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Computation
Special Issues
Applications of Intelligent Computing and Modeling in Construction Engineering
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Applications of Intelligent Computing and Modeling in Construction Engineering

A special issue of Computation (ISSN 2079-3197).

Deadline for manuscript submissions: 30 September 2025 | Viewed by 3602

Special Issue Editors

Dr. Jerzy Rosłon

E-Mail Website
Guest Editor
Civil Engineering Faculty, Warsaw University of Technology, 00-637 Warszawa, Poland
Interests: optimization; construction; education; schedules; sustainability
Special Issues, Collections and Topics in MDPI journals
Dr. Michał Podolski

E-Mail Website
Guest Editor
Faculty of Civil Engineering, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
Interests: scheduling theory in construction projects and construction production; applications of artificial intelligence methods; optimization; construction
Dr. Bartłomiej Sroka

E-Mail Website
Guest Editor
Faculty of Civil Engineering, Cracow University of Technology, 31-155 Kraków, Poland
Interests: scheduling theory in construction projects; optimization

Special Issue Information

Dear Colleagues,

The construction engineering field is increasingly integrating intelligent computing and advanced modeling techniques to enhance design, planning, and execution processes. The application of intelligent computing, such as artificial intelligence (AI), machine learning (ML), and optimization algorithms, is transforming how complex engineering challenges are addressed, leading to more efficient, cost-effective, and sustainable construction practices. This Special Issue, "Applications of Intelligent Computing and Modeling in Construction Engineering", aims to showcase the latest research and developments in this domain.

Contributions are invited that explore the use of computational tools for structural analysis, the predictive modeling of construction materials, the optimization of project management, and real-time monitoring systems for construction sites. Research that leverages AI and ML for improving construction safety, automation in building processes, and sustainable construction practices is particularly encouraged. Methodological studies, original research, and reviews that address challenges in implementing intelligent computing in construction environments or present state-of-the-art modeling techniques are welcome.

Topics of interest include, but are not limited to, the following:

  • AI-based structural health monitoring systems;
  • ML algorithms for predictive maintenance and failure detection;
  • Computational models for sustainable construction materials;
  • Simulation of building information modeling (BIM) processes;
  • Automated construction techniques using robotics and AI;
  • Optimization in construction project management using intelligent tools;
  • Real-time data analysis and decision support systems in construction.

This Special Issue aims to advance the understanding and application of intelligent computing in construction engineering, fostering innovation and improving efficiency, safety, and sustainability in the industry.

Dr. Jerzy Rosłon
Dr. Michał Podolski
Dr. Bartłomiej Sroka
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. Computation 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 1800 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

  • intelligent computing
  • construction engineering
  • artificial intelligence (AI)
  • machine learning (ML)
  • structural analysis
  • predictive modeling
  • optimization algorithms
  • building information modeling (BIM)
  • automation in construction
  • sustainable construction

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

Published Papers (3 papers)

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Research

39 pages, 8637 KiB  
Article
Comparative Analysis of Machine Learning Models for Predicting Interfacial Bond Strength of Fiber-Reinforced Polymer-Concrete
by Miljan Kovačević, Marijana Hadzima-Nyarko, Predrag Petronijević, Tatijana Vasiljević and Miroslav Radomirović
Computation 2025, 13(1), 17; https://doi.org/10.3390/computation13010017 - 17 Jan 2025
Cited by 1 | Viewed by 908
Abstract
This study presents a detailed analysis of various machine learning models for predicting the interfacial bond strength of fiber-reinforced polymer (FRP) concrete, including multiple linear regression, Multigene Genetic Programming (MGGP), an ensemble of regression trees, Gaussian Process Regression (GPR), Support Vector Regression (SVR), [...] Read more.
This study presents a detailed analysis of various machine learning models for predicting the interfacial bond strength of fiber-reinforced polymer (FRP) concrete, including multiple linear regression, Multigene Genetic Programming (MGGP), an ensemble of regression trees, Gaussian Process Regression (GPR), Support Vector Regression (SVR), and neural networks. The evaluation was based on their predictive accuracy. The optimal model identified was the GPR ARD Exponential model, which achieved a mean absolute error (MAE) of 1.8953 MPa and a correlation coefficient (R) of 0.9658. An analysis of this optimal model highlighted the most influential variables affecting the bond strength. Additionally, the research identified several models with lower expression complexity and reduced accuracy, which may still be applicable in practical scenarios. Full article
(This article belongs to the Special Issue Applications of Intelligent Computing and Modeling in Construction Engineering)
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20 pages, 3382 KiB  
Article
Optimization and Prediction of the Mechanical Properties of Concrete with Crumb Rubber and Stainless-Steel Fibers Under Varying Temperatures
by Ayman El-Zohairy and Osman Hamdy
Computation 2025, 13(1), 14; https://doi.org/10.3390/computation13010014 - 9 Jan 2025
Viewed by 625
Abstract
This research develops an equation to describe the relationship between stress (σ) and strain (ε) in concrete under different conditions. It includes important parameters from earlier studies to improve predictions of stress–strain behavior, especially for concrete with crumb rubber and stainless-steel fibers at [...] Read more.
This research develops an equation to describe the relationship between stress (σ) and strain (ε) in concrete under different conditions. It includes important parameters from earlier studies to improve predictions of stress–strain behavior, especially for concrete with crumb rubber and stainless-steel fibers at various temperatures. The initial phase assessed three existing stress–strain formulas as a basis for optimization. Using the Genetic Algorithm (GA) and the Whale Optimization Algorithm (WOA), a new equation was created to simulate the stress–strain relationship while considering temperature changes and material additions. Results showed that Formula (1), optimized with the WOA, performed much better than other polynomial and exponential formulas, proving the WOA’s effectiveness over the traditional GA. A comparison of the mechanical properties from experiments and those predicted by the new formula showed a high level of accuracy. Key properties like the maximum stress, strain at maximum stress, modulus of elasticity, and toughness were well captured. The findings highlight how temperature and material composition significantly affect concrete’s mechanical behavior. Overall, this research offers important insights into the factors influencing concrete performance, providing a solid framework for future studies and practical applications in engineering and construction. The proposed formula is a reliable tool for predicting concrete’s mechanical properties under various conditions, which aids in better modeling and optimization in concrete design. Full article
(This article belongs to the Special Issue Applications of Intelligent Computing and Modeling in Construction Engineering)
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21 pages, 2773 KiB  
Article
Comparative Analysis of Energy Efficiency in Conventional, Modular, and 3D-Printing Construction Using Building Information Modeling and Multi-Criteria Decision-Making
by Abdullah Al Masri, Assed N. Haddad and Mohammad K. Najjar
Computation 2024, 12(12), 247; https://doi.org/10.3390/computation12120247 - 18 Dec 2024
Cited by 1 | Viewed by 1689
Abstract
Energy efficiency has become a crucial focus with the growing attention on sustainable development and decreasing energy consumption in the built environment. Different construction methods are being applied worldwide, such as conventional, modular, and 3D-printing methods, to increase energy efficiency in buildings. This [...] Read more.
Energy efficiency has become a crucial focus with the growing attention on sustainable development and decreasing energy consumption in the built environment. Different construction methods are being applied worldwide, such as conventional, modular, and 3D-printing methods, to increase energy efficiency in buildings. This study aims to enhance the decision-making process by identifying optimal construction techniques, material selection, and ventilation window dimensions to promote sustainable energy use in buildings. A novel framework combining Building Information Modeling (BIM), computational analysis, and Multi-Criteria Decision-Making (MCDM) approaches is applied to assess the energy use intensity (EUI), annual electric energy consumption, and lifecycle energy cost across multiple sequences for each type of construction. Computational analysis in this research is combined in two main tools. Minitab is utilized for experimental design to determine the number and configurations of sequences analyzed. The Simple Additive Weighting (SAW) method, applied as an MCDM tool, is used to assess and rank the performance of sequences based on equally weighted criteria. Subsequently, 3D models of case study buildings are developed, and energy simulations are conducted using Autodesk Revit and Autodesk Green Building Studio, respectively, as BIM tools to compare the energy performance of various design alternatives. The results revealed that 3D printing surpassed other methods, where Sequence 7 achieved approximately 10.3% higher efficiency than modular methods and 40.5% better performance than conventional methods in the evaluated criteria. The findings underscore the higher energy efficiency of 3D printing, followed by modular construction as a competitive method, while conventional methods lagged significantly. Full article
(This article belongs to the Special Issue Applications of Intelligent Computing and Modeling in Construction Engineering)
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