• Advances in Computational Design
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• v.9 no.1
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• pp.1-23
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• 2024

Present paper deals with the cost effective design of reinforced concrete building frame employing unified particle swarm optimization (UPSO). A building frame with G+8 stories have been adopted to demonstrate the effectiveness of the present algorithm. Effect of seismic loads and wind load have been considered as per Indian Standard (IS) 1893 (Part-I) and IS 875 (Part-III) respectively. Analysis of the frame has been carried out in STAAD Pro software.The design loads for all the beams and columns obtained from STAAD Pro have been given as input of the optimization algorithm. Next, cost optimization of all beams and columns have been carried out in MATLAB environment using UPSO, considering the safety and serviceability criteria mentioned in IS 456. Cost of formwork, concrete and reinforcement have been considered to calculate the total cost. Reinforcement of beams and columns has been calculated with consideration for curtailment and feasibility of laying the reinforcement bars during actual construction. The numerical analysis ensures the accuracy of the developed algorithm in providing the cost optimized design of RC building frame considering safety, serviceability and constructional feasibilities. Further, Monte Carlo simulations performed on the numerical results, proved the consistency and robustness of the developed algorithm. Thus, the present algorithm is capable of giving a cost effective design of RC building frame, which can be adopted directly in construction site without making any changes.

• Steel and Composite Structures
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• v.30 no.3
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• pp.271-280
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• 2019

In this paper, numerical and experimental assessments have been conducted in order to investigate the capability of using CFRP for the seismic capacity improvement and relocation of plastic hinge in reinforced concrete connections. Two scaled down exterior reinforced concrete beam to column connections have been used. These two connections from a strengthened moment frame have been tested under uniformly distributed load before and after optimization. The results of experimental tests have been used to verify the accuracy of numerical modeling using computational ABAQUS software. Application of FRP plate on the web of the beam in connections to improve its capacity is of interest in this paper. Several parametric studies were carried out for CFRP reinforced samples, with different lengths and thicknesses in order to relocate the plastic hinge away from the face of the column.

• Structural Engineering and Mechanics
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• v.41 no.2
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• pp.231-242
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• 2012

This research work aims to develop an optimal design using Finite Element (FE) and Genetic Algorithm (GA) methods to replace the traditional concrete and timber material by a Synthetic Polyurethane fibre glass composite material in railway sleepers. The conventional timber railway sleeper technology is associated with several technical problems related to its durability and ability to resist cutting and abrading action of the bearing plate. The use of pre-stress concrete sleeper in railway industry has many disadvantages related to the concrete material behaviour to resist dynamic stress that may lead to a significant mechanical damage with feasible fissures and cracks. Scientific researchers have recently developed a new composite material such as Glass Fibre Reinforced Polyurethane (GFRP) foam to replace the conventional one. The mechanical properties of these materials are reliable enough to help solving structural problems such as durability, light weight, long life span (50-60 years), less water absorption, provide electric insulation, excellent resistance of fatigue and ability to recycle. This paper suggests appropriate sleeper design to reduce the volume of the material. The design optimization shows that the sleeper length is more sensitive to the loading type than the other parameters.

• Advances in nano research
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• v.16 no.6
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• pp.623-638
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• 2024

This study investigates the thermal post-buckling behavior of concrete eccentric annular sector plates reinforced with graphene oxide powders (GOPs). Employing the minimum total potential energy principle, the plates' stability and response under thermal loads are analyzed. The Haber-Schaim foundation model is utilized to account for the support conditions, while the transform differential quadrature method (TDQM) is applied to solve the governing differential equations efficiently. The integration of GOPs significantly enhances the mechanical properties and stability of the plates, making them suitable for advanced engineering applications. Numerical results demonstrate the critical thermal loads and post-buckling paths, providing valuable insights into the design and optimization of such reinforced structures. This study presents a machine learning algorithm designed to predict complex engineering phenomena using datasets derived from presented mathematical modeling. By leveraging advanced data analytics and machine learning techniques, the algorithm effectively captures and learns intricate patterns from the mathematical models, providing accurate and efficient predictions. The methodology involves generating comprehensive datasets from mathematical simulations, which are then used to train the machine learning model. The trained model is capable of predicting various engineering outcomes, such as stress, strain, and thermal responses, with high precision. This approach significantly reduces the computational time and resources required for traditional simulations, enabling rapid and reliable analysis. This comprehensive approach offers a robust framework for predicting the thermal post-buckling behavior of reinforced concrete plates, contributing to the development of resilient and efficient structural components in civil engineering.

• Structural Engineering and Mechanics
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• v.3 no.6
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• pp.529-539
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• 1995

The rigid-plastic yield-line analysis of isotropically reinforced concrete slabs acting in conjunction with torsionally weak supporting beams is developed as the lower-bound form of a linear programming formulation. The analysis is extended to consider geometric variation of chosen yield-line patterns by the technique of sequential linear programming. A strategy is followed of using a fine potential yield-line mesh to identify possible collapse modes, followed by analysis using a coarser, simplified mesh to refine the investigation and for use in conjunction with geometric optimization of the yield-line system. The method is shown to be effective for the analysis of three slabs of varying complexity. The modes detected by the fine and simplified analyses are not always similar but close agreement in load factors has been consistently obtained.

• Earthquakes and Structures
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• v.12 no.6
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• pp.591-601
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• 2017

To prevent or limit the damage caused by earthquakes on existing buildings, several retrofitting techniques are possible. In this work, an ultra high performance concrete based on sand dune has been formulated for use in the reinforcement of a multifunctional tower in the city of Skikda in Algeria. Tests on the formulated ultra high performance concrete are performed to determine its characteristics. A nonlinear dynamic analysis, based on the "Pushover" method was conducted. The analysis allowed an optimization of the width of reinforced concrete walls used in seismic strengthening. Two types of concrete are studied, the ordinary concrete and the ultra high performance concrete. Both alternatives are compared with the reinforcement with carbon fibers and by base isolation retrofit design.

• Advances in concrete construction
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• v.17 no.1
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• pp.27-36
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• 2024

Reinforced concrete (RC) flat slabs should be designed based on punching shear strength. As part of this study, machine learning (ML) algorithms were developed to accurately predict the punching shear strength of RC flat slabs without shear reinforcement. It is based on Bayesian optimization (BO), combined with four standard algorithms (Support vector regression, Decision trees, Random forests, Extreme gradient boosting) on 446 datasets that contain six design parameters. Furthermore, an analysis of feature importance is carried out by Shapley additive explanation (SHAP), in order to quantify the effect of design parameters on punching shear strength. According to the results, the BO method produces high prediction accuracy by selecting the optimal hyperparameters for each model. With R² = 0.985, MAE = 0.0155 MN, RMSE = 0.0244 MN, the BO-XGBoost model performed better than the original XGBoost prediction, which had R² = 0.917, MAE = 0.064 MN, RMSE = 0.121 MN in total dataset. Additionally, recommendations are provided on how to select factors that will influence punching shear resistance of RC flat slabs without shear reinforcement.

• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.6
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• pp.153-163
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• 2011

This research describe improved algorithm that is able to decide terminal criterion of Evolutionary Structural Optimization (ESO), reducing load of calculation to search load path of concrete beam, and apply to agricultural facilities. The ESO method is that make to discrete structure, structural analyze each element stress through FEM. And repeat generation with next material condition to become for most suitable composing. Individual element introduces concept of zero stiffness, but zero stiffness decisions are gone to direction of exclusion. In this stduy, improve algorithm to be convergence by 'Rule of Alive or Die' in arrival because is most suitable. Also, existing terminal criterion lack consistency because that used depend on experience of researcher. This research procedure is fellowed. First, all modulus of elasticity assume a half of elasticity modulus of material, Second, structural analysis by FEM, Third, apply to the remove ratio and restoration ratio for the 'rule of alive or die'. Forth, reconstruct the element and material conditions. And repeat the first to forth process. The terminal time of evolutional procedure is the all elastic modulus of element changed to blank value or elasticity modulus value of original. Therefore, in this study, consist the algorithm for programming, and apply to the agricultural facilities with concrete.

• Journal of Ocean Engineering and Technology
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• v.16 no.4
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• pp.25-31
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• 2002

This paper is to find optimum design of plane framed structures with discrete variables. Global search algorithms for this problem are Genetic Algorithms(GAs), Simulated Annealing(SA) and Shuffled Complex Evolution(SCE), and hybrid methods (GAs-SA, GAs-SCE). GAs and SA are heuristic search algorithms and effective tools which is finding global solution for discrete optimization. In particular, GAs is known as the search method to find global optimum or near global optimum. In this paper, reinforced concrete plane frames with rectangular section and steel plane frames with W-sections are used for the design of discrete optimization. These structures are designed for stress constraints. The robust and effectiveness of Genetic Algorithms are demonstrated through several examples.

• Journal of Korean Association for Spatial Structures
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• v.23 no.4
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• pp.81-88
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• 2023

Numerous factors contribute to the deterioration of reinforced concrete structures. Elevated temperatures significantly alter the composition of the concrete ingredients, consequently diminishing the concrete's strength properties. With the escalation of global CO2 levels, the carbonation of concrete structures has emerged as a critical challenge, substantially affecting concrete durability research. Assessing and predicting concrete degradation due to thermal effects and carbonation are crucial yet intricate tasks. To address this, multiple prediction models for concrete carbonation and compressive strength under thermal impact have been developed. This study employs seven machine learning algorithms-specifically, multiple linear regression, decision trees, random forest, support vector machines, k-nearest neighbors, artificial neural networks, and extreme gradient boosting algorithms-to formulate predictive models for concrete carbonation and thermal impact. Two distinct datasets, derived from reported experimental studies, were utilized for training these predictive models. Performance evaluation relied on metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analytical outcomes demonstrate that neural networks and extreme gradient boosting algorithms outshine the remaining five machine learning approaches, showcasing outstanding predictive performance for concrete carbonation and thermal effect modeling.