Artificial neural network based modelling approach for strength prediction of concrete incorporating agricultural and construction wastes

Getahun, Mulusew Aderaw; Shitote, Stanley Muse; Gariy, Zachary C. Abiero

doi:10.1016/j.conbuildmat.2018.09.097

Cited by 199 publications

(86 citation statements)

References 19 publications

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“…MLR was able to forecast accurately the slump of the concrete. Our work was compared favourably the work of Getahun et al, (2018)…”

Section: Resultsmentioning

confidence: 99%

Compressive Strength and Slump Prediction of Two Blended Agro Waste Materials Concretes

Onikeku¹,

Shitote²,

Mwero³

et al. 2019

TOCIEJ

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Background: Agro industrial wastes such as Bamboo Leaf Ash (BLA) and Bagasse Ash (BA) need to be employed so as to replace cement in order to produce cheaper concrete, which, in turn, save the environment. Objective: This research focuses on the compressive strength and slump based on Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models for forecasting of compressive strength and slump value for concrete by blending BLA and BA as partial supplementary cement materials accordingly. Methods: Three-layer perceptron was constructed through R (nnet package). A sum total of eleven artificial neural networks were formulated using 214 data sets attained from 27 laboratory concrete mixtures performed. Results: The neural network model forecasted the compressive strength for training, testing and validation with predicted errors of 0.802 MPa and 1.380 MPa. The model over forecasted the compressive strength averagely by 0.644 MPa and 1.905 MPa. The forecasted compressive strength changed averagely by 2.328% and 3.946%. The average difference between the predicted and experimental values was 0.588 MPa and 1.050 MPa. The coefficients of determination were 0.961 and 0.905. The MLR model predicted the slump with predictive error values of 6.634 mm and 8.374 mm. The predicted slump deviated on average by 3.633% and 8.034%. The residual error was 3.075 on 12 degrees of freedom. The multiple R2 and adjusted R2 were 0.9336 and 0.9115. The P-value was found to be 5.639e-07. Conclusion: The results show that ANN and MLR are potent tools for forecasting the compressive strength and slump of concrete blending bamboo leaf ash and baggage ash. Hence, this contributes towards forecasting of the compressive strength and slump of BLA and BA blended concrete. They extends 28 days compressive strength usually found in the literature to 56 and 90 days compressive strength and there was a remarkable improvement as curing age increases. The slump of combined effect of blending BLA and BA at different percentage replacements was tested. In this study, we used BLA blended with BA to produce concrete which is an innovation.

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“…MLR was able to forecast accurately the slump of the concrete. Our work was compared favourably the work of Getahun et al, (2018)…”

Section: Resultsmentioning

confidence: 99%

Compressive Strength and Slump Prediction of Two Blended Agro Waste Materials Concretes

Onikeku¹,

Shitote²,

Mwero³

et al. 2019

TOCIEJ

View full text Add to dashboard Cite

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“…This makes ANNs a powerful instrument for solving some of the complex engineering problems. The processing elements of a neural network are similar to the neuron in the brain, which consists of many simple computational elements arranged in layers [21]. Yeh [22] investigated the potential of using neural networks to determine the effect of fly ash replacements on early and late compressive strength of low and high-strength concretes.…”

Section: Artificial Neural Network Methodsmentioning

confidence: 99%

“…Getahun et al [21] developed an ANN model for predicting the strength of concrete incorporating rice husk ash and reclaimed asphalt pavement as partial replacements of Portland cement and virgin aggregates respectively. The ANN model predicted the compressive and tensile splitting strengths with prediction lows error values.…”

Section: Strength Models Of Concrete Using Machine Learning Methodsmentioning

confidence: 99%

“…RMSE MAE R Bresler and Pister (1958) [4] 137.5 100.8 0.559 88.20 Ottosen (1977) [5] 26.84 19.90 0.890 14.20 Willam and Warnke (1975) [6] 47.73 28.17 0,840 25.94 ANN method [22] 5. [26] 13.58 9.82 0.984 6.84 GP method II (Babanajad et al 2017) [26] 7.87 5.91 0.990 3.95 GP method III (Babanajad et al 2017) [26] 11.14 7.11 0.988 5.60 ANN compressive (Getahum, et al 2018) [21] 0.64 0.48 --ANN compressive (Getahum, et al 2018) [21] 0.07 0.05 --ANN method (Kaplan, et al 2019) [22] 6.24 4.85 0.996 -…”

Section: Researchermentioning

confidence: 99%

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A Comparative Study of Machine Learning Methods for Compressive Strength of Concrete

Silva¹,

Moita²

2019

World Congress on Civil, Structural, and Environmental Engineering

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This paper introduces a comparative study for the compressive strength of concrete by employing machine learning approaches such as Genetic Programming (GP) and Artificial Neural Network (ANN). The simulation of concrete strength is strongly needed to better understand its behaviours under different conditions and loads. Since many studies predict the comprehensive strength of conventional concrete from hardened characteristics, based on the data points gathered from different experimental tests, empirical models have been developed and verified in the past years. Proposed models are more reliable if the numbers of tests increase and their repeatability increase as well. However, these models are designed for a specific range of concrete strengths. On the other hand, numerical models are more reliable since they are devised based on theoretical rules which could consider behaviours of concrete under different loading paths. But, the validation of these models is made by different loading paths with different configurations that result in costly experiments and both models use only principal stresses and strains in their formulation. Employing machine learning approaches instead of traditional models makes it possible to develop a better understanding of the compressive strength of concrete. Hence, the focus of this paper is the application of machine learning process and their suitability to model concrete compressive strength compared with early models obtained from the literature and compared with some conventional approaches.

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“…In addition to the various studies mentioned above, various ANN models have been proposed to predict the tensile and compressive strength of concrete [28][29][30][31][32]. The proposed models considered various input data and showed good precision and accuracy compared with the experimental results.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Dynamic Stiffness of Resilient Materials using Artificial Neural Network (ANN) Technique

2019

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High-rise residential buildings are constructed in countries with high population density in response to the need to utilize small development areas. As many high-rise buildings are being constructed, issues of floor impact sound tend to occur in buildings. In general, resilient materials are implemented between the slab and the finishing mortar to control the floor impact sound. Various mechanical properties of resilient materials can affect the floor impact sound. To investigate the impact sound reduction capacity, various experimental tests were conducted. The test results show that the floor impact sound reduction capacity has a close relationship with the dynamic stiffness of resilient materials. A total of six different kinds of resilient materials were loaded under four loading conditions. The test results show that loading time, loading, and material properties influence the change in dynamic stiffness. Artificial neural network (ANN) technique was implemented to obtain the responses between the deflection and dynamic stiffness. Three different algorithms were considered in the ANN models and the trained results were analyzed based on the root mean square error. The feasibility of using the ANN technique was verified with a high and consistent level of accuracy.

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Artificial neural network based modelling approach for strength prediction of concrete incorporating agricultural and construction wastes

Cited by 199 publications

References 19 publications

Compressive Strength and Slump Prediction of Two Blended Agro Waste Materials Concretes

Compressive Strength and Slump Prediction of Two Blended Agro Waste Materials Concretes

A Comparative Study of Machine Learning Methods for Compressive Strength of Concrete

Prediction of the Dynamic Stiffness of Resilient Materials using Artificial Neural Network (ANN) Technique

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