A comparative study on prediction models for strength properties of LWA concrete using artificial neural network

Divyah, N.; Thenmozhi, R.; Neelamegam, M.

doi:10.7764/rdlc.19.1.103-111

Cited by 15 publications

(10 citation statements)

References 24 publications

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“…In particular, splitting tensile strength is one of the mechanical properties of importance in the design of concrete structures [38,39] because cracking in concrete is generally due to tensile stresses that occur under load or due to environmental changes [40]. Machine learning methods have been employed to predict the splitting tensile strength of concrete, with the most widely used being neural networks (ANN) [32,36,[41][42][43][44][45][46], support vector machine (SVM) [16,19,37,38,42,44,45,[47][48][49], eXtreme gradient boosting (XG Boost) [19,37,44], random forest (RF) [16,19,49], decision tree regressor (DTR) [16,27], gradient boosting regressor (GBR) [16,37], and finally multilayer perceptron (MLPs) [37,49].…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete

de‐Prado‐Gil

Palencia

Jagadesh³

et al. 2022

Materials

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Several types of research currently use machine learning (ML) methods to estimate the mechanical characteristics of concrete. This study aimed to compare the capacities of four ML methods: eXtreme gradient boosting (XG Boost), gradient boosting (GB), Cat boosting (CB), and extra trees regressor (ETR), to predict the splitting tensile strength of 28-day-old self-compacting concrete (SCC) made from recycled aggregates (RA), using data obtained from the literature. A database of 381 samples from literature published in scientific journals was used to develop the models. The samples were randomly divided into three sets: training, validation, and test, with each having 267 (70%), 57 (15%), and 57 (15%) samples, respectively. The coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) metrics were used to evaluate the models. For the training data set, the results showed that all four models could predict the splitting tensile strength of SCC made with RA because the R2 values for each model had significance higher than 0.75. XG Boost was the model with the best performance, showing the highest R2 value of R2 = 0.8423, as well as the lowest values of RMSE (=0.0581) and MAE (=0.0443), when compared with the GB, CB, and ETR models. Therefore, XG Boost was considered the best model for predicting the splitting tensile strength of 28-day-old SCC made with RA. Sensitivity analysis revealed that the variable contributing the most to the split tensile strength of this material after 28 days was cement.

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Section: Introductionmentioning

confidence: 99%

A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete

de‐Prado‐Gil

Palencia

Jagadesh³

et al. 2022

Materials

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“…Most studies on concrete and RAC using ANN modeling have been evaluated to predict the performance characteristics of the resulting products. The ANN modeling used to predict concrete performance characteristics include the slump of fly ash and slag concrete (Yeh 2006), setting time and compressive strength of concrete with aluminum waste and sawdust ash (George and Elvis 2019), compressive strength, split tensile strength, and flexural strength of lightweight aggregate concrete (Nagarajan et al 2020), bond strength of reinforcement of concrete (Yartsev et al 2019), demystified behavior of cement-based materials (Chabib and Nehdi 2005), and compressive strength of concrete containing waste rubber (Nyarko et al 2019). From the RAC aspect, some ANN modeling studies have predicted its performance characteristics since 2013, including compressive strength (Deshpande et al 2014;Duan et al 2013bDuan et al , 2018Getahun et al 2018;Kandiri et al 2021;Khademi et al 2016), tensile strength (Getahun et al 2018), modulus of elasticity (Duan et al 2013a(Duan et al , 2018Seyedhamed et al 2019), triaxial loading behavior (Xu et al 2019), water permeability (Chen et al 2020), and dynamic modulus and permanent strain (Ghorbani et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Effective Modeling for Construction Activities of Recycled Aggregate Concrete Using Artificial Neural Network

Kittinaraporn

Tuprakay

Prasittisopin

2022

J. Constr. Eng. Manage.

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Recycled aggregate concrete (RAC) technology is broadly adopted in the construction industry. However, such technology tends to promisingly be implemented only in countries with developed economies, leaving behind countries with emerging economies. To increase the utilization of RAC in these emerging economy countries, this research program aims to investigate the applicability of using the artificial neural network (ANN) technique to predict onsite construction activities of RAC. The construction activities are modeled for the 909 dataset, which includes costs, concrete volume for construction, and total construction time. The results indicate that the mean absolute percentage error values of the RAC cost, concrete volume for construction, and total construction time (including recycled aggregate production and RAC production processes) are 1.98, 28.21, and 2.96, respectively. The mean squared error values of RAC cost, concrete volume for construction, and total construction time are 56,979, 20.9, and 0.56, respectively. Moreover, the coefficient of determination (R 2 ) of RAC cost, concrete volume, and construction time of concrete were calculated at 0.999, 0.976, and 0.968, respectively. Both statistical values indicate that the ANN modeling technique is well implemented for constructing RAC. The results also indicate that ANN modeling can be effectively used in time series for predicting the construction activities of making RAC products. The outcomes offer benefits to stakeholders in construction activities, including improved cost estimations, reduced waste from less concrete going unused, and more accurate project scheduling. ANN modeling represents a relatively simple prediction and can be adopted in preconstruction stages, such as project planning and investment decision making, leading to sustainable construction.

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“…Comparing with the standard concrete the light weight concrete is twenty to twenty ve percent lighter (Lo and Cui 2002). Structural light weight concrete has more exibility, minimum dead weight, strong seismic response, and low cost for foundation (Nagarajan et al 2020). The primary drawback of light weight concrete is it requires more cement to produce the same strength as normal concrete.…”

Section: Introductionmentioning

confidence: 99%

Effect on mechanical properties of lightweight sustainable concrete with the use of waste coconut shell as replacement for coarse aggregate

Natarajan¹,

Ramalingasekar²,

Palanisamy³

et al. 2022

Environ Sci Pollut Res

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Concrete is one of the most important material that is used in the construction industry all around the world. A larger part of the capacity in concrete is generally employed by the coarse aggregate. Due to the tremendous use of coarse aggregate in the construction industry the material is getting degraded. In order to preserve the natural material, we are in search of an alternate material that can be used in concrete instead of the original one. So in this research work a try has been attempted to study the mechanical behaviour of light weight concrete when we use coarse aggregate as waste coconut shell inside concrete.To improve the strength of concrete we also use the sisal bers in various proportions ranging between 1% to 5% in accordance to the binder weight. After the mechanical properties tests such as the compression test, spilt tensile strength, exural test, modulus of elasticity test, impact resistant test were conducted nally it was concluded that there was increment in the compression strength up to 5%, tensile strength was increased to 17% elastic modulus to 7% when the ber content used was 3%. Thus with the use of these waste materials it was found that the concrete's strength gets increased and it leads to the formation of sustainable concrete thus reducing the pollution in the environment.

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A comparative study on prediction models for strength properties of LWA concrete using artificial neural network

Cited by 15 publications

References 24 publications

A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete

A Comparison of Machine Learning Tools That Model the Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete

Effective Modeling for Construction Activities of Recycled Aggregate Concrete Using Artificial Neural Network

Effect on mechanical properties of lightweight sustainable concrete with the use of waste coconut shell as replacement for coarse aggregate

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