A practical methodology for optimum seismic design of RC frames for minimum damage and life-cycle cost

Asadi, Payam; Hajirasouliha, Iman

doi:10.1016/j.engstruct.2019.109896

Cited by 33 publications

(16 citation statements)

References 26 publications

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“…Nyatanya, impelementasi dalam proses perwujudannya cukup sulit terlebih ketika berhadapan dengan dinamik, beban seismik, bangunan berskala besar, overload dan inelastik [1]. Penggunaan parameter desain dari beban seismik yang berbeda-beda saat merancang dapat meningkatkan kinerja dari suatu struktur untuk mencapai tingkat keamanan yang memadai dengan kemungkinan gempa yang akan terjadi selama masa efektif struktur [2]. Selain performa yang andal, suatu struktur juga diharapkan bersifat ekonomis.…”

Section: Pendahuluanunclassified

Pendekatan Artificial Neural Network untuk Mengestimasi Dimensi Optimum dan Rasio Tulangan Gedung

Harahap

Aminullah

Priyosulistyo

2022

inersia

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The conceptual design stage is necessary because it is considered as a fundamental input in decision making for maximizing the performance of a building. On the other hand, to maximize the performance of the building, there are many things that need to be considered. Therefore, an estimation of the optimum dimensions and the reinforcement ratios of beam and column was carried out at the conceptual design stage using the artificial neural network (ANN). ANN is a network based method that allows to get an accurate approach even with the limited information provided. This study aims to help engineers shorten the time for trial at the conceptual design stage. A total of 36 building variations modelling were prepared as the training data for the set up ANN model. Eight parameters used which consist of earthquake accelarations, soil sites class, joint types, beam spans, number of storey, high of storey, concrete strengths and diameters of the reinforcement. There are 16 empirical formulas for estimating the optimum dimensions and the reinforcement ratios of beam and column. The results showed that the dimensional regression values and the reinforcement ratio were 98.53% and 96.06% respectively. This value indicates that ANN can estimate well.

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Section: Pendahuluanunclassified

Pendekatan Artificial Neural Network untuk Mengestimasi Dimensi Optimum dan Rasio Tulangan Gedung

Harahap

Aminullah

Priyosulistyo

2022

inersia

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“…Sometimes it is preferable, in professional practices, the adoption of simple calculation methods of thermal performances based on standard codes. The need of integrating energy evaluations with structural retrofitting is of high priority in seismic prone areas [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Economic vs environmental isocost and isoperformance curves for the seismic and energy improvement of buildings considering Life Cycle Assessment

Giresini

Stochino

Sassu

2021

Engineering Structures

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A methodology to quantitatively assess the improvement of seismic and energy performance of masonry buildings through retrofitting interventions is here proposed. The approach is developed at mesoscale level, considering entire façades with openings and taking into account Life Cycle Assessment (LCA). The costs of retrofitting interventions that couple effects of seismic and thermal improvement (called integrated interventions/approaches) are different whether only the construction phase or the entire life cycle of the building is considered. Therefore, it is necessary to estimate at what extent it is correct to neglect LCA in the analysis of integrated approaches. In this paper, the analysis of three masonry façades is performed with and without LCA. Traditional (insulating panels, diatons, ferro-cement) and more innovative interventions (carbon and glass fiber reinforced polymer composites) are considered. For the comparison, isocost and isoperformance curves, which determine both the economic (Euros) and environmental costs (kg CO2eq) for each intervention, are discussed. The comparison shows the necessity of always considering LCA for a reliable assessment: some retrofitting interventions are the most expensive in the construction phase but they result the most convenient in economic terms and in the amount of CO2eq emissions.

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“…Furthermore, Ganjavi et al [19] investigated the best distribution of seismic lateral loads to achieve uniform damage distribution in 2D shear buildings considering the soil-structure interaction (SSI). Recently, many researchers employed optimization methods to reach the desired seismic performance objectives at various seismic hazard levels in 2D low-rise and mid-rise steel frames [20,21] and also in 2D reinforced concrete frames [22]. Recently, with the aid of gradient-based optimization algorithms, Sarcheshmehpour et al proposed practical methodologies for optimal seismic design of steel-framed tube tall buildings based on conventional building codes [23], as well as life cycle costs [24].…”

Section: Introductionmentioning

confidence: 99%

A Novel Strategy for Tall Building Optimization via the Combination of AGA and Machine Learning Methods

Es-haghi

Sarcheshmehpour

2021

The 1st International Electronic Conference on Algorithms

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The optimum design of tall buildings, which have a proportionately huge quantity of structural elements and a variety of design code constraints, is a very computationally expensive process. In this paper, a novel strategy, with a combination of evolutionary algorithms and machine learning methods, is developed for achieving the optimal design of tall buildings. The most time-consuming part is the analysis of tall buildings and the control of design code constraints requiring long and frequent analyses. The main idea is to use machine learning methods for this purpose. In this study, a practical methodology for obtaining the optimal design of tall building structures, regarding the constraints imposed by typical building codes, is introduced. The optimization process will be performed by a novel evolutionary algorithm, named asymmetric genetic algorithm (AGA), and in each iteration that requires checking the constraints for a large number of different structural states, machine learning methods, including MLP, GMDH and ANFIS-PSO are facilitators. More specifically, MLP (R2 = 0.988) has performed better than GMDH (R2 = 0.961) and ANFIS-PSO (R2 = 0.953). By coupling ETABS and MATLAB software, various combinations of sections for structural elements are assigned and analyzed automatically, thus creating a database for training neural networks. The applicability of the suggested procedure is described through the determination of the optimal seismic design for a 40-story framed tube building. Results designate that the present method not only supports the precision of the methodology but also remarkably diminishes the computational time and memory needed in comparison with the existing classical methods. More importantly, the optimization process time is also significantly decreased.

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A practical methodology for optimum seismic design of RC frames for minimum damage and life-cycle cost

Cited by 33 publications

References 26 publications

Pendekatan Artificial Neural Network untuk Mengestimasi Dimensi Optimum dan Rasio Tulangan Gedung

Pendekatan Artificial Neural Network untuk Mengestimasi Dimensi Optimum dan Rasio Tulangan Gedung

Economic vs environmental isocost and isoperformance curves for the seismic and energy improvement of buildings considering Life Cycle Assessment

A Novel Strategy for Tall Building Optimization via the Combination of AGA and Machine Learning Methods

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