Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach
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| 024 | 7 | 0 | |a 10.1007/s10064-014-0638-0 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s10064-014-0638-0 | ||
| 245 | 0 | 0 | |a Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach |h [Elektronische Daten] |c [Edy Mohamad, Danial Jahed Armaghani, Ehsan Momeni, Seyed Alavi Nezhad Khalil Abad] |
| 520 | 3 | |a Many studies have shown that artificial neural networks (ANNs) are useful for predicting the unconfined compressive strength (UCS) of rocks. However, ANNs do have some deficiencies: they can get trapped in local minima and they have a slow learning rate. It is widely accepted that optimization algorithms such as particle swarm optimization (PSO) can improve ANN performance. This study investigated the application of a hybrid PSO-based ANN model to the prediction of rock UCS. To prepare a dataset for the predictive model, extensive laboratory tests (i.e., 160 tests in total) were conducted on 40 soft rock sample sets (mostly shale) presenting various weathering grades that were obtained from different excavation sites in Johor, Malaysia. The laboratory tests included the UCS test and other basic rock index tests (the Brazilian tensile strength test, point load index test, and ultrasonic test). When developing the predictive model of UCS, the results of the basic rock tests as well as the bulk densities of the samples were used as input parameters, while the UCS was set as the output of the predictive model. The value account for (VAF), root mean squared error (RMSE), and adjusted R 2 (coefficient of determination) were utilized to check the performances of the predictive models. The high performance indices of the proposed model highlight the superiority of the PSO-based ANN model for UCS prediction. | |
| 540 | |a Springer - Verlag GmbH & Co KG Berlin-Heidelberg, 2014 | ||
| 690 | 7 | |a Unconfined compressive strength |2 nationallicence | |
| 690 | 7 | |a Rock index tests |2 nationallicence | |
| 690 | 7 | |a Artificial neural network |2 nationallicence | |
| 690 | 7 | |a Particle swarm optimization |2 nationallicence | |
| 700 | 1 | |a Mohamad |D Edy |u Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia |4 aut | |
| 700 | 1 | |a Jahed Armaghani |D Danial |u Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia |4 aut | |
| 700 | 1 | |a Momeni |D Ehsan |u Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia |4 aut | |
| 700 | 1 | |a Alavi Nezhad Khalil Abad |D Seyed |u Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia |4 aut | |
| 773 | 0 | |t Bulletin of Engineering Geology and the Environment |d Springer Berlin Heidelberg |g 74/3(2015-08-01), 745-757 |x 1435-9529 |q 74:3<745 |1 2015 |2 74 |o 10064 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s10064-014-0638-0 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s10064-014-0638-0 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Mohamad |D Edy |u Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Jahed Armaghani |D Danial |u Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Momeni |D Ehsan |u Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Alavi Nezhad Khalil Abad |D Seyed |u Department of Geotechnics and Transportation, Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310, Skudai, Johor, Malaysia |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Bulletin of Engineering Geology and the Environment |d Springer Berlin Heidelberg |g 74/3(2015-08-01), 745-757 |x 1435-9529 |q 74:3<745 |1 2015 |2 74 |o 10064 | ||