle.

In this digital era, computer-based methods are becoming essential in building engineering. From design and simulation to construction planning and facility management, digital tools are enabling smarter decision-making and better project outcomes. They also support collaboration among architects, engineers, contractors, and building operators.

This Special Issue requests original research articles, case studies, and reviews that explore how computer methods are being applied in building engineering. We encourage contributions from both academic researchers and industry professionals. Our goal is to share innovative ideas, practical solutions, and new developments that advance the field and promote smarter construction practices.

Prof. Dr. Hyunkyu Shin
Prof. Dr. Sanghyo Lee
Guest Editors

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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. 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

  • building information modeling (BIM) and digital twins
  • computational design and performance analysis
  • automation in design, planning, and construction
  • artificial intelligence and machine learning for building systems
  • smart building operations and facility management
  • sensor technologies and real-time monitoring
  • predictive maintenance using data-driven approaches
  • visualization, AR/VR, and digital workflows
  • human–computer interaction in building environments
  • data analytics and decision support tools in construction

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Published Papers (1 paper)

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Research

21 pages, 2898 KB  
Article
Natural Language Processing-Based Model for Litigation Outcome Prediction: Decision-Making Support for Residential Building Defect Alternative Dispute Resolution
by Chang-won Jung, Jae-jun Kim and Joo-sung Lee
Appl. Sci. 2025, 15(21), 11565; https://doi.org/10.3390/app152111565 - 29 Oct 2025
Viewed by 183
Abstract
Defects occurring during the maintenance phase of residential buildings not only undermine the quality of life of residents but also lead to disputes with contractors, which often escalate into litigation rather than being resolved through alternative dispute resolution (ADR), thereby increasing social and [...] Read more.
Defects occurring during the maintenance phase of residential buildings not only undermine the quality of life of residents but also lead to disputes with contractors, which often escalate into litigation rather than being resolved through alternative dispute resolution (ADR), thereby increasing social and economic burdens. While previous studies have mainly focused on identifying the causes of defects, developing classification systems, and improving institutional frameworks, few have sought to predict litigation outcomes from precedent data to support decision-making during pre-litigation dispute resolution. This paper proposes a natural language processing-based multimodal and multitask prediction model that learns from precedent data using information available prior to litigation, such as the claims and evidence of plaintiffs and defendants and the claimed amounts. The proposed model simultaneously predicts judgment outcomes and grant ratios in defect-related disputes and can help to enhance the persuasiveness and voluntariness of ADR by informing parties about the likelihood of settlement and the potential risks of litigation. Furthermore, this paper proposes a decision-support framework for rational and evidence-based dispute resolution which can reduce stakeholder uncertainty and ultimately lower the frequency of litigation related to residential building defects. Full article
(This article belongs to the Special Issue Applied Computer Methods in Building Engineering)
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