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This study proposes a machine learning (ML) model to predict the displacement response of high-rise structures under various vertical and lateral loading conditions. The study combined finite element analysis (FEA), parametric modeling, and a multi-objective genetic algorithm to create a robust and diverse dataset of loading scenarios for developing a predictive ML model. The ML model was trained using a recurrent neural network (RNN) with Long Short-Term Memory (LSTM) layers. The developed model demonstrated high accuracy in predicting time series of vertical, lateral (X), and lateral (Y) displacements. The training and testing results showed Mean Squared Errors (MSE) of 0.1796 and 0.0033, respectively, with R2 values of 0.8416 and 0.9939. The model’s predictions differed by only 0.93% from the actual vertical displacement values and by 4.55% and 7.35% for lateral displacements in the Y and X directions, respectively. The results demonstrate the model’s high accuracy and generalization ability, making it a valuable tool for structural health monitoring (SHM) in high-rise buildings. This research highlights the potential of ML to provide real-time displacement predictions under various load conditions, offering practical applications for ensuring the structural integrity and safety of high-rise buildings, particularly in high-risk seismic areas.

This paper investigates the structural performance of flat double-layer grids with various constitutive units, addressing a notable gap in the literature on diverse geometries. Six common types of flat double-layer grids are selected to provide a comprehensive comparison to understand their structural performance. Parametric models are built using Rhino and Grasshopper plugins. Single- and multi-objective optimization processes are conducted on the considered models to evaluate structural mass and maximum deflection. The number of constitutive units, the structural depth, and the cross-section diameter of the members are selected as design variables. The analysis reveals that the semi-octahedron upon square-grid configuration excels in minimizing structural mass and deflection. Furthermore, models lacking a full pyramid form exhibit higher deflections. Sensitivity analyses disclose the critical influence of the design variables, particularly highlighting the sensitivity of structural mass to the number of constitutive units and cross-section diameter. These findings offer valuable insights and practical design considerations for optimizing double-layer grid space structures.

Experience shows that survivors of disasters are often not satisfied with the shelters provided by relief forces. Therefore, one of the objectives of designing temporary shelters in the study area is to identify patterns that increase the social acceptance of temporary shelters by incorporating limited-time acceptance. Increasing the social acceptance of shelters is one of the main goals of this research. Another objective of this study is to examine the role of temporary shelters in enhancing post-earthquake physical resilience, as well as utilizing indigenous architectural techniques in the design of temporary shelters to enhance environmental resilience. The patterns that need to be applied in the design of temporary shelters to meet the physical-environmental criteria can highlight the necessity of research by interpreting adaptable shelters to the cold-arid mountainous climatic conditions of the Varezqan County. One of the objectives of this research is to design a user-friendly product with high social acceptance as a temporary housing alternative during crises. To achieve these objectives, qualitative and applied analysis and simulation of energy indicators have been conducted in the Design Builder environment. In this study, pre-designed models of temporary shelters have been evaluated in the initial phase. In the second phase of the research, the proposed models are evaluated by experts. In the final stage, the selected model is introduced from the perspective of experts, with an explanation of the optimal physical-environmental patterns as a shelter. The findings of the research indicate that environmental indicators such as safety, hygiene, evoking a sense of home, environmental comfort, cultural compatibility, zoning, etc., as well as physical indicators such as weather-resistant structural form, weather-resistant materials, expandable and modular structure, easy and quick installation, and compliance with dimensions and standards are influential factors in selecting the optimal shelter according to experts.

Background & Aim: Reduced quality of life and self-efficacy are among problems of mothers of children with chronic diseases. This study aimed to determine the effect of empowerment based on the Gibson model on self-efficacy and quality of life in the mothers of children with thalassemia. Methods & Materials: In this quasi-experimental study, the study population were the mothers of children with thalassemia referred to rare disease clinics in Rafsanjan and Kerman in 2020. The sample size was 25. Mothers were selected by the convenience sampling method and divided into two groups. In the intervention group, mothers participated in five training sessions based on the Gibson model, and in the control group, mothers received routine care. Data collection tools included the Zhang’s self-efficacy questionnaire and the SF-36 which were completed before the intervention and six weeks after the intervention. Data were analyzed using chi-square test, independent and paired t-test via the SPSS software version 18. Results: Before the intervention, the mean score of self-efficacy (P=0.31) and quality of life (P=0.47) were not statistically significant between the groups, but after the intervention, the mean score of self-efficacy in the intervention group (68.81±9.36) was significantly higher than that of in the control group (44.69±6.87) (P

Parametric form findings of free-form space structures and qualitative assessment of their aesthetics are among the concerns of architects. This study aims to evaluate the aesthetic aspect of these structures using ML algorithms based on the expert’s experiences. First, various datasets of forms were produced using a parametric algorithm of free-form space structures written in Grasshopper. Then, three multilayer perceptron ANN models were adjusted in their most optimal modes using the results of the preference test based on the aesthetic criteria including simplicity, complexity, and practicality. The results indicate that the ANN models can quantitatively evaluate the aesthetic value of free-form space structures.

With the increasing development of universities and institutions of higher education, student satisfaction is one of the most critical issues for their acceptance to new areas, the main feature of which is the competition to attract and retain students. Despite numerous studies on student satisfaction, more conceptual and methodological understanding is needed to increase the explanatory and predictive power of student satisfaction modes. The first objective of this study is to use artificial neural networks (ANN) to measure the components of student satisfaction. The second goal is to explain the specific components of academic students' Academic Satisfaction through the SEM-ANN combination. Data were collected from 420 graduate students at Tabriz Islamic Art University. The research model was Obtained through a multi-analytic Including structural equation modeling (SEM) and ANN. We used the results of SEM as input to the ANN model to develop a predictive model of Academic Satisfaction of architecture students. Based on the SEM results, we found that the components of educational services (0.99), sociocultural (0.95), and perceptions (0.85) are the most critical components affecting the Academic Satisfaction of architecture students. At the same time, the economic-entrepreneurial component (0.37) has little effect on the Academic Satisfaction of architecture students. The analytical results in ANN using the multi-layer perceptron model led to the explanation of a model with high predictability of components of Academic Satisfaction of architecture students. The findings of this study can be critical, and valuable for universities in predicting students' Academic Satisfaction. The results of this study enable universities to predict the extent of its impact on student satisfaction before formulating their programs, and to cover the stimuli affecting students' Academic Satisfaction with appropriate measures. As a result, it is increasing students' Academic Satisfaction.