Prediction of Later-Age Concrete Compressive Strength Using Feedforward Neural Network
Accurate prediction of the concrete compressive strength is an important task that helps to avoid costly and time-consuming experiments. Notably, the determination of the later-age concrete compressive strength is more difficult due to the time required to perform experiments. Therefore, predicting...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
|
| Series: | Advances in Materials Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/9682740 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832547756216942592 |
|---|---|
| author | Thuy-Anh Nguyen Hai-Bang Ly Hai-Van Thi Mai Van Quan Tran |
| author_facet | Thuy-Anh Nguyen Hai-Bang Ly Hai-Van Thi Mai Van Quan Tran |
| author_sort | Thuy-Anh Nguyen |
| collection | DOAJ |
| description | Accurate prediction of the concrete compressive strength is an important task that helps to avoid costly and time-consuming experiments. Notably, the determination of the later-age concrete compressive strength is more difficult due to the time required to perform experiments. Therefore, predicting the compressive strength of later-age concrete is crucial in specific applications. In this investigation, an approach using a feedforward neural network (FNN) machine learning algorithm was proposed to predict the compressive strength of later-age concrete. The proposed model was fully evaluated in terms of performance and prediction capability over statistical results of 1000 simulations under a random sampling effect. The results showed that the proposed algorithm was an excellent predictor and might be useful for engineers to avoid time-consuming experiments with the statistical performance indicators, namely, the Pearson correlation coefficient (R), root-mean-squared error (RMSE), and mean squared error (MAE) for the training and testing parts of 0.9861, 2.1501, 1.5650 and 0.9792, 2.8510, 2.1361, respectively. The results also indicated that the FNN model was superior to classical machine learning algorithms such as random forest and Gaussian process regression, as well as empirical formulations proposed in the literature. |
| format | Article |
| id | doaj-art-0a8d5eefb2e3416cb2a969a9d7089b5f |
| institution | Kabale University |
| issn | 1687-8434 1687-8442 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Materials Science and Engineering |
| spelling | doaj-art-0a8d5eefb2e3416cb2a969a9d7089b5f2025-02-03T06:43:32ZengWileyAdvances in Materials Science and Engineering1687-84341687-84422020-01-01202010.1155/2020/96827409682740Prediction of Later-Age Concrete Compressive Strength Using Feedforward Neural NetworkThuy-Anh Nguyen0Hai-Bang Ly1Hai-Van Thi Mai2Van Quan Tran3University of Transport Technology, Hanoi 100000, VietnamUniversity of Transport Technology, Hanoi 100000, VietnamUniversity of Transport Technology, Hanoi 100000, VietnamUniversity of Transport Technology, Hanoi 100000, VietnamAccurate prediction of the concrete compressive strength is an important task that helps to avoid costly and time-consuming experiments. Notably, the determination of the later-age concrete compressive strength is more difficult due to the time required to perform experiments. Therefore, predicting the compressive strength of later-age concrete is crucial in specific applications. In this investigation, an approach using a feedforward neural network (FNN) machine learning algorithm was proposed to predict the compressive strength of later-age concrete. The proposed model was fully evaluated in terms of performance and prediction capability over statistical results of 1000 simulations under a random sampling effect. The results showed that the proposed algorithm was an excellent predictor and might be useful for engineers to avoid time-consuming experiments with the statistical performance indicators, namely, the Pearson correlation coefficient (R), root-mean-squared error (RMSE), and mean squared error (MAE) for the training and testing parts of 0.9861, 2.1501, 1.5650 and 0.9792, 2.8510, 2.1361, respectively. The results also indicated that the FNN model was superior to classical machine learning algorithms such as random forest and Gaussian process regression, as well as empirical formulations proposed in the literature.http://dx.doi.org/10.1155/2020/9682740 |
| spellingShingle | Thuy-Anh Nguyen Hai-Bang Ly Hai-Van Thi Mai Van Quan Tran Prediction of Later-Age Concrete Compressive Strength Using Feedforward Neural Network Advances in Materials Science and Engineering |
| title | Prediction of Later-Age Concrete Compressive Strength Using Feedforward Neural Network |
| title_full | Prediction of Later-Age Concrete Compressive Strength Using Feedforward Neural Network |
| title_fullStr | Prediction of Later-Age Concrete Compressive Strength Using Feedforward Neural Network |
| title_full_unstemmed | Prediction of Later-Age Concrete Compressive Strength Using Feedforward Neural Network |
| title_short | Prediction of Later-Age Concrete Compressive Strength Using Feedforward Neural Network |
| title_sort | prediction of later age concrete compressive strength using feedforward neural network |
| url | http://dx.doi.org/10.1155/2020/9682740 |
| work_keys_str_mv | AT thuyanhnguyen predictionoflaterageconcretecompressivestrengthusingfeedforwardneuralnetwork AT haibangly predictionoflaterageconcretecompressivestrengthusingfeedforwardneuralnetwork AT haivanthimai predictionoflaterageconcretecompressivestrengthusingfeedforwardneuralnetwork AT vanquantran predictionoflaterageconcretecompressivestrengthusingfeedforwardneuralnetwork |