Estimation of California bearing ratio for hill highways using advanced hybrid artificial neural network algorithms
California bearing ratio (CBR) is one of the important parameters that is used to express the strength of the pavement subgrade of railways, roadways, and airport runways. CBR is usually determined in the laboratory in soaked and unsoaked conditions, which is an exhaustive and time-consuming process...
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| Vydáno v: | Multiscale and Multidisciplinary Modeling, Experiments and Design Ročník 7; číslo 2; s. 1119 - 1144 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Cham
Springer International Publishing
01.06.2024
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| ISSN: | 2520-8160, 2520-8179 |
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| Abstract | California bearing ratio (CBR) is one of the important parameters that is used to express the strength of the pavement subgrade of railways, roadways, and airport runways. CBR is usually determined in the laboratory in soaked and unsoaked conditions, which is an exhaustive and time-consuming process. Therefore, to sidestep the operation of conducting actual laboratory tests, this study presents the development of efficient hybrid soft computing techniques, by hybridizing artificial neural network (ANN) with nature-inspired optimization algorithm, namely, gradient-based optimization (GBO), firefly algorithm (FF), cultural algorithms (CA), grey wolf optimization (GWO), genetic algorithm (GA), particle swarm optimization (PSO), Harris Hawk optimization (HHO), teaching learning-based optimization (TLBO), Whale optimization algorithm (WOA) and invasive weed optimization (IWO). For this purpose, a data set was prepared from the experimental results of soaked CBR of soil samples collected from an ongoing Nepal’s Mid-Hill Highway project. Based on the detailed comparative study one explicit model is proposed to estimate the CBR of soils in soaked conditions. The predictive accuracy of the proposed models was evaluated via several statistical and graphical parameters. Separate statistical indices were employed to evaluate the generalization capabilities of the developed models. In addition, in the end, the best predictive model was determined using a novel tool called order analysis. The results of the study reveal that the proposed artificial neural network coupled with the gradient-based optimizer (ANN–GBO) model attained the most accurate prediction (
R
2
= 0. 997and
R
2
= 0.956, during the training and testing phase) in predicting the soaked CBR. Based on the accuracies attained, the proposed ANN–GBO model has very potential to be an alternate solution to estimate the CBR value in different phases of civil engineering projects. |
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| AbstractList | California bearing ratio (CBR) is one of the important parameters that is used to express the strength of the pavement subgrade of railways, roadways, and airport runways. CBR is usually determined in the laboratory in soaked and unsoaked conditions, which is an exhaustive and time-consuming process. Therefore, to sidestep the operation of conducting actual laboratory tests, this study presents the development of efficient hybrid soft computing techniques, by hybridizing artificial neural network (ANN) with nature-inspired optimization algorithm, namely, gradient-based optimization (GBO), firefly algorithm (FF), cultural algorithms (CA), grey wolf optimization (GWO), genetic algorithm (GA), particle swarm optimization (PSO), Harris Hawk optimization (HHO), teaching learning-based optimization (TLBO), Whale optimization algorithm (WOA) and invasive weed optimization (IWO). For this purpose, a data set was prepared from the experimental results of soaked CBR of soil samples collected from an ongoing Nepal’s Mid-Hill Highway project. Based on the detailed comparative study one explicit model is proposed to estimate the CBR of soils in soaked conditions. The predictive accuracy of the proposed models was evaluated via several statistical and graphical parameters. Separate statistical indices were employed to evaluate the generalization capabilities of the developed models. In addition, in the end, the best predictive model was determined using a novel tool called order analysis. The results of the study reveal that the proposed artificial neural network coupled with the gradient-based optimizer (ANN–GBO) model attained the most accurate prediction (
R
2
= 0. 997and
R
2
= 0.956, during the training and testing phase) in predicting the soaked CBR. Based on the accuracies attained, the proposed ANN–GBO model has very potential to be an alternate solution to estimate the CBR value in different phases of civil engineering projects. |
| Author | Ghani, Sufyan Thapa, Ishwor |
| Author_xml | – sequence: 1 givenname: Ishwor surname: Thapa fullname: Thapa, Ishwor organization: Department of Civil Engineering, Sharda University – sequence: 2 givenname: Sufyan surname: Ghani fullname: Ghani, Sufyan email: Sufyan.ghani@sharda.ac.in organization: Department of Civil Engineering, Sharda University |
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| Cites_doi | 10.1016/j.earscirev.2022.103991 10.1007/978-981-19-6998-0_44 10.1016/j.jss.2007.05.005 10.1155/2022/8938836 10.1155/2023/8198648 10.1007/s13369-023-07962-y 10.3390/math11143064 10.1007/s12517-020-5273-4 10.3390/buildings13010255 10.1016/j.asoc.2021.107595 10.1007/s00521-015-1943-7 10.1007/978-981-19-6774-0_16 10.1007/s12594-020-1409-0 10.1007/978-3-030-26458-1_12 10.1007/s40996-023-01205-8 10.1007/s41939-022-00131-y 10.1016/j.matpr.2023.05.097 10.1016/j.eswa.2010.12.054 10.1007/s42107-023-00822-y 10.1016/j.asoc.2020.106738 10.1080/03052150410001647966 10.1007/s12517-022-10534-3 10.1007/s13369-019-03803-z 10.1029/2000JD900719 10.1007/s12517-020-5171-9 10.3390/ma15124330 10.3390/aerospace5010003 10.1016/j.asoc.2014.10.034 10.1007/s10706-018-0604-x 10.1007/s13369-020-04441-6 10.1007/s42947-022-00268-6 10.1007/s13369-022-06697-6 10.1016/j.cemconres.2021.106449 10.1016/j.enggeo.2021.106239 10.1016/j.advengsoft.2010.01.003 10.1007/s10706-013-9643-5 10.1007/s11440-022-01450-7 |
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| Keywords | Statistical parameters California bearing ratio Order analysis Artificial intelligence Gradient-based optimizer |
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| References | Amin, Iqbal, Ashfaq, Salami, Khan, Faraz, Alabdullah, Jalal (CR2) 2022 Erzin, Turkoz (CR14) 2016; 27 Prakash, Kumar, Rai (CR34) 2023 Yildirim, Gunaydin (CR44) 2011; 38 Bardhan, Singh, Ghani, Konstantakatos, Asteris (CR7) 2023; 11 Kassa, Wubineh (CR22) 2023; 2023 Nagaraju, Bahrami, Prasad, Mantena, Biswal, Islam (CR32) 2023; 13 Bardhan, Gokceoglu, Burman, Samui, Asteris (CR5) 2021; 291 Varghese, Babu, Bijukumar, Cyrus, Abraham (CR42) 2013; 31 Khatti, Grover (CR24) 2023 Ghani, Kumari, Jaiswal, Sawant (CR18) 2022; 15 Tenpe, Patel (CR40) 2020; 45 Taskiran (CR38) 2010; 41 Kim, Ordu, Arslan, Ko (CR27) 2023; 33 Raj Kiran, Ravi (CR35) 2008; 81 Ghani, Kumari, Ahmad (CR17) 2022; 47 Asteris, Skentou, Bardhan, Samui, Pilakoutas (CR3) 2021; 145 Khatti, Grover (CR26) 2023 Katte, Mfoyet, Manefouet, Wouatong, Bezeng (CR23) 2019; 37 Khatti, Grover (CR25) 2023; 6 Ghani, Kumari, Muthukkumaran-Kasinathan, Ayothiraman (CR16) 2023 Kumar, Gandhi, Bhattacharjya (CR30) 2020 Bardhan, Samui, Ghosh, Gandomi, Bhattacharyya (CR6) 2021; 110 Taylor (CR39) 2001; 106 Alam, Mondal, Shiuly (CR1) 2020; 95 Zhou, Zhu, Qiu, Armaghani, Zhou, Yong (CR45) 2022; 17 Verma, Kumar, Kumar, Ray, Khandelwal (CR43) 2023 CR29 CR28 Kurnaz, Kaya (CR31) 2020; 13 Vamsi Krishna, Sai Santosh, Sai Prasanth (CR41) 2023 Soltanali, Rohani, Abbaspour-Fard, Farinha (CR36) 2021; 98 Baghbani, Choudhury, Costa, Reiner (CR4) 2022; 228 Ceryan, Samui (CR11) 2020; 13 Dababneh, Kipouros, Whidborne (CR13) 2018 Coello Coello, Becerra (CR12) 2004; 36 Taha, Gabr, El-Badawy (CR37) 2019; 44 Bui, Al-Ansari, Le, Prakash, Pham (CR9) 2022 Ghani, Kumari, Choudhary (CR19) 2023 Huang, Ma, Wan, Chen (CR20) 2015; 27 S Prakash (269_CR34) 2023 TV Nagaraju (269_CR32) 2023; 13 B Yildirim (269_CR44) 2011; 38 SK Alam (269_CR1) 2020; 95 M Kim (269_CR27) 2023; 33 CA Coello Coello (269_CR12) 2004; 36 J Khatti (269_CR24) 2023 A Bardhan (269_CR7) 2023; 11 A Bardhan (269_CR6) 2021; 110 S Ghani (269_CR16) 2023 KE Taylor (269_CR39) 2001; 106 G Verma (269_CR43) 2023 J Khatti (269_CR25) 2023; 6 J Zhou (269_CR45) 2022; 17 N Ceryan (269_CR11) 2020; 13 VK Varghese (269_CR42) 2013; 31 M Huang (269_CR20) 2015; 27 269_CR28 O Dababneh (269_CR13) 2018 TF Kurnaz (269_CR31) 2020; 13 T Taskiran (269_CR38) 2010; 41 N Raj Kiran (269_CR35) 2008; 81 VY Katte (269_CR23) 2019; 37 S Taha (269_CR37) 2019; 44 SH Vamsi Krishna (269_CR41) 2023 A Baghbani (269_CR4) 2022; 228 PG Asteris (269_CR3) 2021; 145 S Ghani (269_CR18) 2022; 15 Y Erzin (269_CR14) 2016; 27 269_CR29 S Ghani (269_CR17) 2022; 47 D Kumar (269_CR30) 2020 H Soltanali (269_CR36) 2021; 98 SM Kassa (269_CR22) 2023; 2023 AR Tenpe (269_CR40) 2020; 45 S Ghani (269_CR19) 2023 MN Amin (269_CR2) 2022 A Bardhan (269_CR5) 2021; 291 QAT Bui (269_CR9) 2022 J Khatti (269_CR26) 2023 |
| References_xml | – volume: 228 year: 2022 ident: CR4 article-title: Application of artificial intelligence in geotechnical engineering: A state-of-the-art review publication-title: Earth-Sci Rev doi: 10.1016/j.earscirev.2022.103991 – start-page: 515 year: 2023 end-page: 527 ident: CR16 article-title: Plasticity-based liquefaction prediction using support vector machine and adaptive neuro-fuzzy inference system publication-title: Soil dynamics, earthquake and computational geotechnical engineering doi: 10.1007/978-981-19-6998-0_44 – volume: 81 start-page: 576 issue: 4 year: 2008 end-page: 583 ident: CR35 article-title: Software reliability prediction by soft computing techniques publication-title: J Syst Softw doi: 10.1016/j.jss.2007.05.005 – year: 2022 ident: CR9 article-title: Hybrid model: teaching learning-based optimization of artificial neural network (TLBO-ANN) for the prediction of soil permeability coefficient publication-title: Math Probl Eng doi: 10.1155/2022/8938836 – volume: 2023 start-page: 1 year: 2023 end-page: 11 ident: CR22 article-title: Use of machine learning to predict california bearing ratio of soils publication-title: Adv Civ Eng doi: 10.1155/2023/8198648 – year: 2023 ident: CR43 article-title: Application of KRR, K-NN and GPR algorithms for predicting the soaked CBR of fine-grained plastic soils publication-title: Arab J Sci Eng doi: 10.1007/s13369-023-07962-y – volume: 11 start-page: 3064 issue: 14 year: 2023 ident: CR7 article-title: Modelling soil compaction parameters using an enhanced hybrid intelligence paradigm of ANFIS and improved grey wolf optimiser publication-title: Mathematics doi: 10.3390/math11143064 – volume: 13 start-page: 288 issue: 7 year: 2020 ident: CR11 article-title: Application of soft computing methods in predicting uniaxial compressive strength of the volcanic rocks with different weathering degree publication-title: Arab J Geosci doi: 10.1007/s12517-020-5273-4 – volume: 13 start-page: 255 issue: 1 year: 2023 ident: CR32 article-title: Predicting California bearing ratio of lateritic soils using hybrid machine learning technique publication-title: Buildings doi: 10.3390/buildings13010255 – volume: 110 year: 2021 ident: CR6 article-title: ELM-based adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditions publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2021.107595 – volume: 27 start-page: 1415 issue: 5 year: 2016 end-page: 1426 ident: CR14 article-title: Use of neural networks for the prediction of the CBR value of some Aegean sands publication-title: Neural Comput Appl doi: 10.1007/s00521-015-1943-7 – start-page: 171 year: 2023 end-page: 185 ident: CR26 publication-title: Relationship between index properties and CBR of soil and prediction of CBR doi: 10.1007/978-981-19-6774-0_16 – ident: CR29 – volume: 95 start-page: 190 issue: 2 year: 2020 end-page: 196 ident: CR1 article-title: Prediction of CBR value of fine grained soils of Bengal Basin by genetic expression programming, artificial neural network and krigging method publication-title: J Geol Soc India doi: 10.1007/s12594-020-1409-0 – start-page: 203 year: 2020 end-page: 214 ident: CR30 publication-title: Introduction to invasive weed optimization method doi: 10.1007/978-3-030-26458-1_12 – year: 2023 ident: CR19 article-title: Geocell mattress reinforcement for bottom ash: a comprehensive study of load-settlement characteristics publication-title: Iran J Sci Technol Trans Civ Eng doi: 10.1007/s40996-023-01205-8 – volume: 6 start-page: 97 issue: 1 year: 2023 end-page: 121 ident: CR25 article-title: Prediction of soaked CBR of fine-grained soils using soft computing techniques publication-title: Multiscale Multidiscip Model Exp Des doi: 10.1007/s41939-022-00131-y – year: 2023 ident: CR41 article-title: Prediction of UCS and CBR of a stabilized Black-cotton soil using artificial intelligence approach: ANN publication-title: Mater Today Proc doi: 10.1016/j.matpr.2023.05.097 – volume: 38 start-page: 6381 issue: 5 year: 2011 end-page: 6391 ident: CR44 article-title: Estimation of California bearing ratio by using soft computing systems publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2010.12.054 – year: 2023 ident: CR34 article-title: A new technique based on the gorilla troop optimization coupled with artificial neural network for predicting the compressive strength of ultrahigh performance concrete publication-title: Asian J Civ Eng doi: 10.1007/s42107-023-00822-y – volume: 98 year: 2021 ident: CR36 article-title: A comparative study of statistical and soft computing techniques for reliability prediction of automotive manufacturing publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2020.106738 – volume: 36 start-page: 219 issue: 2 year: 2004 end-page: 236 ident: CR12 article-title: Efficient evolutionary optimization through the use of a cultural algorithm publication-title: Eng Optim doi: 10.1080/03052150410001647966 – volume: 15 start-page: 1262 issue: 14 year: 2022 ident: CR18 article-title: Comparative and parametric study of AI-based models for risk assessment against soil liquefaction for high-intensity earthquakes publication-title: Arab J Geosci doi: 10.1007/s12517-022-10534-3 – volume: 44 start-page: 8691 issue: 10 year: 2019 end-page: 8705 ident: CR37 article-title: Regression and neural network models for california bearing ratio prediction of typical granular materials in Egypt publication-title: Arab J Sci Eng doi: 10.1007/s13369-019-03803-z – volume: 106 start-page: 7183 issue: D7 year: 2001 end-page: 7192 ident: CR39 article-title: Summarizing multiple aspects of model performance in a single diagram publication-title: J Geophys Res Atmos doi: 10.1029/2000JD900719 – volume: 13 start-page: 159 issue: 4 year: 2020 ident: CR31 article-title: The performance comparison of the soft computing methods on the prediction of soil compaction parameters publication-title: Arab J Geosci doi: 10.1007/s12517-020-5171-9 – year: 2022 ident: CR2 article-title: Prediction of strength and CBR characteristics of chemically stabilized coal gangue: ANN and random forest tree approach publication-title: Materials doi: 10.3390/ma15124330 – year: 2018 ident: CR13 article-title: Application of an efficient gradient-based optimization strategy for aircraft wing structures publication-title: Aerospace doi: 10.3390/aerospace5010003 – volume: 27 start-page: 1 year: 2015 end-page: 10 ident: CR20 article-title: A sensor-software based on a genetic algorithm-based neural fuzzy system for modeling and simulating a wastewater treatment process publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2014.10.034 – volume: 37 start-page: 217 issue: 1 year: 2019 end-page: 234 ident: CR23 article-title: Correlation of California bearing ratio (CBR) value with soil properties of road subgrade soil publication-title: Geotech Geol Eng doi: 10.1007/s10706-018-0604-x – volume: 45 start-page: 4301 issue: 5 year: 2020 end-page: 4319 ident: CR40 article-title: Utilization of support vector models and gene expression programming for soil strength modeling publication-title: Arab J Sci Eng doi: 10.1007/s13369-020-04441-6 – year: 2023 ident: CR24 article-title: CBR prediction of pavement materials in unsoaked condition using LSSVM, LSTM-RNN, and ANN approaches publication-title: Int J Pavement Res Technol doi: 10.1007/s42947-022-00268-6 – volume: 47 start-page: 5411 issue: 4 year: 2022 end-page: 5441 ident: CR17 article-title: Prediction of the seismic effect on liquefaction behavior of fine-grained soils using artificial intelligence-based hybridized modeling publication-title: Arab J Sci Eng doi: 10.1007/s13369-022-06697-6 – volume: 33 start-page: 183 issue: 2 year: 2023 end-page: 194 ident: CR27 article-title: Prediction of California bearing ratio (CBR) for coarse- and fine-grained soils using the GMDH-model publication-title: Geomech Eng – volume: 145 year: 2021 ident: CR3 article-title: Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models publication-title: Cem Concr Res doi: 10.1016/j.cemconres.2021.106449 – volume: 291 year: 2021 ident: CR5 article-title: Efficient computational techniques for predicting the California bearing ratio of soil in soaked conditions publication-title: Eng Geol doi: 10.1016/j.enggeo.2021.106239 – ident: CR28 – volume: 41 start-page: 886 issue: 6 year: 2010 end-page: 892 ident: CR38 article-title: Prediction of California bearing ratio (CBR) of fine grained soils by AI methods publication-title: Adv Eng Softw doi: 10.1016/j.advengsoft.2010.01.003 – volume: 31 start-page: 1187 issue: 4 year: 2013 end-page: 1205 ident: CR42 article-title: Artificial neural networks: a solution to the ambiguity in prediction of engineering properties of fine-grained soils publication-title: Geotech Geol Eng doi: 10.1007/s10706-013-9643-5 – volume: 17 start-page: 1343 issue: 4 year: 2022 end-page: 1366 ident: CR45 article-title: Predicting tunnel squeezing using support vector machine optimized by whale optimization algorithm publication-title: Acta Geotech doi: 10.1007/s11440-022-01450-7 – volume: 145 year: 2021 ident: 269_CR3 publication-title: Cem Concr Res doi: 10.1016/j.cemconres.2021.106449 – volume: 44 start-page: 8691 issue: 10 year: 2019 ident: 269_CR37 publication-title: Arab J Sci Eng doi: 10.1007/s13369-019-03803-z – volume: 13 start-page: 288 issue: 7 year: 2020 ident: 269_CR11 publication-title: Arab J Geosci doi: 10.1007/s12517-020-5273-4 – volume: 110 year: 2021 ident: 269_CR6 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2021.107595 – ident: 269_CR28 – year: 2022 ident: 269_CR2 publication-title: Materials doi: 10.3390/ma15124330 – volume: 98 year: 2021 ident: 269_CR36 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2020.106738 – volume: 41 start-page: 886 issue: 6 year: 2010 ident: 269_CR38 publication-title: Adv Eng Softw doi: 10.1016/j.advengsoft.2010.01.003 – volume: 37 start-page: 217 issue: 1 year: 2019 ident: 269_CR23 publication-title: Geotech Geol Eng doi: 10.1007/s10706-018-0604-x – volume: 95 start-page: 190 issue: 2 year: 2020 ident: 269_CR1 publication-title: J Geol Soc India doi: 10.1007/s12594-020-1409-0 – year: 2023 ident: 269_CR34 publication-title: Asian J Civ Eng doi: 10.1007/s42107-023-00822-y – volume: 33 start-page: 183 issue: 2 year: 2023 ident: 269_CR27 publication-title: Geomech Eng – volume: 81 start-page: 576 issue: 4 year: 2008 ident: 269_CR35 publication-title: J Syst Softw doi: 10.1016/j.jss.2007.05.005 – volume: 6 start-page: 97 issue: 1 year: 2023 ident: 269_CR25 publication-title: Multiscale Multidiscip Model Exp Des doi: 10.1007/s41939-022-00131-y – year: 2023 ident: 269_CR43 publication-title: Arab J Sci Eng doi: 10.1007/s13369-023-07962-y – volume: 2023 start-page: 1 year: 2023 ident: 269_CR22 publication-title: Adv Civ Eng doi: 10.1155/2023/8198648 – volume: 38 start-page: 6381 issue: 5 year: 2011 ident: 269_CR44 publication-title: Expert Syst Appl doi: 10.1016/j.eswa.2010.12.054 – year: 2022 ident: 269_CR9 publication-title: Math Probl Eng doi: 10.1155/2022/8938836 – volume: 27 start-page: 1415 issue: 5 year: 2016 ident: 269_CR14 publication-title: Neural Comput Appl doi: 10.1007/s00521-015-1943-7 – volume: 36 start-page: 219 issue: 2 year: 2004 ident: 269_CR12 publication-title: Eng Optim doi: 10.1080/03052150410001647966 – volume: 13 start-page: 159 issue: 4 year: 2020 ident: 269_CR31 publication-title: Arab J Geosci doi: 10.1007/s12517-020-5171-9 – ident: 269_CR29 – start-page: 171 volume-title: Relationship between index properties and CBR of soil and prediction of CBR year: 2023 ident: 269_CR26 doi: 10.1007/978-981-19-6774-0_16 – volume: 17 start-page: 1343 issue: 4 year: 2022 ident: 269_CR45 publication-title: Acta Geotech doi: 10.1007/s11440-022-01450-7 – year: 2023 ident: 269_CR19 publication-title: Iran J Sci Technol Trans Civ Eng doi: 10.1007/s40996-023-01205-8 – volume: 15 start-page: 1262 issue: 14 year: 2022 ident: 269_CR18 publication-title: Arab J Geosci doi: 10.1007/s12517-022-10534-3 – volume: 13 start-page: 255 issue: 1 year: 2023 ident: 269_CR32 publication-title: Buildings doi: 10.3390/buildings13010255 – volume: 31 start-page: 1187 issue: 4 year: 2013 ident: 269_CR42 publication-title: Geotech Geol Eng doi: 10.1007/s10706-013-9643-5 – volume: 291 year: 2021 ident: 269_CR5 publication-title: Eng Geol doi: 10.1016/j.enggeo.2021.106239 – year: 2023 ident: 269_CR24 publication-title: Int J Pavement Res Technol doi: 10.1007/s42947-022-00268-6 – year: 2018 ident: 269_CR13 publication-title: Aerospace doi: 10.3390/aerospace5010003 – volume: 45 start-page: 4301 issue: 5 year: 2020 ident: 269_CR40 publication-title: Arab J Sci Eng doi: 10.1007/s13369-020-04441-6 – volume: 27 start-page: 1 year: 2015 ident: 269_CR20 publication-title: Appl Soft Comput doi: 10.1016/j.asoc.2014.10.034 – start-page: 515 volume-title: Soil dynamics, earthquake and computational geotechnical engineering year: 2023 ident: 269_CR16 doi: 10.1007/978-981-19-6998-0_44 – volume: 228 year: 2022 ident: 269_CR4 publication-title: Earth-Sci Rev doi: 10.1016/j.earscirev.2022.103991 – volume: 11 start-page: 3064 issue: 14 year: 2023 ident: 269_CR7 publication-title: Mathematics doi: 10.3390/math11143064 – volume: 47 start-page: 5411 issue: 4 year: 2022 ident: 269_CR17 publication-title: Arab J Sci Eng doi: 10.1007/s13369-022-06697-6 – start-page: 203 volume-title: Introduction to invasive weed optimization method year: 2020 ident: 269_CR30 doi: 10.1007/978-3-030-26458-1_12 – volume: 106 start-page: 7183 issue: D7 year: 2001 ident: 269_CR39 publication-title: J Geophys Res Atmos doi: 10.1029/2000JD900719 – year: 2023 ident: 269_CR41 publication-title: Mater Today Proc doi: 10.1016/j.matpr.2023.05.097 |
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| Snippet | California bearing ratio (CBR) is one of the important parameters that is used to express the strength of the pavement subgrade of railways, roadways, and... |
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| Title | Estimation of California bearing ratio for hill highways using advanced hybrid artificial neural network algorithms |
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