Shear capacity estimation of fully grouted reinforced concrete masonry walls using neural network and adaptive neuro-fuzzy inference system models

Zhou, Qiang; Zhu, Fei; Xu, Yang; Chi, Bin; Zhang, Zhiming

doi:10.1016/j.conbuildmat.2017.07.171

Cited by 33 publications

(10 citation statements)

References 25 publications

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“…In the above construction procedure, original cleaning-hole blocks can be eliminated since the precast technology enables workers to clean redundant mortar and connect vertical steel bars more easily, which leads to the improvement of construction efficiency and structural integrity. In the literature, lots of theoretical, experimental, and numerical investigations have been conducted to evaluate the seismic performance of the traditional RMSW and the parameters that influence the inelastic behavior of the RMSW, which include the axial compressive stress, the horizontal and vertical reinforcement, and the aspect ratio [4][5][6][7][8][9]. The flexural and shear failure are the two typical failure modes of RMSW under earthquake conditions [10].…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation on the Seismic Behavior of Newly-Developed Precast Reinforced Concrete Block Masonry Shear Walls

Wang

2018

Applied Sciences

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Typically, a special type of concrete block with cleaning holes is used in the bottom layer of traditional reinforced masonry shear walls (RMSWs) for mortar cleaning and vertical rebar connection, which results in reduced integrity and weakened structural behavior. In this paper, a precast construction technology was introduced to overcome these shortcomings. The cleaning-hole blocks were eliminated in the newly-developed precast RMSWs. Quasi-static tests on two traditional and two precast fully grouted RMSWs were conducted. The results showed that the flexural capacity of precast walls exhibited about a 10% increase when compared to traditional RMSWs under the same axial compression. Precast RMSWs that failed in flexural mode showed favorable deformation capacity and the displacement ductility value corresponding to 15% strength degradation reached 4.9. The wall stiffness degraded rapidly to 50% of the initial stiffness, K 0 , at 0.2% drift and, at 0.5% drift, the corresponding stiffness decreased to about 21% K 0 at a more gradual rate. Furthermore, precast RMSWs exhibited significant energy dissipation capacity. The experiment suggests that precast RMSWs have a satisfactory seismic performance.

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Section: Introductionmentioning

confidence: 99%

Experimental Investigation on the Seismic Behavior of Newly-Developed Precast Reinforced Concrete Block Masonry Shear Walls

Wang

2018

Applied Sciences

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“…Asteris et al [28] put in evidence the ability of ANN to solve nonlinear problems by modeling an anisotropy masonry failure criterion under biaxial compressive strength. Zhou et al [34] used ANNs and adaptive neuro-fuzzy networks to predict the shear resistance of fully grouted reinforced concrete masonry. Zhang et al [35] developed an ANN model for predicting the cracking patterns of different masonry wallettes subjected to a vertical load.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks prediction of in-plane and out-of-plane homogenized coefficients of hollow blocks masonry wall

et al. 2020

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A masonry wall is a composite structure characterized by a large variety in geometrical and material parameters. The determination of the effective macroscopic properties, through the homogenization scheme, depends on a great number of variables. Thus, in order to replace heavy numerical simulation, in this paper, the use of artificial neural networks (ANN) is proposed to predict elastic membrane and bending constants of the equivalent Love-Kirchhoff plate of hollow concrete blocks masonry wall. To model the ANN, a numerical periodic homogenization in several parameters is used. To construct the model, five main material and geometrical input parameters are utilized. Multilayer perceptron neural networks are designed and trained (with the best selected ANN model) by the sets of input-output patterns using the backpropagation algorithm. As a result, in both training and testing phases, the developed ANN indicates high accuracy and precision in predicting the equivalent plate of a hollow masonry wall with insignificant error rates compared to FEM results.

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“…The produced linear regression model was compared to an existing model, and the results showed an improved performance of the developed model in the study. ANN and adaptive neuro-fuzzy inference system models were proposed to predict the shear strength of grouted reinforced concrete masonry walls [ 18 ]. The ANN and neuro-fuzzy inference system models well predicted the test results and obtained improved performance as compared to the existing empirical models.…”

Section: Introductionmentioning

confidence: 99%

Predicting Long-Term Deformation of Soundproofing Resilient Materials Subjected to Compressive Loading: Machine Learning Approach

2020

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Soundproofing materials are widely used within structural components of multi-dwelling residential buildings to alleviate neighborhood noise problems. One of the critical mechanical properties for the soundproofing materials to ensure its appropriate structural and soundproofing performance is the long-term compressive deformation under the service loading conditions. The test method in the current test specifications only evaluates resilient materials for a limited period (90-day). It then extrapolates the test results using a polynomial function to predict the long-term compressive deformation. However, the extrapolation is universally applied to materials without considering the level of loads; thus, the calculated deformation may not accurately represent the actual compressive deformation of the materials. In this regard, long-term compressive deformation tests were performed on the selected soundproofing resilient materials (i.e., polystyrene, polyethylene, and ethylene-vinyl acetate). Four levels of loads were chosen to apply compressive loads up to 350 to 500 days continuously, and the deformations of the test specimens were periodically monitored. Then, three machine learning algorithms were used to predict long-term compressive deformations. The predictions based on machine learning and ISO 20392 method are compared with experimental test results, and the accuracy of machine learning algorithms and ISO 20392 method are discussed.

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Shear capacity estimation of fully grouted reinforced concrete masonry walls using neural network and adaptive neuro-fuzzy inference system models

Cited by 33 publications

References 25 publications

Experimental Investigation on the Seismic Behavior of Newly-Developed Precast Reinforced Concrete Block Masonry Shear Walls

Experimental Investigation on the Seismic Behavior of Newly-Developed Precast Reinforced Concrete Block Masonry Shear Walls

Artificial neural networks prediction of in-plane and out-of-plane homogenized coefficients of hollow blocks masonry wall

Predicting Long-Term Deformation of Soundproofing Resilient Materials Subjected to Compressive Loading: Machine Learning Approach

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