Prediction and sensitivity analysis of compressive strength in segregated lightweight concrete based on artificial neural network using ultrasonic pulse velocity

Tenza-Abril, Antonio José; Villacampa, Yolanda; Solak, Afonso Miguel; Baeza-Brotons, F.

doi:10.1016/j.conbuildmat.2018.09.096

Cited by 94 publications

(48 citation statements)

References 38 publications

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“…In order to quantify the segregation of concrete, following [22], we analyze the compressive strength of segregated LWAC using ultrasonic pulse velocity.…”

Section: Taking the Expression (3) From (2) It Followsmentioning

confidence: 99%

Parallel approach of a Galerkin-based methodology for predicting the compressive strength of the lightweight aggregate concrete

Migallón

Navarro-González

Penadés

et al. 2019

Construction and Building Materials

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A methodology based on the Galerkin formulation of the finite element method has been analyzed for predicting the compressive strength of the lightweight aggregate concrete using ultrasonic pulse velocity. Due to both the memory requirements and the computational cost of this technique, its parallelization becomes necessary for solving this problem. For this purpose a mixed MPI/OpenMP parallel algorithm has been designed and different approaches and data distributions analyzed. On the other hand, this Galerkin methodology has been compared with multiple linear regression models, regression trees and artificial neural networks. Based on different measures of goodness of fit, the effectiveness of the Galerkin methodology, compared with these statistical techniques for data mining, is shown.

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“…In order to quantify the segregation of concrete, following [22], we analyze the compressive strength of segregated LWAC using ultrasonic pulse velocity.…”

Section: Taking the Expression (3) From (2) It Followsmentioning

confidence: 99%

Parallel approach of a Galerkin-based methodology for predicting the compressive strength of the lightweight aggregate concrete

Migallón

Navarro-González

Penadés

et al. 2019

Construction and Building Materials

View full text Add to dashboard Cite

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“…Changing the LWA used in the concrete varies other properties of LWAC like compressive strength and density which is not predictable for all types of lightweight aggregates. For this reason, many different techniques have been used to provide better mixtures design using different types of approaches [11], such as genetic algorithms [5], artificial neural networks [12], [13] or finite elements [14].…”

Section: Introductionmentioning

confidence: 99%

“…In fact, during the vibration of the concrete, lightweight aggregates tend to float. Segregated concretes are susceptible to an increased risk of cracking due to the separation of the aggregates from the rest of the mixture reducing their durability [13]. There are several methods to measure the segregation [16], which can be detected by ultrasonic pulse velocity [13] and can be measured through image analysis [17] because the manufacture process can affect it [18].…”

Section: Introductionmentioning

confidence: 99%

“…Segregated concretes are susceptible to an increased risk of cracking due to the separation of the aggregates from the rest of the mixture reducing their durability [13]. There are several methods to measure the segregation [16], which can be detected by ultrasonic pulse velocity [13] and can be measured through image analysis [17] because the manufacture process can affect it [18]. There are numerical models for describing it [15] and also neural networks have been applied to analyse the phenomenon [13].…”

Section: Introductionmentioning

confidence: 99%

“…There are several methods to measure the segregation [16], which can be detected by ultrasonic pulse velocity [13] and can be measured through image analysis [17] because the manufacture process can affect it [18]. There are numerical models for describing it [15] and also neural networks have been applied to analyse the phenomenon [13]. The supplementary materials can also affect the segregation [19].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Compressive Strength Classification of Lightweight Aggregate Concrete Using a Support Vector Machine Model

Tenza-Abril

Satorre-Cuerda

Compañ

et al. 2019

Computational Methods and Experimental Measurements XIX

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Lightweight aggregates (LWA) are used to produce low-density concretes required for building applications. Lightweight aggregate concrete (LWAC) is a multipurpose material for construction, which offers technical, economical and environment benefits, and it is produced by replacing the normal-weight aggregates with LWA, depending upon the requirements of density and strength. LWAC is a complex composite material, and a model of its compressive strength must be highly nonlinear because it is very sensitive to its ingredients, so modelling its behaviour is a difficult task. Many studies have tried to develop accurate and effective predictive models for LWAC compressive strength. In this study, a support vector machine (SVM) learning algorithm is used to propose a model to classify the compressive strength of a wide range of LWAC. A dataset of 241 different LWACs were used for classifying the compressive strength into six different classes (from low-strength to high-strength) using different variables-quantity of cement, water and LWA in the dosage and density of the LWAC produced. The results show that increasing the variables means the model becomes more accurate up to approximately an 80% rate of success. The SVM model proved to be a significant tool to classify the compressive strength of LWAC contributing to engineers avoiding costly experimental trial tests.

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Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

Kazemi

2023

Engineering Reports

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Advanced concrete technology is the science of efficient, cost‐effective, and safe design in civil engineering projects. Engineers and concrete designers are generally faced with the slightest change in the conditions or objectives of the project, which makes it challenging to choose the optimal design among several ones. Besides, the experimental examination of all of them requires time and high costs. Hence, an efficient approach is to utilize artificial intelligence (AI) techniques to predict and optimize real‐world problems in concrete technology. Despite the large body of publications in this field, there are few comprehensive surveys that conduct scientometric analysis. This paper provides a state‐of‐the‐art review that lists, summarizes, and categorizes the most widely used machine learning methods, meta‐heuristic algorithms, and hybrid approaches to concrete issues. To this end, 457 publications are considered during the recent decade with a scientometric approach to highlight the annual trend/active journals/top researchers/co‐occurrence of key title words/countries' participation/research hotspots. In addition, AI techniques are classified into distinct clusters using VOSviewer clustering visualization to identify the application scope and their relationship through the link strength. The findings can be a beacon to help researchers utilize AI techniques in future research on advanced concrete technology.

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Prediction and sensitivity analysis of compressive strength in segregated lightweight concrete based on artificial neural network using ultrasonic pulse velocity

Cited by 94 publications

References 38 publications

Parallel approach of a Galerkin-based methodology for predicting the compressive strength of the lightweight aggregate concrete

Parallel approach of a Galerkin-based methodology for predicting the compressive strength of the lightweight aggregate concrete

Compressive Strength Classification of Lightweight Aggregate Concrete Using a Support Vector Machine Model

Artificial intelligence techniques in advanced concrete technology: A comprehensive survey on 10 years research trend

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