{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,23]],"date-time":"2026-01-23T23:26:30Z","timestamp":1769210790513,"version":"3.49.0"},"reference-count":67,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T00:00:00Z","timestamp":1720569600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T00:00:00Z","timestamp":1720569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s12145-024-01398-0","type":"journal-article","created":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T06:02:31Z","timestamp":1720591351000},"page":"4507-4526","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Assessing the shear strength of sandy soil reinforced with polyethylene-terephthalate: an AI-based approach"],"prefix":"10.1007","volume":"17","author":[{"given":"Masoud","family":"Samaei","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Morteza","family":"Alinejad Omran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohsen","family":"Keramati","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reza","family":"Naderi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roohollah","family":"Shirani Faradonbeh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,10]]},"reference":[{"key":"1398_CR1","unstructured":"Acharyya R, Lahiri A, Mukherjee SP, Raghu PV (2013) Improvement of undrained shear strength of clayey soil with pet bottle strips. In Proceedings of Indian Geotechnical Conference, pp 22\u201324"},{"key":"1398_CR2","doi-asserted-by":"publisher","first-page":"608","DOI":"10.3390\/toxins15100608","volume":"15","author":"JM Ahn","year":"2023","unstructured":"Ahn JM, Kim J, Kim K (2023) Ensemble machine learning of gradient boosting (XGBoost, LightGBM, CatBoost) and attention-based CNN-LSTM for harmful algal blooms forecasting. Toxins 15:608","journal-title":"Toxins"},{"key":"1398_CR3","doi-asserted-by":"crossref","unstructured":"Alvarez A, Sosa J, Duran G, Pacheco L (2020) Improved mechanical properties of a high plasticity clay soil by adding recycled PET. In IOP Conf Ser: Mater Sci Eng 758:012075","DOI":"10.1088\/1757-899X\/758\/1\/012075"},{"key":"1398_CR4","unstructured":"Astm-D3080 (2011) Standard Test Method for Direct Shear Test of Soils Under Consolidated Drained Conditions. ASTM, USA"},{"key":"1398_CR5","unstructured":"Astm-D422 (2016) Standard Test Method for Particle-Size Analysis of Soils. ASTM, USA"},{"key":"1398_CR6","unstructured":"Azmi SS, Baliga S (2020) An overview of boosting decision tree algorithms utilizing AdaBoost and XGBoost boosting strategies. Int Res J Eng Technol 7:6867\u20136870"},{"key":"1398_CR7","doi-asserted-by":"publisher","first-page":"481","DOI":"10.1016\/j.wasman.2010.09.018","volume":"31","author":"GL Babu","year":"2011","unstructured":"Babu GL, Chouksey SK (2011) Stress-strain response of plastic waste mixed soil. Waste Manag 31:481\u2013488","journal-title":"Waste Manag"},{"key":"1398_CR8","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1016\/j.geotexmem.2015.04.003","volume":"43","author":"E Botero","year":"2015","unstructured":"Botero E, Ossa A, Sherwell G, Ovando-Shelley E (2015) Stress-strain behavior of a silty soil reinforced with polyethylene terephthalate (PET). Geotext Geomembr 43:363\u2013369","journal-title":"Geotext Geomembr"},{"key":"1398_CR9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13174-017-0073-0","volume":"9","author":"R Boutaba","year":"2018","unstructured":"Boutaba R, Salahuddin MA, Limam N, Ayoubi S, Shahriar N, Estrada-Solano F, Caicedo OM (2018) A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. J Int Serv Appl 9:1\u201399","journal-title":"J Int Serv Appl"},{"key":"1398_CR10","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45:5\u201332","journal-title":"Mach Learn"},{"key":"1398_CR11","doi-asserted-by":"crossref","unstructured":"Carvalho B, Casagrande M, Farias M (2019) Evaluation of resilient behavior of a clayey soil with polyethylene terephthalate (PET) insertion for application in pavements base. In Geotechnical engineering in the XXI century: lessons learned and future challenges, pp 1510\u20131517","DOI":"10.1051\/e3sconf\/20199212006"},{"key":"1398_CR12","doi-asserted-by":"crossref","unstructured":"Charles V, Gherman T, Paliza JC (2022) The Gini index: a modern measure of inequality. In: Charles V, Emrouznejad A (eds) Modern Indices for International Economic Diplomacy. Palgrave Macmillan, Cham, pp 55\u201384","DOI":"10.1007\/978-3-030-84535-3_3"},{"key":"1398_CR13","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"key":"1398_CR14","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1061\/(ASCE)1090-0241(2002)128:6(462)","volume":"128","author":"NC Consoli","year":"2002","unstructured":"Consoli NC, Montardo JP, Prietto PDM, Pasa GS (2002) Engineering behavior of a sand reinforced with plastic waste. J Geotech Geoenviron Eng 128:462\u2013472","journal-title":"J Geotech Geoenviron Eng"},{"key":"1398_CR15","doi-asserted-by":"publisher","first-page":"3173","DOI":"10.1007\/s00521-022-07856-4","volume":"35","author":"S Demir","year":"2023","unstructured":"Demir S, Sahin EK (2023) An investigation of feature selection methods for soil liquefaction prediction based on tree-based ensemble algorithms using AdaBoost, gradient boosting, and XGBoost. Neural Comput Appl 35:3173\u20133190","journal-title":"Neural Comput Appl"},{"issue":"7","key":"1398_CR16","doi-asserted-by":"publisher","first-page":"149","DOI":"10.3390\/info9070149","volume":"9","author":"S Dhaliwal","year":"2018","unstructured":"Dhaliwal S, Nahid A, Abbas R (2018) Effective intrusion detection system using XGBoost. Information 9(7):149","journal-title":"Information"},{"key":"1398_CR17","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1007\/s10706-019-01057-y","volume":"38","author":"H Fathi","year":"2020","unstructured":"Fathi H, Chenari RJ, Vafaeian M (2020) Shaking Table Study on PET Strips-Sand Mixtures Using Laminar Box Modelling. Geotech Geol Eng 38:683\u2013694","journal-title":"Geotech Geol Eng"},{"key":"1398_CR18","doi-asserted-by":"publisher","first-page":"15","DOI":"10.3390\/computation8010015","volume":"8","author":"M Frank","year":"2020","unstructured":"Frank M, Drikakis D, Charissis V (2020) Machine-learning methods for computational science and engineering. Computation 8:15","journal-title":"Computation"},{"key":"1398_CR19","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55:119\u2013139","journal-title":"J Comput Syst Sci"},{"key":"1398_CR20","doi-asserted-by":"crossref","unstructured":"Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 29:1189\u20131232","DOI":"10.1214\/aos\/1013203451"},{"key":"1398_CR21","unstructured":"Gwetu MV, Tapamo J-R, Viriri S (2019) Exploring the impact of purity gap gain on the efficiency and effectiveness of random forest feature selection. Computational Collective Intelligence: 11th International Conference, ICCCI 2019, Hendaye, France, September 4\u20136, 2019, Proceedings, Part I 11. Springer"},{"key":"1398_CR22","doi-asserted-by":"crossref","unstructured":"Hafez M, Mousa R, Awed A, El-Badawy S (2019) Soil Reinforcement using recycled plastic waste for sustainable pavements. In: El-Badawy S, Valentin J (eds) Sustainable solutions for railways and transportation engineering. GeoMEast 2018. Sustainable Civil Infrastructures. Springer, Cham","DOI":"10.1007\/978-3-030-01911-2_2"},{"key":"1398_CR23","doi-asserted-by":"publisher","first-page":"2790","DOI":"10.3390\/w13192790","volume":"13","author":"A Hannan","year":"2021","unstructured":"Hannan A, Anmala J (2021) Classification and Prediction of Fecal Coliform in Stream Waters Using Decision Trees (DTs) for Upper Green River Watershed, Kentucky, USA. Water 13:2790","journal-title":"Water"},{"key":"1398_CR24","doi-asserted-by":"crossref","unstructured":"Hastie T, Rosset S, Zhu J, Zou H (2009) Multi-class adaboost. Statistics and its interface 2:349\u201360","DOI":"10.4310\/SII.2009.v2.n3.a8"},{"key":"1398_CR25","doi-asserted-by":"publisher","first-page":"180235","DOI":"10.1109\/ACCESS.2019.2952107","volume":"7","author":"A Javeed","year":"2019","unstructured":"Javeed A, Zhou SJ, Liao YJ, Qasim I, Noor A, Nour R (2019) An Intelligent Learning System Based on Random Search Algorithm and Optimized Random Forest Model for Improved Heart Disease Detection. Ieee Access 7:180235\u2013180243","journal-title":"Ieee Access"},{"key":"1398_CR26","doi-asserted-by":"publisher","first-page":"7367","DOI":"10.1007\/s13369-022-06560-8","volume":"47","author":"T Kavzoglu","year":"2022","unstructured":"Kavzoglu T, Teke A (2022) Predictive Performances of ensemble machine learning algorithms in landslide susceptibility mapping using random forest, extreme gradient boosting (XGBoost) and natural gradient boosting (NGBoost). Arab J Sci Eng 47:7367\u20137385","journal-title":"Arab J Sci Eng"},{"key":"1398_CR27","doi-asserted-by":"crossref","unstructured":"Kuhn M, Johnson K (2013) Applied predictive modeling, New York: Springer 613","DOI":"10.1007\/978-1-4614-6849-3"},{"key":"1398_CR28","doi-asserted-by":"publisher","first-page":"977","DOI":"10.1016\/S0267-7261(02)00122-7","volume":"22","author":"J Li","year":"2002","unstructured":"Li J, Ding DW (2002) Nonlinear elastic behavior of fiber-reinforced soil under cyclic loading. Soil Dyn Earthq Eng 22:977\u2013983","journal-title":"Soil Dyn Earthq Eng"},{"key":"1398_CR29","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1186\/s40537-021-00473-3","volume":"8","author":"H Li","year":"2021","unstructured":"Li H, Sheu PC-Y (2021) A scalable association rule learning heuristic for large datasets. J Big Data 8:86","journal-title":"J Big Data"},{"key":"1398_CR30","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1080\/13467581.2019.1696203","volume":"18","author":"CL Lin","year":"2019","unstructured":"Lin CL, Fan CL (2019) Evaluation of CART, CHAID, and QUEST algorithms: a case study of construction defects in Taiwan. J Asian Archit Build Eng 18:539\u2013553","journal-title":"J Asian Archit Build Eng"},{"key":"1398_CR31","doi-asserted-by":"publisher","first-page":"1028","DOI":"10.1016\/j.jrmge.2021.08.018","volume":"14","author":"L Liu","year":"2022","unstructured":"Liu L, Zhou W, Gutierrez M (2022) Effectiveness of predicting tunneling-induced ground settlements using machine learning methods with small datasets. J Rock Mech Geotech Eng 14:1028\u20131041","journal-title":"J Rock Mech Geotech Eng"},{"key":"1398_CR32","doi-asserted-by":"publisher","first-page":"04019218","DOI":"10.1061\/(ASCE)MT.1943-5533.0002863","volume":"31","author":"NDL Louzada","year":"2019","unstructured":"Louzada NDL, JaC Malko, Casagrande MD (2019) Behavior of Clayey Soil Reinforced with Polyethylene Terephthalate. J Mater Civil Eng 31:04019218","journal-title":"J Mater Civil Eng"},{"key":"1398_CR33","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1186\/1471-2156-5-32","volume":"5","author":"KL Lunetta","year":"2004","unstructured":"Lunetta KL, Hayward LB, Segal J, Van Eerdewegh P (2004) Screening large-scale association study data: exploiting interactions using random forests. BMC Genet 5:32","journal-title":"BMC Genet"},{"key":"1398_CR34","doi-asserted-by":"publisher","first-page":"1381","DOI":"10.1061\/(ASCE)0733-9410(1994)120:8(1381)","volume":"120","author":"MH Maher","year":"1994","unstructured":"Maher MH, Ho YC (1994) Mechanical-Properties of Kaolinite Fiber Soil Composite. J Geotech Eng-Asce 120:1381\u20131393","journal-title":"J Geotech Eng-Asce"},{"key":"1398_CR35","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1080\/19386362.2017.1298300","volume":"12","author":"NR Malidarreh","year":"2018","unstructured":"Malidarreh NR, Shooshpasha I, Mirhosseini SM, Dehestani M (2018) Effects of reinforcement on mechanical behaviour of cement treated sand using direct shear and triaxial tests. Int J Geotech Eng 12:491\u2013499","journal-title":"Int J Geotech Eng"},{"key":"1398_CR36","doi-asserted-by":"crossref","unstructured":"Mantovani RG, Horv\u00e1th T, Cerri R, Vanschoren J, De Carvalho AC (2016) Hyper-parameter tuning of a decision tree induction algorithm. 2016 5th Brazilian Conference on Intelligent Systems (BRACIS). IEEE","DOI":"10.1109\/BRACIS.2016.018"},{"key":"1398_CR37","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.conbuildmat.2018.09.074","volume":"190","author":"B Mishra","year":"2018","unstructured":"Mishra B, Gupta MK (2018) Use of randomly oriented polyethylene terephthalate (PET) fiber in combination with fly ash in subgrade of flexible pavement. Constr Build Mater 190:95\u2013107","journal-title":"Constr Build Mater"},{"key":"1398_CR38","doi-asserted-by":"crossref","unstructured":"Moghaddas Tafreshi SN, Parvizi Omran M, Rahimi M, Dawson A (2021) Experimental investigation of the behavior of soil reinforced with waste plastic bottles under cyclic loads. Transp Geotech 26:100455","DOI":"10.1016\/j.trgeo.2020.100455"},{"key":"1398_CR39","doi-asserted-by":"crossref","unstructured":"Momeni E, Samaei M, Hashemi A, Dowlatshahi MB (2023) A review on the application of soft computing techniques in foundation engineering. Artificial intelligence in mechatronics and civil engineering: Bridging the Gap 16:111\u2013133","DOI":"10.1007\/978-981-19-8790-8_5"},{"key":"1398_CR40","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-10-7290-1_76","volume-title":"Fly ash as backfill material in slopes using waste pet bottles as reinforcement","author":"MB Nadaf","year":"2019","unstructured":"Nadaf MB, Dutta S, Mandal J (2019) Fly ash as backfill material in slopes using waste pet bottles as reinforcement. Springer, Waste Management and Resource Efficiency"},{"key":"1398_CR41","doi-asserted-by":"publisher","unstructured":"Naghadehi MZ, Samaei M, Ranjbarnia M, Nourani V (2018) Corrigendum to \u201cState-of-the-art predictive modeling of TBM performance in changing geological conditions through gene expression programming\u201d [Measurement 126 (2018) 46\u201357] (Measurement (2018) 126 (46\u201357), (S0263224118304366). Measurement: Journal of the International Measurement Confederation.\u00a0https:\/\/doi.org\/10.1016\/j.measurement.2018\u00a0","DOI":"10.1016\/j.measurement.2018"},{"key":"1398_CR42","unstructured":"Naghadehi MZ, Samaei M, Ranjbarnia M (2019) Superior modeling of hard rock TBM performance using novel predictive analytics methodologies. Proceedings of the 3rd International conference on applied researches in structural engineering and construction management, pp 1\u201311"},{"key":"1398_CR43","first-page":"19","volume":"2","author":"Y Necmi","year":"2020","unstructured":"Necmi Y, Ekrem K (2020) The Mechanical Performance of Clayey Soils Reinforced with Waste PET Fibers. Int J Earth Sci Knowl Appl 2:19\u201326","journal-title":"Int J Earth Sci Knowl Appl"},{"key":"1398_CR44","doi-asserted-by":"publisher","first-page":"2153419","DOI":"10.1080\/23311916.2022.2153419","volume":"10","author":"KC Onyelowe","year":"2023","unstructured":"Onyelowe KC, Mojtahedi FF, Ebid AM, Rezaei A, Osinubi KJ, Eberemu AO, Salahudeen B, Gadzama EW, Rezazadeh D, Jahangir H (2023) Selected AI optimization techniques and applications in geotechnical engineering. Cogent Engineering 10:2153419","journal-title":"Cogent Engineering"},{"key":"1398_CR45","unstructured":"Patil A, Waghere G, Inamdar N, Gavali P, Dhore R, Shah S (2016) Experimental review for utilisation of waste plastic bottles in soil improvement techniques. Int J Eng Res Appl 6:25\u201331"},{"key":"1398_CR46","doi-asserted-by":"publisher","first-page":"2907","DOI":"10.1007\/s10706-018-0512-0","volume":"36","author":"S Peddaiah","year":"2018","unstructured":"Peddaiah S, Burman A, Sreedeep S (2018) Experimental Study on Effect of Waste Plastic Bottle Strips in Soil Improvement. Geotech Geol Eng 36:2907\u20132920","journal-title":"Geotech Geol Eng"},{"key":"1398_CR47","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: Machine Learning in Python. J Mach Learn Res 12:2825\u20132830","journal-title":"J Mach Learn Res"},{"key":"1398_CR48","doi-asserted-by":"crossref","unstructured":"Probst P, Wright MN, Boulesteix AL (2019) Hyperparameters and tuning strategies for random forest. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery 9:e1301","DOI":"10.1002\/widm.1301"},{"key":"1398_CR49","doi-asserted-by":"publisher","first-page":"2103","DOI":"10.1007\/s42107-023-00629-x","volume":"24","author":"A Rabbani","year":"2023","unstructured":"Rabbani A, Samui P, Kumari S (2023a) Implementing ensemble learning models for the prediction of shear strength of soil. Asian J Civil Eng 24:2103\u20132119","journal-title":"Asian J Civil Eng"},{"key":"1398_CR50","doi-asserted-by":"publisher","first-page":"2327","DOI":"10.1007\/s40808-022-01610-4","volume":"9","author":"A Rabbani","year":"2023","unstructured":"Rabbani A, Samui P, Kumari S (2023b) A novel hybrid model of augmented grey wolf optimizer and artificial neural network for predicting shear strength of soil. Model Earth Syst Environ 9:2327\u20132347","journal-title":"Model Earth Syst Environ"},{"key":"1398_CR51","doi-asserted-by":"publisher","first-page":"3627","DOI":"10.1007\/s42107-023-00739-6","volume":"24","author":"A Rabbani","year":"2023","unstructured":"Rabbani A, Samui P, Kumari S (2023c) Optimized ANN-based approach for estimation of shear strength of soil. Asian J Civil Eng 24:3627\u20133640","journal-title":"Asian J Civil Eng"},{"key":"1398_CR52","doi-asserted-by":"crossref","unstructured":"Rabbani A, Samui P, Kumari S, Saraswat., Tiwari M, Rai A (2023d) Optimization of an artificial neural network using three novel meta-heuristic algorithms for predicting the shear strength of soil. Transp Infrastruct Geotechnol 1\u201322","DOI":"10.1007\/s40515-023-00343-w"},{"key":"1398_CR53","doi-asserted-by":"crossref","unstructured":"Rabbani A, Muslih JA, Saxena M, Patil SK, Mulay B. N, Tiwari M, Usha A, Kumari S, Samui P (2024) Utilization of tree-based ensemble models for predicting the shear strength of soil. Transp Infrastruct Geotechnol 1\u201324","DOI":"10.1007\/s40515-024-00379-6"},{"key":"1398_CR54","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1016\/j.bspc.2017.12.004","volume":"41","author":"KNVPS Rajesh","year":"2018","unstructured":"Rajesh KNVPS, Dhuli R (2018) Classification of imbalanced ECG beats using re-sampling techniques and AdaBoost ensemble classifier. Biomed Signal Process Control 41:242\u2013254","journal-title":"Biomed Signal Process Control"},{"key":"1398_CR55","doi-asserted-by":"crossref","unstructured":"R\u00e4tsch G, Onoda T, M\u00fcller KR (2001) Soft margins for AdaBoost. Mach Learn 42:287\u2013320","DOI":"10.1023\/A:1007618119488"},{"key":"1398_CR56","first-page":"33","volume":"48","author":"M Samaei","year":"2018","unstructured":"Samaei M, Ranjbarnia M, Zare NM (2018) Prediction of the Rock Brittleness Index Using Nonlinear Multivariable Regression and the CART Regression Tree. J Civil Environ Eng 48:33\u201340","journal-title":"J Civil Environ Eng"},{"key":"1398_CR57","doi-asserted-by":"publisher","first-page":"9187","DOI":"10.3390\/app12189187","volume":"12","author":"M Samaei","year":"2022","unstructured":"Samaei M, Massalow T, Abdolhosseinzadeh A, Yagiz S, Sabri MMS (2022) Application of Soft Computing Techniques for Predicting Thermal Conductivity of Rocks. Applied Sciences-Basel 12:9187","journal-title":"Applied Sciences-Basel"},{"key":"1398_CR58","unstructured":"Dos Santos Aguiar MJ, Paulo M (2019) Learning to classify a subject-line quality for email marketing using data mining techniques, Master Dissertation, University of Porto, p 113"},{"key":"1398_CR59","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/BF00116037","volume":"5","author":"RE Schapire","year":"1990","unstructured":"Schapire RE (1990) The strength of weak learnability. Mach Learn 5:197\u2013227","journal-title":"Mach Learn"},{"key":"1398_CR60","doi-asserted-by":"crossref","unstructured":"Shariatmadari N, Karimpour-Fard M, Hasanzadehshooiili H, Hoseinzadeh S, Karimzadeh Z (2020) Effects of drainage condition on the stress-strain behavior and pore pressure buildup of sand-PET mixtures. Construc Build Mater 233:117295","DOI":"10.1016\/j.conbuildmat.2019.117295"},{"key":"1398_CR61","doi-asserted-by":"crossref","unstructured":"Sinha AK, Jha JN, Choudhary AK (2019) A study on cbr behaviour of waste pet strip reinforced stone dust. In Proceedings of the 1st international conference on sustainable waste management through design: ICSWMD 2018. Lecture Notes in Civil Engineering, vol 21. Springer, Cham, pp 302\u2013312","DOI":"10.1007\/978-3-030-02707-0_36"},{"key":"1398_CR62","unstructured":"Tofigh Tabrizi M, Keramati M, Ramesh A (2021) Investigation of dynamic behavior of Anzali Port sandy soil reinforced with PET fibers. J Mar Eng 37\u201348"},{"key":"1398_CR63","unstructured":"Vinayak RK, Gilad-Bachrach R (2015) Dart: Dropouts meet multiple additive regression trees. In Artificial intelligence and statistics, pp 489\u2013497"},{"key":"1398_CR64","unstructured":"Witten IH, Frank E, Hall MA, Pal CJ, Data M (2005) Practical machine learning tools and techniques. Data mining. Elsevier Amsterdam, The Netherlands"},{"key":"1398_CR65","doi-asserted-by":"publisher","DOI":"10.1016\/j.soildyn.2020.106390","volume":"139","author":"J Zhou","year":"2020","unstructured":"Zhou J, Asteris PG, Armaghani DJ, Pham BT (2020) Prediction of ground vibration induced by blasting operations through the use of the Bayesian Network and random forest models. Soil Dyn Earthq Eng 139:106390","journal-title":"Soil Dyn Earthq Eng"},{"key":"1398_CR66","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1016\/j.jrmge.2021.06.012","volume":"13","author":"X Zhu","year":"2021","unstructured":"Zhu X, Chu J, Wang KD, Wu SF, Yan W, Chiam K (2021) Prediction of rockhead using a hybrid N-XGBoost machine learning framework. J Rock Mech Geotech Eng 13:1231\u20131245","journal-title":"J Rock Mech Geotech Eng"},{"key":"1398_CR67","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.enggeo.2007.10.009","volume":"96","author":"K Zorlu","year":"2008","unstructured":"Zorlu K, Gokceoglu C, Ocakoglu F, Nefeslioglu HA, Acikalin S (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96:141\u2013158","journal-title":"Eng Geol"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01398-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-024-01398-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01398-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T18:19:43Z","timestamp":1729102783000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-024-01398-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,10]]},"references-count":67,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["1398"],"URL":"https:\/\/doi.org\/10.1007\/s12145-024-01398-0","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,10]]},"assertion":[{"value":"17 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 July 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}