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%.

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 modeling 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.