Masonry Compressive Strength Prediction Using Artificial Neural Networks

Argyropoulos, Ioannis; Cavaleri, Liborio; Rodrigues, Hugo; Varum, Humberto; Thomas, Job; Lourenço, Paulo B.

doi:10.1007/978-3-030-12960-6_14

Cited by 52 publications

(21 citation statements)

References 68 publications

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“…In most cases, the hidden layer performs a nonlinear transformation in order to capture the nonlinear behavior between the input and output variables of the considered problem [72][73][74][75]. In this study, the well-known sigmoid function was employed as the nonlinear transformation of the signal for a given neuron in the hidden layer as follows [76][77][78][79][80]:…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

“…given neuron in the hidden layer as follows [76][77][78][79][80] are the received signals coming from the previous neurons and y is the output signal of the considered neuron. In the output layer, a linear transformation function is applied to calculate the response of the prediction problem, which is the compressive strength of HPC in this study.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

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A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation

et al. 2020

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This study aims to analyze the sensitivity and robustness of two Artificial Intelligence (AI) techniques, namely Gaussian Process Regression (GPR) with five different kernels (Matern32, Matern52, Exponential, Squared Exponential, and Rational Quadratic) and an Artificial Neural Network (ANN) using a Monte Carlo simulation for prediction of High-Performance Concrete (HPC) compressive strength. To this purpose, 1030 samples were collected, including eight input parameters (contents of cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregates, fine aggregates, and concrete age) and an output parameter (the compressive strength) to generate the training and testing datasets. The proposed AI models were validated using several standard criteria, namely coefficient of determination (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). To analyze the sensitivity and robustness of the models, Monte Carlo simulations were performed with 500 runs. The results showed that the GPR using the Matern32 kernel function outperforms others. In addition, the sensitivity analysis showed that the content of cement and the testing age of the HPC were the most sensitive and important factors for the prediction of HPC compressive strength. In short, this study might help in selecting suitable AI models and appropriate input parameters for accurate and quick estimation of the HPC compressive strength.

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Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation

et al. 2020

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“…To investigate the performance of the models, the root mean square error (RMSE), R 2 , Ratio of RMSE to the standard deviation of the observations (RSR), mean absolute error (MAE), and degree of agreement (d) were taken into account, which are shown in Equations (9)-(12) [42][43][44][45][46][47][48][49][50][51][52][53][54][55][56]:…”

Section: Development Of Bbo-ann Pso-ann Mpmr and Elm To Predict Ppvmentioning

confidence: 99%

Developing a New Computational Intelligence Approach for Approximating the Blast-Induced Ground Vibration

Kumar

Samui

et al. 2020

Applied Sciences

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Ground vibration induced by blasting operations is an important undesirable effect in surface mines and has significant environmental impacts on surrounding areas. Therefore, the precise prediction of blast-induced ground vibration is a challenging task for engineers and for managers. This study explores and evaluates the use of two stochastic metaheuristic algorithms, namely biogeography-based optimization (BBO) and particle swarm optimization (PSO), as well as one deterministic optimization algorithm, namely the DIRECT method, to improve the performance of an artificial neural network (ANN) for predicting the ground vibration. It is worth mentioning this is the first time that BBO-ANN and DIRECT-ANN models have been applied to predict ground vibration. To demonstrate model reliability and effectiveness, a minimax probability machine regression (MPMR), extreme learning machine (ELM), and three well-known empirical methods were also tested. To collect the required datasets, two quarry mines in the Shur river dam region, located in the southwest of Iran, were monitored, and the values of input and output parameters were measured. Five statistical indicators, namely the percentage root mean square error (%RMSE), coefficient of determination (R2), Ratio of RMSE to the standard deviation of the observations (RSR), mean absolute error (MAE), and degree of agreement (d) were taken into account for the model assessment. According to the results, BBO-ANN provided a better generalization capability than the other predictive models. As a conclusion, BBO, as a robust evolutionary algorithm, can be successfully linked to the ANN for better performance.

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“…The volume fraction is obtained by dividing the respective volume with the corresponding total volume in the prism. The above proposed analytical formula (Equation (5)) seems to be the most reliable for the determination of masonry compressive strength [93] among a plethora of proposed equations available in the literature [76][77][78][79][80][81][82][83][84][85][86][87][88][89][90][91].…”

Section: Step 7: Repairing And/or Strengthening Decisions and Reanalysismentioning

confidence: 99%

“…A trained ANN has learned to rapidly map a given input into the desired output quantities (similar to curve fitting procedures) and thereby can be used as a meta-model enhancing the computational efficiency of a numerical analysis process. This major advantage of a trained ANN over conventional numerical analysis procedures, such as regression analysis, under the condition that the training and validation data cover the entire range of input parameters values, is that the results can be produced with much less computational effort [93,[177][178][179][180][181][182][183][184][185][186].…”

Section: Failure Criterion Based On Artificial Neural Networkmentioning

confidence: 99%

Stochastic Vulnerability Assessment of Masonry Structures: Concepts, Modeling and Restoration Aspects

et al. 2019

Self Cite

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A methodology aiming to predict the vulnerability of masonry structures under seismic action is presented herein. Masonry structures, among which many are cultural heritage assets, present high vulnerability under earthquake. Reliable simulations of their response to seismic stresses are exceedingly difficult because of the complexity of the structural system and the anisotropic and brittle behavior of the masonry materials. Furthermore, the majority of the parameters involved in the problem such as the masonry material mechanical characteristics and earthquake loading characteristics have a stochastic-probabilistic nature. Within this framework, a detailed analytical methodological approach for assessing the seismic vulnerability of masonry historical and monumental structures is presented, taking into account the probabilistic nature of the input parameters by means of analytically determining fragility curves. The emerged methodology is presented in detail through application on theoretical and built cultural heritage real masonry structures. leading to a final approval (or rejection) of the decisions already taken for repair or strengthening of the existing structure. Step 8: Explanatory ReportThe last step, as a result of the proposed methodology, includes the detailed "Explanatory Report", where all the collected information, the diagnosis, including the safety evaluation, and any decision to intervene should be fully detailed. This document is essential for eventual future analyses and interventions' measures in the structure. Computational and Mathematical AspectsIn this section, the most basic analytical constitutive laws and numerical models required for the successful implementation of the proposed methodology are presented in detail. In particular, the finite element model for the macro-modeling of masonry structures, the failure criteria, the damage indices, the performance levels and the mathematical background of fragility curves are presented.

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Masonry Compressive Strength Prediction Using Artificial Neural Networks

Cited by 52 publications

References 68 publications

A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation

A Sensitivity and Robustness Analysis of GPR and ANN for High-Performance Concrete Compressive Strength Prediction Using a Monte Carlo Simulation

Developing a New Computational Intelligence Approach for Approximating the Blast-Induced Ground Vibration

Stochastic Vulnerability Assessment of Masonry Structures: Concepts, Modeling and Restoration Aspects

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