Energy-efficient buildings have attracted vast attention as a key component of sustainable development. Thermal load analysis is a pivotal step for the proper design of heating, ventilation, and air conditioning (HVAC) systems for increasing thermal comfort in energy-efficient buildings. In this work, novel a methodology is proposed to predict the cooling load (L_C) of residential buildings based on their geometrical characteristics. Multi-layer perceptron (MLP) neural network was coupled with metaheuristic algorithms to attain its optimum hyperparameter values. According to the results, the L_C pattern can be promisingly captured and predicted by all developed hybrid models. Nevertheless, the comparison analysis revealed that the electrostatic discharge algorithm (ESDA) achieved the most powerful MLP model. Hence, utilizing the proposed methodology would give new insights into the thermal load analysis method and bridge the existing gap between the most recently developed computational intelligence techniques and energy performance analysis in the sustainable design of energy-efficient residential buildings.

Estimating the mechanical parameters of concrete is significant towards achieving an efficient mixture design. This research deals with concrete slump analysis using novel integrated models. To this end, four wise metaheuristic techniques of biogeography-based optimization (BBO), salp swarm algorithm (SSA), moth-flame optimization (MFO), and wind driven optimization (WDO) are employed to optimize a popular member of the neural computing family, namely multilayer perceptron (MLP). Four predictive ensembles are constructed to analyze the relationship between concrete slump and seven concrete ingredients including cement, water, slag, fly ash, fine aggregate, superplasticizer, and coarse aggregate. After discovering the optimal complexities by sensitivity analysis, the results demonstrated that the combination of metaheuristic algorithms and neural methods can properly handle the early prediction of concrete slump. Moreover, referring to the calculated ranking scores (RSs), the BBO-MLP (RS = 21) came up as the most accurate model, followed by the MFO-MLP (RS = 17), SSA-MLP (RS = 12), and WDO-MLP (RS = 10). Lastly, the suggested models can be promising substitutes to traditional approaches in approximating the concrete slump.

This research investigates the efficacy of a proposed novel machine learning tool for the optimal simulation of building thermal load. By applying a symbiotic organism search (SOS) metaheuristic algorithm to a well-known model, namely an artificial neural network (ANN), a sophisticated optimizable methodology is developed for estimating heating load (H_L) in residential buildings. Moreover, the SOS is comparatively assessed with several identical optimizers, namely political optimizer, heap-based optimizer, Henry gas solubility optimization, atom search optimization, stochastic fractal search, and cuttlefish optimization algorithm. The dataset used for this study lists the H_L versus the corresponding building conditions and the model tries to disclose the nonlinear relationship between them. For each mode, an extensive trial and error effort revealed the most suitable configuration. Examining the accuracy of prediction showed that the SOS–ANN hybrid is a strong predictor as its results are in great harmony with expectations. Moreover, to verify the results of the SOS–ANN, it was compared with several benchmark models employed in this study, as well as in the earlier literature. This comparison revealed the superior accuracy of the suggested model. Hence, utilizing the SOS–ANN is highly recommended to energy-building experts for attaining an early estimation of the H_L from a designed building’s characteristics.

Using ANN algorithms to address optimization problems has substantially benefited recent research. This study assessed the heating load (HL) of residential buildings’ heating, ventilating, and air conditioning (HVAC) systems. Multi-layer perceptron (MLP) neural network is utilized in association with the MVO (multi-verse optimizer), VSA (vortex search algorithm), and SOSA (self-organizing self-adaptive) algorithms to solve the computational challenges compounded by the model’s complexity. In a dataset that includes independent factors like overall height and glazing area, orientation, wall area, compactness, and the distribution of glazing area, HL is a goal factor. It was revealed that metaheuristic ensembles based on the MVOMLP and VSAMLP metaheuristics had a solid ability to recognize non-linear relationships between these variables. In terms of performance, the MVO-MLP model was considered superior to the VSA-MLP and SOSA-MLP models.

This paper attempts to identify the effective managerial factors in renovating old building tissues in a city in Iran (Langrud). The present research is practical in terms of purpose, and it is descriptive and contextual in terms of data collection. Furthermore, since this is a mixed research study from both the perspective of its nature and purpose, we conducted the study with both qualitative (interviews) and quantitative (questionnaires) methods. As the statistical population in the qualitative section consists of experts on improving worn-out tissues, we used an available sampling method and took into account the individuals’ characteristics in the sampling process. Seven managers, assistants, and engineers with more work experience than the rest of the managers and assistants were selected. During the quantitative phase, the population included all the municipality staff and the engineering system organization of Langrod city, which contains a total of 650 people. A total of 335 people were selected, and the questionnaire was distributed using Cochran’s formula. A semi-structured interview and a questionnaire were used as research tools distributed among participants. The validity and reliability of the questionnaires were determined based on existing standards. Additionally, the data were analyzed using Factor Analysis (FA), the Fuzzy Analytic Hierarchy Process (FAHP), and Structural Equations Modeling. According to the results, the most effective managerial factors and indicators in the improvement and renovation of the city’s old tissues were related to resources. Next, attention to training, commitment to environmental assessment, idea creation, planning, management, technical factors, experience, attention to legal requirements, and attention to external factors are placed.