Ehsan Mansouri’s research while affiliated with Birjand University of Medical Sciences and other places

Polyurethane elastomer exhibits a complex behavior in response to external forces, which necessitates the use of specialized modeling methods that are often unavailable in standard analysis software. In response to this challenge, researchers have developed a new class in the OpenSees software that enables highly accurate modeling of polyurethane elastomer behavior using laboratory test data. The newly developed class has proven to be highly effective in validating the behavior of polyurethane elastomer, demonstrating that it offers high accuracy in modeling the material’s behavior. The ability to accurately model the behavior of polyurethane elastomer is essential to ensure the safety and reliability of structures that incorporate this material. The findings of this research have significant implications for the design and analysis of structures incorporating polyurethane elastomer, offering new insights into its use as a smart material for enhancing structural performance under extreme loads.

Soft computing techniques have become popular for solving complex engineering problems and developing models for evaluating structural material properties. There are limitations to the available methods, including semi-empirical equations, such as overestimating or underestimating outputs, and, more importantly, they do not provide predictive mathematical equations. Using gene expression programming (GEP) and artificial neural networks (ANNs), this study proposes models for estimating recycled aggregate concrete (RAC) properties. An experimental database compiled from parallel studies, and a large amount of literature was used to develop the models. For compressive strength prediction, GEP yielded a coefficient of determination (R²) value of 0.95, while ANN achieved an R² value of 0.93, demonstrating high reliability. The proposed predictive models are both simple and robust, enhancing the accuracy of RAC property estimation and offering a valuable tool for sustainable construction.

The global shear capacity of steel–concrete composite downstand cellular beams with precast hollow-core units is an important calculation as it affects the span-to-depth ratios and the amount of material used, hence affecting the embodied CO2 calculation when designers are producing floor grids. This paper presents a reliable tool that can be used by designers to alter and optimise grip options during the preliminary design stages, without the need to run onerous calculations. The global shear capacity prediction formula is developed using five machine learning models. First, a finite element model database is developed. The influence of the opening diameter, web opening spacing, tee-section height, concrete topping thickness, interaction degree, and the number of shear studs above the web opening are investigated. Reliability analysis is conducted to assess the design method and propose new partial safety factors. The Catboost regressor algorithm presented better accuracy compared to the other algorithms. An equation to predict the shear capacity of composite cellular beams with hollow-core units is proposed using gene expression programming. In general, the partial safety factor for resistance, according to the reliability analysis, varied between 1.25 and 1.26.

The calculation of the global shear capacity of steel-concrete composite downstand cellular beams with precast hollow-core units is important as it affects the span to depth ratios and the amount of material used, hence affects the embodied CO2 calculation when designers are deciding on the floor grids. This paper presents a reliable tool that can be used by designers to alter and optimise grip options during the preliminary design stages, without the need to run onerous calculations. The global shear capacity prediction formula is developed using five machine learning models. First, a finite element model database is developed. The influence of the opening diameter, web opening spacing, tee-section height, concrete topping thickness, the interaction degree, and the number of shear studs above the web opening are investigated. Reliability analysis is conducted to assess the design method and propose new partial safety factors. The Catboost Regressor algorithm presented better accuracy compared to the other algorithms. An equation to predict the shear capacity of composite cellular beams with hollow-core units is proposed by Gene Expression Programming. In general, the partial safety factor for resistance, according to the reliability analysis, varied between 1.25 and 1.26.

In this study, new steel material classes were added to the OpenSees 3.0.0 library to model their behavior within cold-formed profiles under high temperatures. The new material classes that were added are capable of modeling G-450 and G-550 grade galvanized steels under mechanical and thermal loads. Gypsum panel, a nonstructural material within walls, significantly contributed to the lateral resistance of cold-formed structures. For the first time, the relevant material class was added to OpenSees. First, heat transfer analysis was performed to determine the temperature distribution within different parts of the frame structure. Second, the structure was analyzed under gravity loads, followed by thermal loads. Results from the first step were applied to the structure, and a transient thermomechanical analysis was performed. The output of this analysis included the deformation and force of the members of the structure. The behavior of each new material class was compared with the experimental results to determine the accuracy of the developed OpenSees scripts. Moreover, the results related to mod-eling with this material class were compared with those of the material classes available in OpenSees. The results exhibited high accuracy with the new material class, and the difference in the results obtained with the current material classes in OpenSees was significant.

In order to reduce the adverse effects of concrete on the environment, options for eco-friendly and green concretes are required. For example, geopolymers can be an economically and environmentally sustainable alternative to portland cement. This is accomplished through the utilization of alumina-silicate waste materials as a cementitious binder. These geopolymers are synthesized by activating alumina-silicate minerals with alkali. This paper employs a three-step machine learning (ML) approach in order to estimate the compressive strength of geopolymer concrete. The ML methods include CatBoost regressors, extra trees regressors, and gradient boosting regressors. In addition to the 84 experiments in the literature, 63 geopolymer concretes were constructed and tested. Using Python language programming, machine learning models were built from 147 green concrete samples and four variables. Three of these models were combined using a blending technique. Model performance was evaluated using several metric indices. Both the individual and the hybrid models can predict the compressive strength of geopolymer concrete with high accuracy. However, the hybrid model is claimed to be able to improve the prediction accuracy by 13%.