Journal of Civil Engineering and Management

Quality and reliability of IFC/BIM models for public educational facilities construction projects via clash detection

Michał Juszczyk — 2025-01-20

This study investigates the reliability of monodiscipline IFC/BIM models in public construction projects of educational facilities through advanced clash detection and quantitative analysis. Data were collected from BIM models of two kindergartens and a school in Vilnius, Lithuania, representing different design disciplines. A mixed-methods approach was employed to analyse the number, types, and geometric characteristics of detected clashes. The research introduces innovative metrics, such as the Relative Quality Coefficient (RQC), Relative Uncertainty Coefficient (RUC), and Modified Relative Quality Coefficient (MRQC), to assess model quality and reliability quantitatively. The findings reveal a direct relationship between model complexity, clash detection precision, and the number of identified clashes, underscoring the importance of enhanced quality control measures in IFC/BIM models for public procurement. The study concludes that the implementation of these novel metrics can enhance the reliability of IFC/BIM models, thereby optimizing the design and construction process.

Impacts of human resources management strategies and practices on workers performance in construction industry: a review

Mohammed N. Maliha — 2025-01-29

Human resource management (HRM) plays a vital role in the growth and sustainability of companies and in achieving company objectives, as HRM relates to the workers’ practices and their functional roles. The main objective of this paper is to identify the HRM strategies, practices and their impact on worker’s performance in the Architecture, Engineering, and Construction (AEC) Industry. The method which was used in this study was based on three stages. After the completion of the database and web engine search, the total number of sources found were 149. Next, the sources’ titles and abstracts were reviewed and those marked as relevant to the review were chosen to be retrieved and thoroughly reviewed. The sources were chosen based on the following inclusion criteria: (a) the sources implied in HRM strategies in the AEC industry, (b) the sources released between 2010 and 2023, (c) the online sources, and (d) the English-language sources. Also, the selected sources are reviewed to extract the factors using the content analysis method which is a thorough and systematic study of the contents of a specific body of material. In addition, content analysis is carried out to extract practices of HRM strategies and their impact on worker performance. This can be followed up and observed through the figures and tables that come later. As a final result, which concludes the outputs of the previous stages which results in a first-round total of 31 HRM strategies practice but in the second-round result of reaching 39 different sources from 149 related sources. After that, discussing the most popular between them based on the appearance of it in the sources which reviewed beside that, the impact of it on worker’s performance were viewed in the AEC industry. Based on that, it was found the following 5 categories of strategies: (1) Human Resources Planning; (2) Polarization and Recruitment; (3) Training and Development; (4) Human Resources Following-up; (5) Career Planning. At the same time the most popular practices are based on appearance in sources: In terms of Human Resources Planning Strategy, “The company’s management analyses the functions accurately”. In Polarization and Recruitment Strategy, “The company’s internal resources are the best to provide its human resource needs” and “Selection and recruitment policies are in line with the company’s current and future needs”. In Training and Development of Human Resources Strategy, “The company evaluates the results of development and training programs to achieve the purpose of feedback”. In Human Resources Following-up Strategy, “There is a fair incentive system in the company”. In Career Planning Strategy, “There is a clear description of the relationship of workers to each other”. Finally, human resource management strategies and practices are one of the major players in the AEC industry, which is evidenced by its impact on the performance of workers.

First published online 22 October 2024

A novel hybrid model for predicting the bearing capacity of piles

Li Tao — 2025-01-29

Due to the uncertainty of soil condition and pile design characteristics, it is always a challenge for geotechnical engineers to accurately determine the bearing capacity of piles. The main objective of this study is to propose a hybrid model coupling least squares support vector machine (LSSVM) with an improved particle swarm optimization (IPSO) algorithm for the prediction of bearing capacity of piles. The improved PSO algorithm was used to optimize the LSSVM hyperparameters. The performance of the IPSO-LSSVM model was compared with seven artificial intelligence models, namely adaptive neuro-fuzzy inference system (ANFIS), M5 model tree (M5MT), multivariate adaptive regression splines (MARS), gene expression programming (GEP), random forest (RF), regression tree (RT) and a stacked ensemble model. Six statistical indices (e.g., coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), relative root mean squared error (RRMSE), BIAS and discrepancy ratio (DR)) were used to evaluate the performance of the models. The R2, MAE, RMSE, RRMSE and BIAS values of the IPSO-LSSVM model were 1, 4.27 kN, 6.164 kN, 0.005 and 0, respectively, for the training datasets and 0.9977, 22 kN, 36.03 kN, 0.0275 and –11, respectively, for the testing datasets. Compared with the ANFIS, MARS, GEP, M5MT, RF, RT and the stacked ensemble models, the proposed IPSO-LSSVM model shows high accuracy and robustness on the test datasets. In addition, the sensitivity, uncertainty, reliability and resilience of the IPSO-LSSVM model were also analyzed in this study.

First published online 22 October 2024

Electromagnetic wave-driven deep learning for structural evaluation of reinforced concrete strength

Alan Putranto — 2025-01-29

Monitoring the performance of reinforced concrete structures, particularly in terms of strength reduction, presents significant challenges due to the practical limitations of traditional detection methods. This study introduces an innovative framework that incorporates a non-destructive technique using electromagnetic waves (EM-waves) transmitted via Radio Frequency Identification (RFID) technology, combined with two-dimensional (2-D) Fourier transform, fractal dimension analysis, and deep learning techniques to predict reductions in structural strength. Experiments were conducted on three reinforced concrete beam (RCB) specimens exhibiting various levels of reinforcement corrosion. From these, a dataset of 1,800 EMwave images was generated and classified into “normal” and “reduced strength” categories. These categories were used to train and validate a Convolutional Neural Network (CNN), which demonstrated robust performance, achieving a high accuracy of 0.91 and an F1-score of 0.93 in classifying instances of reduced structural strength. This approach offers a promising solution for detecting strength reduction in reinforced concrete infrastructures, enhancing both safety and maintenance efficiency.

First published online 5 November 2024

A regression-based model for parametric cost estimation of industrial steel structures

Adel Alshibani — 2025-01-29

Construction industry is considered one of the most versatile industries characterized by uncertainties and risk. Estimating the steel structure cost of industrial buildings is a challenging task compared with traditional buildings due to the uniqueness of this class of projects. This paper aims to introduce an effective and accurate parametric model for construction cost estimation of industrial steel structures. The paper proposes a regression-based model for estimating the cost of a critical construction component: the industrial steel structure where the is not enough historical data is available. The factors that affect the construction cost of industrial steel structures are initially identified based on the literature and interviews with local experts. The correlation between input factors and model’s output is then investigated. In addition, sensitivity analysis is performed to examine the relative importance of the regression model’s inputs. The model is validated using actual data on industrial steel structure costs in Saudi Arabia. The model adequately predicted the construction costs of actual projects with an accuracy of more than 88%. This indicates that the model is capable of accurately predicting the cost of such structures. The proposed model can be of great assistance to investors and decision-makers looking to invest in the industrial sector.

First published online 10 December 2024

Do actors’ incentives obstruct sector-wide long-term productivity in the design and production of bridges in Sweden?

Johan Lagerkvist — 2025-01-29

An increase in productivity is necessary to reduce economic costs in bridge projects. Previous research indicates that construction productivity has decreased since the 1960s. A quantitative study was performed to find out how the incentives of the three major actors (client, contractor, and design engineer) could be obstacles to long-term productivity in the Swedish bridge construction industry. The study was performed as a self-completed questionnaire and received 151 responses. The results show that the contractors’ employees find profit in a single project more important than the company’s profit over time. Thus, the project´s incentives obstruct innovation and standardization, which could benefit future projects and thereby increase long-term productivity and the company’s profit over time. In contrast to contractors, design engineers and clients value company profit more than profit in a single project, and they value the quality of delivered products as the most important factor for increased long-term productivity.

First published online 10 December 2024