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Information
Special Issues
AI Applications in Construction and Infrastructure
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AI Applications in Construction and Infrastructure

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 15 September 2024 | Viewed by 1756

Special Issue Editor

Dr. Sudipta Chowdhury

E-Mail Website
Guest Editor
Department of Mechanical and Industrial Engineering, Marshall University, Huntington, WV 25755, USA
Interests: system resilience; system restoration modeling; disaster management; supply chain and logistics; extended reality

Special Issue Information

Dear Colleagues,

The journal Information is pleased to announce a Special Issue on "AI Applications in Construction and Infrastructure." This Special Issue aims to explore the advancements, challenges, and potential of artificial intelligence (AI) in revolutionizing the construction industry and infrastructure development.

AI is a transformative technology with the potential to streamline and enhance construction and infrastructure projects. By leveraging AI algorithms, machine learning, robotics, and data analytics, innovative solutions can be developed to improve the efficiency, productivity, safety, and sustainability of construction.

This Special Issue seeks high-quality, original research articles, reviews, technical notes, and case studies that address the utilization of AI in diverse areas related to construction and infrastructure. Topics of interest include, but are not limited to:

  • AI-assisted project planning and design;
  • Robotics and automation in construction tasks;
  • AI-powered monitoring and surveillance systems;
  • Safety and risk management using AI techniques;
  • AI-driven optimization in construction logistics and supply chain management;
  • Energy efficiency and sustainability using AI applications;
  • AI-based predictive maintenance and equipment optimization;
  • Virtual reality and augmented reality for construction visualization and simulation;
  • AI-enabled decision support systems for construction management;
  • Ethical, legal, and social implications of AI adoption in construction.

We look forward to receiving your valuable contributions and disseminating the latest research findings on AI applications in construction and infrastructure in this Special Issue of Information.

Dr. Sudipta Chowdhury
Guest Editor

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. Information 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 1600 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)
  • AI algorithms
  • AI decision support systems
  • construction
  • infrastructure design, planning, and monitoring
  • system-of-systems (SoS)

Published Papers (3 papers)

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Research

22 pages, 1014 KiB  
Article
The Application of Machine Learning in Diagnosing the Financial Health and Performance of Companies in the Construction Industry
by Jarmila Horváthová, Martina Mokrišová and Alexander Schneider
Information 2024, 15(6), 355; https://doi.org/10.3390/info15060355 - 14 Jun 2024
Viewed by 199
Abstract
Diagnosing the financial health of companies and their performance is currently one of the basic questions that attracts the attention of researchers and experts in the field of finance and management. In this study, we focused on the proposal of models for measuring [...] Read more.
Diagnosing the financial health of companies and their performance is currently one of the basic questions that attracts the attention of researchers and experts in the field of finance and management. In this study, we focused on the proposal of models for measuring the financial health and performance of businesses. These models were built for companies doing business within the Slovak construction industry. Construction companies are identified by their higher liquidity and different capital structure compared to other industries. Therefore, simple classifiers are not able to effectively predict their financial health. In this paper, we investigated whether boosting ensembles are a suitable alternative for performance analysis. The result of the research is the finding that deep learning is a suitable approach aimed at measuring the financial health and performance of the analyzed sample of companies. The developed models achieved perfect classification accuracy when using the AdaBoost and Gradient-boosting algorithms. The application of a decision tree as a base learner also proved to be very appropriate. The result is a decision tree with adequate depth and very good interpretability. Full article
(This article belongs to the Special Issue AI Applications in Construction and Infrastructure)
21 pages, 5173 KiB  
Article
Knowledge-Driven and Diffusion Model-Based Methods for Generating Historical Building Facades: A Case Study of Traditional Minnan Residences in China
by Sirui Xu, Jiaxin Zhang and Li Yunqin
Information 2024, 15(6), 344; https://doi.org/10.3390/info15060344 - 11 Jun 2024
Viewed by 182
Abstract
The preservation of historical traditional architectural ensembles faces multifaceted challenges, and the need for facade renovation and updates has become increasingly prominent. In conventional architectural updating and renovation processes, assessing design schemes and the redesigning component are often time-consuming and labor-intensive. The knowledge-driven [...] Read more.
The preservation of historical traditional architectural ensembles faces multifaceted challenges, and the need for facade renovation and updates has become increasingly prominent. In conventional architectural updating and renovation processes, assessing design schemes and the redesigning component are often time-consuming and labor-intensive. The knowledge-driven method utilizes a wide range of knowledge resources, such as historical documents, architectural drawings, and photographs, commonly used to guide and optimize the conservation, restoration, and management of architectural heritage. Recently, the emergence of artificial intelligence-generated content (AIGC) technologies has provided new solutions for creating architectural facades, introducing a new research paradigm to the renovation plans for historic districts with their variety of options and high efficiency. In this study, we propose a workflow combining Grasshopper with Stable Diffusion: starting with Grasshopper to generate concise line drawings, then using the ControlNet and low-rank adaptation (LoRA) models to produce images of traditional Minnan architectural facades, allowing designers to quickly preview and modify the facade designs during the renovation of traditional architectural clusters. Our research results demonstrate Stable Diffusion’s precise understanding and execution ability concerning architectural facade elements, capable of generating regional traditional architectural facades that meet architects’ requirements for style, size, and form based on existing images and prompt descriptions, revealing the immense potential for application in the renovation of traditional architectural groups and historic districts. It should be noted that the correlation between specific architectural images and proprietary term prompts still requires further addition due to the limitations of the database. Although the model generally performs well when trained on traditional Chinese ancient buildings, the accuracy and clarity of more complex decorative parts still need enhancement, necessitating further exploration of solutions for handling facade details in the future. Full article
(This article belongs to the Special Issue AI Applications in Construction and Infrastructure)
34 pages, 10124 KiB  
Article
Fuzzy Integrated Delphi-ISM-MICMAC Hybrid Multi-Criteria Approach to Optimize the Artificial Intelligence (AI) Factors Influencing Cost Management in Civil Engineering
by Hongxia Hu, Shouguo Jiang, Shankha Shubhra Goswami and Yafei Zhao
Information 2024, 15(5), 280; https://doi.org/10.3390/info15050280 - 14 May 2024
Viewed by 688
Abstract
This research paper presents a comprehensive study on optimizing the critical artificial intelligence (AI) factors influencing cost management in civil engineering projects using a multi-criteria decision-making (MCDM) approach. The problem addressed revolves around the need to effectively manage costs in civil engineering endeavors [...] Read more.
This research paper presents a comprehensive study on optimizing the critical artificial intelligence (AI) factors influencing cost management in civil engineering projects using a multi-criteria decision-making (MCDM) approach. The problem addressed revolves around the need to effectively manage costs in civil engineering endeavors amidst the growing complexity of projects and the increasing integration of AI technologies. The methodology employed involves the utilization of three MCDM tools, specifically Delphi, interpretive structural modeling (ISM), and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). A total of 17 AI factors, categorized into eight broad groups, were identified and analyzed. Through the application of different MCDM techniques, the relative importance and interrelationships among these factors were determined. The key findings reveal the critical role of certain AI factors, such as risk mitigation and cost components, in optimizing the cost management processes. Moreover, the hierarchical structure generated through ISM and the influential factors identified via MICMAC provide insights for prioritizing strategic interventions. The implications of this study extend to informing decision-makers in the civil engineering domain about effective strategies for leveraging AI in their cost management practices. By adopting a systematic MCDM approach, stakeholders can enhance project outcomes while optimizing resource allocation and mitigating financial risks. Full article
(This article belongs to the Special Issue AI Applications in Construction and Infrastructure)
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