https://wss.torbath.ac.ir/ Journal of Advanced Informatics in Water, Soil, and Structure en daily 1 Tue, 10 Jun 2025 00:00:00 +0330 Tue, 10 Jun 2025 00:00:00 +0330 https://wss.torbath.ac.ir/article_221745.html Understanding the dynamic behavior of structures under various types of loads is essential for ensuring the safety and durability of buildings, especially in seismically active regions. Natural frequencies and corresponding mode shapes are key dynamic properties that influence a building’s response to environmental excitations such as earthquakes, wind, and operational vibrations. When the excitation frequency approaches the natural frequency of a structure, resonance may occur, potentially leading to excessive deformations or structural failure. Therefore, accurately determining these dynamic characteristics is crucial for both design and retrofitting purposes. Composite structures, comprising steel frames and concrete walls or slabs, are increasingly utilized in modern construction due to their combined strength, stiffness, and energy dissipation capacity. Despite their advantages, the modeling of such structures is challenging due to the nonlinear material behavior and the complex interactions between steel and concrete elements. Analytical methods often fall short in capturing these complexities, making numerical simulation techniques such as the Finite Element Method (FEM) essential. In this study, a five-story steel-concrete composite building is modeled and analyzed using Abaqus software. Modal analysis is performed to identify natural frequencies and mode shapes. Furthermore, the research includes a mesh sensitivity study, the implementation of I-beam sections to enhance model realism, and a comparative discussion with theoretical formulations. These efforts aim to improve the accuracy, applicability, and engineering relevance of vibration analysis in mid-rise structures. https://wss.torbath.ac.ir/article_213145.html Data-basis simulation models such as artificial neural network (ANN) and support vector machines (LS-SVM) are suitable substitute for expensive and laboratory methods of estimating soil hydraulic properties, including hydraulic saturation conductivity of soil. In this study, the objective is to predict the saturated hydraulic conductivity of soil in the study area, Sistan Dam, using ANN and LS-SVM. To this purpose, the saturated hydraulic conductivity of soil was measured in 112 points by the suction disc method. For selecting input variables among simple characteristics, the multistep regression method was used. In these models, the selection of different transfer and training functions represents a significant source of error. Consequently, a comprehensive analysis was conducted to identify uncertainty sources in simulating the saturated hydraulic conductivity of soil. To determine the best simulation model among neural network functions and support vector machines, a Monte Carlo method was employed to sample and evaluate their performance. This rigorous approach ensures a thorough examination of the strengths and limitations of these models, thereby enhancing the reliability and accuracy of soil conductivity predictions. Finally, the uncertainty in predicting the solution model can be analyzed to determine how much ANN models and support vector machines (LS-SVMs) are reliable. In general, when considering both linear and nonlinear scenarios in ANNs and LS-SVM, linear input methods may not be a suitable approach. This is primarily due to the complex and nonlinear nature of the soil properties and processes being modeled. Therefore, nonlinear input methods are typically preferred to accurately capture the intricate relationships and dynamics of soil systems. This highlights the importance of selecting appropriate input methods to ensure reliable and accurate modelling of soil properties and behaviour. In addition, among the ANN and LS-SVM models used in this study, ANN, with values of content ratio criteria (CR), (with the values of 90, 0.263, and 0.740) high degree of asymmetry index (S) and high and low asymmetry index (T), was of higher certainty and accuracy than the other models. Moreover, Logsig_trainlm and Tansig_trainbfg scenarios in predicting saturated hydraulic conductivity had a satisfactory process and less uncertainty. https://wss.torbath.ac.ir/article_232325.html Asphalt concrete cores were first used as an impervious element in rock-fill dams in 1948. Although clay soil is abundant and has low permeability, the low shear resistance, long construction time, high material volume requirement, led to an increasing tendency to use asphalt concrete cores. In recent years, with the advancement of computer applications, the use of numerical methods in solving geotechnical problems, has significantly developed. This study compares the performance of vertical and inclined asphalt cores in a rockfill dam under construction and in a steady state condition using the GeoStudio 2021 software. Furthermore, dynamic and static analysis has been conducted on the rockfill dam to investigate the core's stiffness effects. The results indicate that the inclined core experiences less settlement at the end of the construction compared to the vertical core. However, its horizontal displacement is greater due to its position in the upper part of the dam. https://wss.torbath.ac.ir/article_233304.html Steel-concrete-steel (SCS) sandwich composite structures consist of two steel face plates and a concrete core connected by shear connectors. Due to their high strength-to-weight ratio and excellent energy dissipa-tion capacity, these systems are increasingly used in offshore structures. This study develops 211 numerical models using the Explicit solver and mass-scaling technique in ABAQUS to efficiently simulate quasi-static behavior despite geometric complexities. The models investigate the influence of key geometric pa-rameters—including steel face plate thickness, concrete core thickness, and concrete compressive strength—on the shear performance of corrugated strip connectors (CSC). A major contribution of this research is the formulation of predictive linear regression equations for estimating maximum shear strength, offering a practical and time-efficient tool for the preliminary design and optimization of SCS structures. The findings demonstrated the critical influence of connector geometry and concrete strength on the shear performance of SCS sandwich structures, and improved predictive accuracy was achieved through refined numerical modeling.