scholarly journals Damage Level Prediction of Reinforced Concrete Building Based on Earthquake Time History Using Artificial Neural Network

2017 ◽  
Vol 138 ◽  
pp. 02024
Author(s):  
Reni Suryanita ◽  
Harnedi Maizir ◽  
Enno Yuniarto ◽  
Muhamad Zulfakar ◽  
Hendra Jingga
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reinforced Concrete ◽  
Time History ◽  
Reinforced Concrete Building ◽  
Damage Level ◽  
Concrete Building ◽  
Artificial Neural

Evaluation of Medium-Rise Reinforced Concrete Building Performance under Low Intensity Earthquake Effect

2014 ◽  
Vol 661 ◽  
pp. 106-110 ◽  
Author(s):  
Rozaina Ismail ◽  
Hamidah Mohd Saman ◽  
Masitah Hassim
Keyword(s):  
Reinforced Concrete ◽  
Structural Damage ◽  
Damage Index ◽  
Design Procedure ◽  
Time History ◽  
Building Design ◽  
Reinforced Concrete Building ◽  
Damage Level ◽  
Low Intensity ◽  
Concrete Building

The paper presents an evaluation of medium-rise reinforced concrete building in Johor which is subjected to low intensity earthquake effects. Even-though Malaysia is outside the earthquake region, the country had experienced and did suffer from major cases due earthquake in the past like tsunami. Engineers should concern and consider the loading for reinforced concrete building due to earthquake in the building design procedure. The study addresses the performance of critical frame reinforced concrete building when subjected to earthquake motion. The building of Marlborough College Malaysia chooses as model. The building was analyzed using Finite Element Modelling (FEM) using IDARC (2D) with respect to various earthquake intensities obtained from Time History Analysis (THA) data. The yield point at the beam-column connections was analyzed to determine the damage index and damage level of the building subjected to the various earthquake intensities. The building performed the early yielding point at 4.2650 sec for beam element at the intensity of 0.15g. Based on the results, it was found that the critical frame of Condominium Marlborough College Malaysia can stand an earthquake occurrence with intensity up to 0.20g. There is no structural damage some non-structural damage is expected in the non-linear analysis of modal frames. The building was also categorized as the one in the light damage level.

scholarly journals Application of artificial neural networks to predict the deflections of reinforced concrete beams

2016 ◽  
Vol 38 (2) ◽  
pp. 37-46 ◽  
Author(s):  
Mateusz Kaczmarek ◽  
Agnieszka Szymańska
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Reinforced Concrete ◽  
Concrete Beams ◽  
Reinforced Concrete Beams ◽  
The Neural Network ◽  
Eurocode 2 ◽  
Artificial Neural ◽  
Concrete Elements

Abstract Nonlinear structural mechanics should be taken into account in the practical design of reinforced concrete structures. Cracking is one of the major sources of nonlinearity. Description of deflection of reinforced concrete elements is a computational problem, mainly because of the difficulties in modelling the nonlinear stress-strain relationship of concrete and steel. In design practise, in accordance with technical rules (e.g., Eurocode 2), a simplified approach for reinforced concrete is used, but the results of simplified calculations differ from the results of experimental studies. Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. This paper describes the creation and operation of a neural network for making predictions of deflections of reinforced concrete beams at different load levels. In order to obtain a database of results, that is necessary for training and testing the neural network, a research on measurement of deflections in reinforced concrete beams was conducted by the authors in the Certified Research Laboratory of the Building Engineering Institute at Wrocław University of Science and Technology. The use of artificial neural networks is an innovation and an alternative to traditional methods of solving the problem of calculating the deflections of reinforced concrete elements. The results show the effectiveness of using artificial neural network for predicting the deflection of reinforced concrete beams, compared with the results of calculations conducted in accordance with Eurocode 2. The neural network model presented in this paper can acquire new data and be used for further analysis, with availability of more research results.

scholarly journals Artificial Neural Network (ANN) and Finite Element (FEM) Models for GFRP-Reinforced Concrete Columns under Axial Compression

Materials ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7172
Author(s):  
Haytham F. Isleem ◽  
Bassam A. Tayeh ◽  
Wesam Salah Alaloul ◽  
Muhammad Ali Musarat ◽  
Ali Raza
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Reinforced Concrete ◽  
Concrete Structures ◽  
Fiber Reinforced Polymer ◽  
Concrete Core ◽  
Reinforced Polymer ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In reinforced concrete structures, the fiber-reinforced polymer (FRP) as reinforcing rebars have been widely used. The use of GFRP (glass fiber-reinforced polymer) bars to solve the steel reinforcement corrosion problem in various concrete structures is now well documented in many research studies. Hollow concrete-core columns (HCCs) are used to make a lightweight structure and reduce its cost. However, the use of FRP bars in HCCs has not yet gained an adequate level of confidence due to the lack of laboratory tests and standard design guidelines. Therefore, the present paper numerically and empirically explores the axial compressive behavior of GFRP-reinforced hollow concrete-core columns (HCCs). A total of 60 HCCs were simulated in the current version of Finite Element Analysis (FEA) ABAQUS. The reference finite element model (FEM) was built for a wide range of test variables of HCCs based on 17 specimens experimentally tested by the same group of researchers. All columns of 250 mm outer diameter, 0, 40, 45, 65, 90, 120 mm circular inner-hole diameter, and a height of 1000 mm were built and simulated. The effects of other parameters cover unconfined concrete strength from 21.2 to 44 MPa, the internal confinement (center to center spiral spacing = 50, 100, and 150 mm), and the amount of longitudinal GFRP bars (ρv = 1.78–4.02%). The complex column response was defined by the concrete damaged plastic model (CDPM) and the behavior of the GFRP reinforcement was modeled as a linear-elastic behavior up to failure. The proposed FEM showed an excellent agreement with the tested load-strain responses. Based on the database obtained from the ABAQUS and the laboratory test, different empirical formulas and artificial neural network (ANN) models were further proposed for predicting the softening and hardening behavior of GFRP-RC HCCs.

Sign in / Sign up

Export Citation Format

Share Document