• Structural Engineering and Mechanics
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• 제63권4호
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• pp.429-438
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• 2017

Structural design has an imperative role in deciding the failure possibility of a Reinforced Concrete (RC) structure. Recent research works achieved the goal of predicting the structural failure of the RC structure with the assistance of machine learning techniques. Previously, the Artificial Neural Network (ANN) has been trained supported by Particle Swarm Optimization (PSO) to classify RC structures with reasonable accuracy. Though, keeping in mind the sensitivity in predicting the structural failure, more accurate models are still absent in the context of Machine Learning. Since the efficiency of multi-objective optimization over single objective optimization techniques is well established. Thus, the motivation of the current work is to employ a Multi-objective Genetic Algorithm (MOGA) to train the Neural Network (NN) based model. In the present work, the NN has been trained with MOGA to minimize the Root Mean Squared Error (RMSE) and Maximum Error (ME) toward optimizing the weight vector of the NN. The model has been tested by using a dataset consisting of 150 RC structure buildings. The proposed NN-MOGA based model has been compared with Multi-layer perceptron-feed-forward network (MLP-FFN) and NN-PSO based models in terms of several performance metrics. Experimental results suggested that the NN-MOGA has outperformed other existing well known classifiers with a reasonable improvement over them. Meanwhile, the proposed NN-MOGA achieved the superior accuracy of 93.33% and F-measure of 94.44%, which is superior to the other classifiers in the present study.

• KIEAE Journal
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• 제7권4호
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• pp.141-146
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• 2007

It is necessary to guarantee the safety, serviceability and durability of reinforced concrete structures over their service life. However, concrete structures represent a decrease in their durability due to the effects of external environments according to the passage of time, and such degradation in durability can cause structural degradation in materials. In concrete structures, some degradations in durability increase the corrosion of embedded rebars and also decrease the structural performance of materials. Thus, the structural condition assessment of RC materials damaged by corrosion of rebars becomes an important factor that judges needs to apply restoration. In order to detect the damage of reinforced concrete structures, a visual inspection, a nondestructive evaluation method(NDE) and a specific loading test have been employed. However, obscurities for visual inspection and inaccessible members raise difficulty in evaluating structure condition. For these reasons, detection of location and quantification of the damage in structures via structural response have been one of the very important topics in system identification research. The main objective of this project is to develope a methodologies for the damage identification via static responses of the members damaged by durability. Six reinforced concrete beams with variables of corrosion position and corrosion width were fabricated and the damage detections of corroded RC beams were performed by the optimization and the conjugate beam methods using static deflection. In results it is proved that the conjugate beam method could predict the damage of RC members practically.

• KCI Concrete Journal
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• 제13권2호
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• pp.26-32
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• 2001

A simple expected damage cost model is developed and a systematic approach to evaluate the economic effects of seismic hazards to reinforced concrete structures is presented. An expected damage cost function during a specific lifetime is modeled by a Poisson's process with uniform continuous cash flow assumption. It is possible that the proposed method can decouple the damage cost effect from random earthquake events. Thus, expected damage cost function can be formulated as a combination of three independent terms; a present worth factor of Poisson's process, a damage cost interpolation function and a mean occurrence rate of earthquake intensity. The validity of the proposed method is demonstrated by a comparative study of LCC evaluations with the previous study.

• 한국재난관리표준학회지
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• 제4권1호
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• pp.49-55
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• 2011

본 연구는 철근 콘크리트 구조물의 새로운 자동화 최적설계 알고리즘을 제시하였다. 기존의 주철근과 콘크리트 단면사이즈 등의 국한된 최적설계 범위를 벗어나 철근의 부착길이, 매입길이, 콘크리트 커버두께 등 세부설계사항까지 모두 고려한 실무에 적합한 효용성 높은 설계알고리즘을 제시함으로써 앞으로 실무분야에 많은 기여를 할 수 있다고 보여 진다.

• Structural Engineering and Mechanics
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• 제19권4호
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• pp.361-379
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• 2005

Reinforced concrete deep beams have useful applications in tall buildings and foundations. Over the past two decades, numerous design models for deep beams were suggested. However even the latest design manuals still offer little insight into the design of deep beams in particular when complexities exist in the beams like web openings. A method commonly suggested for the design of deep beams with openings is the strut-and-tie model which is primarily used to represent the actual load transfer mechanism in a structural concrete member under ultimate load. In the present study, the development of the strut-and-tie model is transformed to the topology optimization problem of continuum structures. During the optimization process, both the stress and displacement constraints are satisfied and the performance of progressive topologies is evaluated. The influences on the strut-and-tie model in relation to different size, location and number of openings, as well as different loading and support conditions in deep beams are examined in some detail. In all, eleven deep beams with web openings are optimized and compared in nine groups. The optimal strut-and-tie models achieved are also compared with published experimental crack patterns. Numerical results have shown to confirm the experimental observations and to efficiently represent the load transfer mechanism in concrete deep beams with openings under ultimate load.

• Structural Engineering and Mechanics
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• 제83권5호
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• pp.671-680
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• 2022

The shear capacity of beams is an essential parameter in designing beams carrying shear loads. Precise estimation of the ultimate shear capacity typically requires comprehensive calculation methods. For steel fiber reinforced concrete (SFRC) beams, traditional design methods may not accurately predict the interaction between different parameters affecting ultimate shear capacity. In this study, artificial neural network (ANN) modeling was utilized to predict the ultimate shear capacity of SFRC beams using ten input parameters. The results demonstrated that the ANN with 30 neurons had the best performance based on the values of root mean square error (RMSE) and coefficient of determination (R²) compared to other ANN models with different neurons. Analysis of the ANN model has shown that the clear shear span to depth ratio significantly affects the predicted ultimate shear capacity, followed by the reinforcement steel tensile strength and steel fiber tensile strength. Moreover, a Genetic Algorithm (GA) was used to optimize the ANN model's input parameters, resulting in the least cost for the SFRC beams. Results have shown that SFRC beams' cost increased with the clear span to depth ratio. Increasing the clear span to depth ratio has increased the depth, height, steel, and fiber ratio needed to support the SFRC beams against shear failures. This study approach is considered among the earliest in the field of SFRC.

• 콘크리트학회논문집
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• 제18권2호
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• pp.151-159
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• 2006

강섬유보강 콘크리트는 일반 콘크리트에 비해 휨성능이 월등히 우수하지만, 아직까지 국내에는 이에 대한 명확한 배합설계 지침이 확립되어 있지 않은 상황이다. 또한, 강섬유를 2종이상 동시에 혼입하여 사용하는 하이브리드 섬유보강 콘크리트에 대해서는 최근에 들어서야 그 연구가 시작되었으며, 이에 대해서도 배합에 대한 구체적인 지침이 확립되어 있지 않다. 따라서 본 연구에서는 새로운 최적화 기법인 화음탐색법을 이용하여 강섬유 및 하이브리드 섬유보강 콘크리트의 최적배합 프로그램을 개발하였으며, 검증 실험을 수행하여 프로그램의 신뢰도를 높였다. 이는 현장 시험 배합횟수의 감소 및 배합설계의 편의성 향상 등에 도움이 될 것으로 기대된다. 또한, 실험 결과 동일한 강섬유 혼입률이라 하더라도, 하이브리드 섬유보강 콘크리트가 일반 강섬유보강 콘크리트보다 휨강도 및 휨인성 모두 우수한 것으로 나타났으며 이를 프로그램 상에 추가 반영하였다. 이는 세계적으로도 아직 연구 초기 단계에 있는 하이브리드 섬유보강 콘크리트의 휨 특성을 파악하는데에도 향후 연구의 발판이 될 것으로 생각되며, 지속적인 실험 및 연구로 보완이 된다면 보다 더 정밀도를 높일 수 있을 것으로 기대된다.

• 한국농공학회지
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• 제30권3호
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• pp.82-94
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• 1988

A growing attention has been paid to the optimum design of structures in recent years. Most studies on the optimum design of reinforced concrete structures has been mainly focussed to the design of structural members such as beams, slabs and columns, and there exist few studies that deal with the optimum design of large-scale concrete shell structures. The purpose of the present investigation is, therefore, to set up an efficient optimum design method for the large-scale reinforced concrete cylindrical shell structures like intake tower of reservoir. The major design variables are the dimensions and steel areas of each member of structures. The construction cost which is compo8ed of the concrete, steel, and form work costs, respectively, is taken as the objective function. The constraint equations for the design of intake-tower are derived on the basis of strength design method. The results obtained are summarized as follows 1. The efficient optimlzation algorithrns which can execute the automatic optimum design of reinforced concrete intake tower based on the strength design method were developed. 2. Since the objective function and design variables were converged to their optimum values within the first or second iteration, the optimization algorithms developed in this study seem to be efficient and stable. 3. When using the strength design method, the construction cost could be saved about 9% compared with working stress design method. Therefore, the reliability of algorithm was proved. 4. The difference in construction cost between the optimum designs with substructures and with entire structure was found to be small and thus the optimum design with substructures may conveniently be used in practical design. 5. The major active constraints of each structural member were found to be the 'bending moment constraint for slab, the minimum longitudinal steel ratio constraint for tower body and the shearing force, bending moment and maximum eccentricity constraints for footing, respectively. 6. The computer program developed in the present study can be effectively used even by an uneiperienced designer for the optimum design of reinforced concrete intake-tower on the basis of strength design method.

• Steel and Composite Structures
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• 제22권5호
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• pp.1163-1192
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• 2016

This paper presents the cost optimization of a composite I beam floor system, designed to be made from a reinforced concrete slab and steel I sections. The optimization was performed by the mixed-integer non-linear programming (MINLP) approach. For this purpose, a number of different optimization models were developed that enable different design possibilities such as welded or standard steel I sections, plastic or elastic cross-section resistances, and different positions of the neutral axes. An accurate economic objective function of the self-manufacturing costs was developed and subjected to design, resistance and deflection (in)equality constraints. Dimensioning constraints were defined in accordance with Eurocode 4. The Modified Outer-Approximation/Equality-Relaxation (OA/ER) algorithm was applied together with a two-phase MINLP strategy. A numerical example of the optimization of a composite I beam floor system, as presented at the end of this paper, demonstrates the applicability of the proposed approach. The optimal result includes the minimal produced costs of the structure, the optimal concrete and steel strengths, and dimensions.

• Computers and Concrete
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• 제24권4호
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• pp.313-327
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• 2019

The purpose of the present study is to present a new approach to designing and selecting the details of multidimensional continuous RC beam by applying all strength, serviceability, ductility and other constraints based on ACI318-14 using Teaching Learning Based Optimization (TLBO) algorithm. The optimum reinforcement detailing of longitudinal bars is done in two steps. in the first stage, only the dimensions of the beam in each span are considered as the variables of the optimization algorithm. in the second stage, the optimal design of the longitudinal bars of the beam is made according to the first step inputs. In the optimum shear reinforcement, using gradient-based methods, the most optimal possible mode is selected based on the existing assumptions. The objective function in this study is a cost function that includes the cost of concrete, formwork and reinforcing steel bars. The steel used in the objective function is the sum of longitudinal and shear bars. The use of a catalog list consisting of all existing patterns of longitudinal bars based on the minimum rules of the regulation in the second stage, leads to a sharp reduction in the volume of calculations and the achievement of the best solution. Three example with varying degrees of complexity, have been selected in order to investigate the optimal design of the longitudinal and shear reinforcement of continuous beam.