Journal of Civil Engineering and Management

Estimates of construction infrastructure stock for Cape Verde: 1980–2019

Jorge Lopes — 2024-04-15

Building and other construction assets constitute a significant part of a country’s physical and economic infrastructure. According to several writers, the knowledge of reliable data of building and other construction assets of a specific country or region is a crucial element for the long-term management of these assets. Built capital stock statistics at the national or international levels have been available for most countries of the world, both developed and less developed ones, for some time, but construction infrastructure stock statistics at the disaggregated level are very scarce, even for most developed countries. Furthermore, the methodologies to produce the estimates of built capital stock, at the international level, do not consider countries’ specificities. This paper discusses the methodologic issues for producing construction infrastructure stock statistics for Cape Verde, and makes estimates for the period 1980–2019. The paper outlines the Perpetual Inventory Method (PIM) used to produce capital estimation, data employed, and the assumptions made to estimate missing data. The paper analyses the level of the construction infrastructure stock estimates for Cape Verde, as well as their impact on the development pattern of the country’s construction industry, and suggests how further studies can enhance our comprehension of the relationship between construction investment and economic growth and development.

Integrating evolutionary game and system dynamics for multi-player safety regulation of major infrastructure projects in China

Xiaolong Xue — 2024-04-19

Aiming at safety regulation in the operation of major infrastructure projects (MIPs) to prevent potential risk loss and adverse social impacts, this research presents a novel model integrating evolutionary game and system dynamics (SD) for optimizing safety regulation strategies with different stakeholders involving the operating company (OC), government section (GS), and public under the bounded rationality, where the evolutionary game theory is applied to describe the interactions among stakeholders in the safety regulation of MIPs followed by simulating through adopting the SD to analyze the effects of different strategies on equilibrium solutions and the stability of game equilibrium. In view of the simulation results based on five scenarios, the dynamic penalty-incentive scenario not only effectively restrains the fluctuations of the strategy selection, but also provides an ideal evolutionary stable strategy, in which the OC could nearly choose to comply with the regulations, while the public could nearly choose to supervise the OC as their optimal strategy to prevent risks. All results indicate that the application of the evolutionary game with the SD model is an effective way to analyze the effects of different strategies and provide effective solutions to study complex multi-player game problems. Overall, this research contributes to developing an evolutionary game with the SD model for the safety regulation of MIPs, which can serve as a platform to identify reasonable regulatory strategies with great practical application.

Characteristics analysis for high-rise buildings during top-down construction

Yuanhang Wang — 2024-04-23

New advances in top-down construction are developing with the construction of supertall buildings in China. This study presents a finite element analysis for high-rise structural construction with a top-down method (TDM) considering complex environmental conditions. Based on this analysis model, the forces and the deformation of the diaphragm wall, beams, and soldier piles at various stages of construction are computed. Taking a super high-rise building with a 5-story basement in Nanjing as an example, the reliability and accuracy of the model is verified by comparing the measured and simulated results of displacement and stress values at various locations. The research results reveal the relationship between excavation depth, soil settlement and pile displacement, which is convenient for finding the optimal construction critical surface. It lays a foundation for the study of the critical height of the subsequent construction and facilitates the prediction of the weak link in the process. At present, this project is under construction, so this study has reference value for subsequent construction projects.

Knowledge dissemination trajectory of BIM in construction engineering applications

Jieh-Haur Chen — 2024-05-10

In recent years, the construction industry worldwide has shown significant interest in Building Information Modeling (BIM). This study aims to analyze the dissemination of knowledge about BIM in construction engineering applications using Main Path Analysis (MPA). The research sample comprises 3,761 papers related to BIM’s application in the construction industry, sourced from the ISI Web of Science database. Initially, we investigate trends in paper publications, conduct country and journal analyses, and examine author statistics. Subsequently, we calculate traversal counts along the search path links to reveal the development trajectory of BIM. The trajectory of BIM’s evolution in the construction industry can be divided into four stages as identified through the global key-route main path analysis: 1) BIM standardization; 2) Integration of completed building projects using BIM; 3) BIM applications in precast construction projects; and 4) BIM applications in land management. These findings provide a clear understanding of how BIM has been applied and evolved within the construction industry.

Multi-objective green design model based on costs, CO2 emissions and serviceability for high-rise buildings with a mega-structure system

Jewoo Choi — 2024-05-17

In light of growing environmental concerns, the reduction of CO₂ emissions is increasingly vital. Particularly in the construction industry, a major contributor to global carbon emissions, addressing this issue is critical for environmental sustainability and mitigating the accelerating impacts of climate change. This study proposes the Optimal Green Design Model for Mega Structures (OGDMM) to optimise CO₂ emissions, cost-effectiveness, and serviceability in highrise buildings with mega structures. The OGDMM examines the impact of each material and structural design of main members on these three critical aspects. Analytical results for high-rise buildings (120–200 m, slenderness ratio: 2.0–8.0) demonstrate that OGDMM can reduce CO₂ emissions and costs by an average of 4.67% and 3.97%, respectively, without compromising serviceability. To ensure comprehensive evaluation, this study introduces five new evaluation indicators encompassing environmental, economic, and serviceability performances of high-rise buildings. Based on these criteria, optimised structural designs for high-rise buildings are classified into four categories according to slenderness ratio, leading to the formulation of corresponding design guidelines. The model’s applicability is further validated through its application to a 270-m-tall high-rise building in Korea, showing reductions in CO₂ emissions and costs by 8.99% and 18.50%, respectively, while maintaining structural serviceability.

Application of machine learning models and GSA method for designing stud connectors

Guorui Sun — 2024-05-17

The design of stud connectors is aided by determining the relationship between shear strength and the input variables (number, diameter, height, tensile strength and elastic modulus of the studs, and compressive strength and elastic modulus of the concrete) that influence strength. Since strength is nonlinearly related to the influencing variables, which makes the predictions of the relevant empirical equations unreliable, the use of machine learning (ML) models is preferred. The prediction results of eight machine learning models were evaluated, including linear regression (LR1), ridge regression (RR), lasso regression (LR2), back-propagation artificial neural network (BP ANN), genetic algorithm optimized BP ANN (GA-BP ANN), extreme learning machines (ELM), random forests (RF), and support vector machines (SVM). The results show that the GA-BP ANN model is the most accurate model for prediction with a mean absolute percentage error (MAPE) of 6.17% and an R² of 0.9599. Based on the GA-BP ANN model and the global sensitivity analysis (GSA) method, a new parameter importance analysis method was developed to compare the magnitude of the effect of different input variables on strength. It was found that stud diameter had the greatest effect on shear strength.