The Effect of Soil around the Basement Walls on the Base Level of Braced Framed Tube System

According to the 2800 standard, the Iranian code of practice for seismic-resistant design of buildings, the base level refers to the level at which it is assumed that the horizontal movement of the ground is transmitted to the structure. In cases that there are reinforced concrete walls being run by an integrative structure in the underground perimeter, and the surrounding ground is dense and compressed, the base level is considered on the top of the basement wall. In tall structures, due to strong forces and moments at the foot of the structure, examining the location of base level and its movement becomes specially important. The aim of this study was to investigate the impact of changing the properties of the soil around the underground perimeter walls on the base level, taking into account the effects of soil-structure interaction systems. In this regard, the soil-structure system was investigated in two-dimensional models and the location of the base level was identified using shear and drift changes. The results indicated that taking into account the level of the upper stories is possible through performing appropriate walls integrated with the structure even without Compacting the soil around the structure

A Modeling Strategy for Predicting the Response of Steel Plate-Concrete Composite Walls

Shear walls are among lateral load resisting systems which are used to provide adequate stiffness, strength, and nonlinear deformation capacity to withstand strong ground motion. Usually at the base of the wall, these structures tolerate inelastic deformations subjected to strong ground motions. Researchers have offered composite walls to solve these problems. Steel plate-concrete composite (SCC) walls have been regarded as an alternative to reinforced concrete walls in terms of seismic performance and constructability. In this study, a new semi-macro modified fixed strut angle finite element model is proposed to predict the nonlinear response of SCC walls using OpenSees. A new modified fixed strut angle model and a quadrilateral flat shell element are adapted to the analysis of SCC shear walls. The numerical model is validated using the results of a set of experimental data reported in the literature. Comprehensive comparisons between analytical-model-predictions and experimental data suggest that the numerical model can accurately simulate the steel plate-concrete composite wall responses

Efficiency of Hybrid Algorithms for Estimating the Shear Strength of Deep Reinforced Concrete Beams

Earthquakes occurred in recent years have highlighted the need to examine the strength of reinforced concrete (RC) members. RC beams are one of the elements of reinforced concrete structures. Due to the dramatic increase in the population and the number of medium/high-rise buildings, in recent years, the beams of buildings have been mainly designed and executed in the type of deep beams. In this study, the artificial neural network (ANN) with optimization algorithms, including particle swarm optimization (PSO), Archimedes optimization algorithm (AOA), and sparrow search algorithm (SSA), are used to determine the shear strength of reinforced concrete deep (RCD) beams. 271 samples from experimental tests are employed to develop algorithms. The results of this study, design codes equations, and previous research are compared. Comparison between the results shows that the PSO-ANN algorithm is more accurate than previous methods. Finally, SHApley Additive exPlanations (SHAP) method is utilized to explain the predictions. SHAP reveals that the beam span and the ratio of the beam span to beam depth have the highest impact in predicting shear strength

Base level Investigation in various buildings and corresponding effective factors

Base level is one of the important parameters in determining the seismic force and preliminary design of structural sections. Base level, According to 2800 seismic regulations, in cases which the basement perimeter is executed with reinforced concrete walls integrated by structure, in addition with surrounding dense soil; set top of basement walls. The critical issue involved in determining base level is horizontal motion of the land. Usually horizontal movement of the Earth is transferred by shear and friction between the edges of the basement walls and foundation, also this process is completed by soil friction between underside of slabs and shallow. Different conditions such as non-same elevated foundations, soil type around building, soil-structure interaction and type of foundation are impressive on location of base level. Other factors including retaining wall openings in basement, basement floors and soil characteristics around the base structures affect base level coordination. As regards there is cleared definition for base level in different regulation all around the world, sometimes engineers cannot comprehend main purpose correctly, or concepts occasionally are interpreted inaccurately. When structure conditions little different from what normally there is, for example, buildings on slope, or structures on deep foundation such as piles, often experts are conflicted by finding location of base level in this status. In this paper investigations about base level in the past years expressed and studied, also, important issues around them are discussed

The Efficiency of Hybrid Intelligent Models in Predicting Fiber-Reinforced Polymer Concrete Interfacial-Bond Strength

Fiber-reinforced polymer (FRP) has several benefits, in addition to excellent tensile strength and low self-weight, including corrosion resistance, high durability, and easy construction, making it among the most optimum options for concrete structure restoration. The bond behavior of the FRP-concrete (FRPC) interface, on the other hand, is extremely intricate, making the bond strength challenging to estimate. As a result, a robust modeling framework is necessary. In this paper, data-driven hybrid models are developed by combining state-of-the-art population-based algorithms (bald eagle search (BES), dynamic fitness distance balance-manta ray foraging optimization (dFDB-MRFO), RUNge Kutta optimizer (RUN)) and artificial neural networks (ANN) named “BES-ANN”, “dFDB-MRFO -ANN”, and “RUN-ANN” to estimate the FRPC interfacial-bond strength accurately. The efficacy of these models in predicting bond strength is examined using an extensive database of 969 experimental samples. Compared to the BES-ANN and dFDB-MRFO models, the RUN-ANN model better estimates the interfacial-bond strength. In addition, the SHapley Additive Explanations (SHAP) approach is used to help interpret the best model and examine how the features influence the model’s outcome. Among the studied hybrid models, the RUN-ANN algorithm is the most accurate model with the highest coefficient of determination (R2 = 92%), least mean absolute error (0.078), and least coefficient of variation (18.6%). The RUN-ANN algorithm also outperformed mechanics-based models. Based on SHAP and sensitivity analysis method, the FRP bond length and width contribute more to the final prediction results