Analyzing Green Construction Development Barriers by a Hybrid Decision-Making Method Based on DEMATEL and the ANP

There is a great deal of interest in analyzing construction development barriers to identify and rank them based on sustainability criteria and have less environmental pollution. Due to the importance of construction projects in developing countries such as Iran, this study implements a green construction development paradigm to identify and rank barriers for a case study in Tehran, Iran. The main novelty of this paper is the development of a new decision-making method using the DEMATEL and Delphi techniques and the ANP. In this regard, first of all, data collection is performed through a literature review and survey studies using questionnaires, interviews, and observations. The applied method for experts’ agreement was integrated through brainstorming and the classical Delphi method. By analyzing different economic, environmental, cultural, and social criteria using a hybrid decision-making framework, the results show that the main economic barrier with a weight of 0.2607 is ranked first, while the main feature of economic assessment is connected to the risk of investment. The cultural and social barriers, with a weight of 0.2258, ranked second, and the managerial barrier, with a weight of 0.2052, ranked third. In the social and managerial aspects, the main barriers were related to looking at green construction as luxurious and the uncertainty of green construction performance due to the climate and texture of the local area, respectively. According to the findings and results, the proposed barriers and sub-barriers in this study can be used to develop and create planning at the strategic level for the development of green construction for our case study in Tehran, Iran. With a concentration on the outcomes of the present research, the sustainable green building framework can be implemented by the application of a prioritized knowledge management concept

An Optimized Machine Learning Approach for Forecasting Thermal Energy Demand of Buildings

Recent developments in indirect predictive methods have yielded promising solutions for energy consumption modeling. The present study proposes and evaluates a novel integrated methodology for estimating the annual thermal energy demand (DAN), which is considered as an indicator of the heating and cooling loads of buildings. A multilayer perceptron (MLP) neural network is optimally trained by symbiotic organism search (SOS), which is among the strongest metaheuristic algorithms. Three benchmark algorithms, namely, political optimizer (PO), harmony search algorithm (HSA), and backtracking search algorithm (BSA) are likewise applied and compared with the SOS. The results indicate that (i) utilizing the properties of the building within an artificial intelligence framework gives a suitable prediction for the DAN indicator, (ii) with nearly 1% error and 99% correlation, the suggested MLP-SOS is capable of accurately learning and reproducing the nonlinear DAN pattern, and (iii) this model outperforms other models such as MLP-PO, MLP-HSA and MLP-BSA. The discovered solution is finally expressed in an explicit mathematical format for practical uses in the future

Analyzing Green Construction Development Barriers by a Hybrid Decision-Making Method Based on DEMATEL and the ANP

There is a great deal of interest in analyzing construction development barriers to identify and rank them based on sustainability criteria and have less environmental pollution. Due to the importance of construction projects in developing countries such as Iran, this study implements a green construction development paradigm to identify and rank barriers for a case study in Tehran, Iran. The main novelty of this paper is the development of a new decision-making method using the DEMATEL and Delphi techniques and the ANP. In this regard, first of all, data collection is performed through a literature review and survey studies using questionnaires, interviews, and observations. The applied method for experts’ agreement was integrated through brainstorming and the classical Delphi method. By analyzing different economic, environmental, cultural, and social criteria using a hybrid decision-making framework, the results show that the main economic barrier with a weight of 0.2607 is ranked first, while the main feature of economic assessment is connected to the risk of investment. The cultural and social barriers, with a weight of 0.2258, ranked second, and the managerial barrier, with a weight of 0.2052, ranked third. In the social and managerial aspects, the main barriers were related to looking at green construction as luxurious and the uncertainty of green construction performance due to the climate and texture of the local area, respectively. According to the findings and results, the proposed barriers and sub-barriers in this study can be used to develop and create planning at the strategic level for the development of green construction for our case study in Tehran, Iran. With a concentration on the outcomes of the present research, the sustainable green building framework can be implemented by the application of a prioritized knowledge management concept

A Partial Least Squares Structural Equation Modelling Analysis of the Primary Barriers to Sustainable Construction in Iran

This paper outlines the obstacles to sustainable construction growth in Iran and thereafter examines the effect and relation between these barriers and the direction of sustainable construction growth as one of the essential objectives for achieving sustainable cities and infrastructure. The study is applied for research purposes that are based on descriptive survey data gathering and correlational data analysis techniques. The statistical population for this study consists of 120 construction-related engineers and university professors who were assessed on a five-point Likert scale. Using SmartPLS software version 4, the responses to the questionnaire were examined. The Kolmogorov–Smirnov assessment was utilized to evaluate the normalcy of the variables, as this assessment is typically employed for this purpose. For data analysis, the PLS (partial least squares) method was used, while SEM (structural equation modeling) methods have been used to assess the study hypotheses. Cronbach’s alpha and the composite reliability coefficient (CR) were applied to determine the instrument’s viability, and the results show that the coefficient connected to all variables is above 7.0, which is an acceptable value. The AVE (average variance extracted) was also used to evaluate the questionnaire’s validity, which was greater than 0.4 and deemed acceptable for coefficients of significance (T-values), coefficient of predictive power (Q2), and coefficient of determination (R2). The obtained results support and confirm all research hypotheses, including that the identified obstacles directly affect the performance of sustainable construction. According to the results of the Friedman test, the legal restrictions variable (CL) is the most significant obstacle to sustainable construction in Iran, with a rank of 4.24. The indicators of political limits (CP) and social and cultural constraints (CSC) came in at second and third, respectively. The results could help government officials make better decisions about where to focus their attention and how to distribute scarce resources

[In Press] A novel approach for optimized design of low-E windows and visual comfort for residential spaces

Double low-E glasses are effective and well-established choices for residential buildings in temperate climatic regions of Sydney, Australia, and Tehran, Iran. The current study's measurements and field experiments have shown that using a double low-E windowpane can improve window total transmitted radiation energy (TSRE) and daylight glare factor. Nevertheless, spatial daylight autonomy (sDA) and daylight illuminance are the shortcomings of using double-low-E glasses. These implications demonstrated that using double low-E glazing is a double-edged sword. Despite its efficiency in improving energy consumption, it cannot satisfy daylight comfort requirements. Therefore, this research intends to find the most suitable solution to exploit double-low-E glasses' benefits and avoid their drawbacks. Subsequently, the genetic algorithm has been used to find the optimum window size through a multi-objective simulation by Climate Studio. The findings suggest that the optimum WWR of 10.35%–10.99% in Tehran brings the daylight comfort metrics above the threshold while the energy consumption metrics are kept at a minimum. Similarly, for Sydney, these measures are 20%–24% room length for the horizontal dimension of a window and 33%–40% room height for the vertical penetration dimension. In this way, using a double low-E window pane is justifiable for both examined regions