Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network
Stabilized dredged sediments are used as a backfilling material to reduce construction costs and a solution to environmental protection. Therefore, the compressive strength is an important criterion to determine the stabilized dredged sediments application such as road construction, building constru...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2021/6656084 |
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| author | Van Quan Tran |
| author_facet | Van Quan Tran |
| author_sort | Van Quan Tran |
| collection | DOAJ |
| description | Stabilized dredged sediments are used as a backfilling material to reduce construction costs and a solution to environmental protection. Therefore, the compressive strength is an important criterion to determine the stabilized dredged sediments application such as road construction, building construction, and highway construction. Using the traditional method such as empirical approach and experimental methods, the determination of compressive strength of stabilized dredged sediments is difficult due to the complexity of this composite material. In this investigation, the artificial neural network (ANN) model is introduced to forecast the compressive strength. To perform the simulation, 51 experimental datasets were collected from the literature. The dataset consists of 4 input variables (water content, cement content, air foam content, and waste fishing net content) and output variable (compressive strength). Evaluation of the models was made and compared on training dataset (70% data) and testing dataset (30% remaining data) by the criteria of Pearson’s correlation coefficient (R), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results show that the ANN model can accurately predict the compressive strength of stabilized dredged sediments with low water content. The cement content is the most important input affecting the unconfined compressive strength. The important input affecting the unconfined compressive strength can be in the following order: cement content > air foam content > water content > waste fishing net. |
| format | Article |
| id | doaj-art-4b313920b7f24c5bae3d0c6efa3391e0 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-4b313920b7f24c5bae3d0c6efa3391e02025-02-03T01:00:15ZengWileyAdvances in Civil Engineering1687-80861687-80942021-01-01202110.1155/2021/66560846656084Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural NetworkVan Quan Tran0University of Transport Technology, Hanoi 100000, VietnamStabilized dredged sediments are used as a backfilling material to reduce construction costs and a solution to environmental protection. Therefore, the compressive strength is an important criterion to determine the stabilized dredged sediments application such as road construction, building construction, and highway construction. Using the traditional method such as empirical approach and experimental methods, the determination of compressive strength of stabilized dredged sediments is difficult due to the complexity of this composite material. In this investigation, the artificial neural network (ANN) model is introduced to forecast the compressive strength. To perform the simulation, 51 experimental datasets were collected from the literature. The dataset consists of 4 input variables (water content, cement content, air foam content, and waste fishing net content) and output variable (compressive strength). Evaluation of the models was made and compared on training dataset (70% data) and testing dataset (30% remaining data) by the criteria of Pearson’s correlation coefficient (R), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The results show that the ANN model can accurately predict the compressive strength of stabilized dredged sediments with low water content. The cement content is the most important input affecting the unconfined compressive strength. The important input affecting the unconfined compressive strength can be in the following order: cement content > air foam content > water content > waste fishing net.http://dx.doi.org/10.1155/2021/6656084 |
| spellingShingle | Van Quan Tran Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network Advances in Civil Engineering |
| title | Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network |
| title_full | Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network |
| title_fullStr | Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network |
| title_full_unstemmed | Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network |
| title_short | Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network |
| title_sort | compressive strength prediction of stabilized dredged sediments using artificial neural network |
| url | http://dx.doi.org/10.1155/2021/6656084 |
| work_keys_str_mv | AT vanquantran compressivestrengthpredictionofstabilizeddredgedsedimentsusingartificialneuralnetwork |