{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T03:14:37Z","timestamp":1789096477588,"version":"build-2803163510"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T00:00:00Z","timestamp":1731456000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T00:00:00Z","timestamp":1731456000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-024-79588-5","type":"journal-article","created":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T19:36:47Z","timestamp":1731526607000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Long short term memory networks for predicting resilient Modulus of stabilized base material subject to wet-dry cycles"],"prefix":"10.1038","volume":"14","author":[{"given":"Mohammad A.","family":"Al\u2011Zubi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mahmood","family":"Ahmad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahriar","family":"Abdullah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beenish Jehan","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wajeeha","family":"Qamar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gamil M. S.","family":"Abdullah","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Roberto Alonso","family":"Gonz\u00e1lez-Lezcano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sonjoy","family":"Paul","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N. S. Abd","family":"EL-Gawaad","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tariq","family":"Ouahbi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Kashif","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,11,13]]},"reference":[{"key":"79588_CR1","doi-asserted-by":"crossref","unstructured":"Kaloop, M. R. et al. Particle swarm optimization algorithm-extreme learning machine (PSO-ELM) model for predicting resilient modulus of stabilized aggregate bases, Applied Sciences, 9, 16, p. 3221, (2019).","DOI":"10.3390\/app9163221"},{"key":"79588_CR2","doi-asserted-by":"crossref","unstructured":"Sas, W., Gluchowski, A. & Szymanski, A. Determination of the Resilient modulus MR for the lime stabilized clay obtained from the repeated loading CBR tests, Annals of Warsaw University of Life Sciences-SGGW. Land Reclamation, vol. 44, no. 2, (2012).","DOI":"10.2478\/v10060-011-0070-0"},{"key":"79588_CR3","unstructured":"Barksdale, R. D. et al. Laboratory determination of resilient modulus for flexible pavement design, (1997)."},{"issue":"4","key":"79588_CR4","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1080\/10298430701342874","volume":"8","author":"N Khoury","year":"2007","unstructured":"Khoury, N. & Zaman, M. M. Durability of stabilized base courses subjected to wet\u2013dry cycles. Int. J. Pavement Eng. 8 (4), 265\u2013276 (2007).","journal-title":"Int. J. Pavement Eng."},{"key":"79588_CR5","unstructured":"AASHTO. AASHTO Guide for Design of Pavement Structures. (1993)."},{"issue":"1","key":"79588_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1061\/(ASCE)1532-3641(2010)10:1(1)","volume":"10","author":"M Zaman","year":"2010","unstructured":"Zaman, M., Solanki, P., Ebrahimi, A. & White, L. Neural network modeling of resilient modulus using routine subgrade soil properties. Int. J. Geomech. 10 (1), 1\u201312 (2010).","journal-title":"Int. J. Geomech."},{"key":"79588_CR7","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhang, X. & Jiang, S. J. S. A laboratory and field universal estimation method for tire\u2013pavement interaction noise (TPIN) based on 3D image technology, vol. 14, no. 19, p. 12066, (2022).","DOI":"10.3390\/su141912066"},{"issue":"1","key":"79588_CR8","doi-asserted-by":"publisher","first-page":"11","DOI":"10.3141\/2369-02","volume":"2369","author":"S Erlingsson","year":"2013","unstructured":"Erlingsson, S. & Rahman, M. S. Evaluation of permanent deformation characteristics of unbound granular materials by means of multistage repeated-load triaxial tests. Transp. Res. Rec. 2369 (1), 11\u201319 (2013).","journal-title":"Transp. Res. Rec."},{"issue":"1","key":"79588_CR9","doi-asserted-by":"publisher","first-page":"126","DOI":"10.3141\/2473-15","volume":"2473","author":"MS Hossain","year":"2015","unstructured":"Hossain, M. S. & Kim, W. S. Estimation of subgrade resilient modulus for fine-grained soil from unconfined compression test. Transp. Res. Rec. 2473 (1), 126\u2013135 (2015).","journal-title":"Transp. Res. Rec."},{"key":"79588_CR10","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.conbuildmat.2018.03.119","volume":"171","author":"W Fedrigo","year":"2018","unstructured":"Fedrigo, W., N\u00fa\u00f1ez, W. P., L\u00f3pez, M. A. C., Kleinert, T. R. & Ceratti, J. A. P. A study on the resilient modulus of cement-treated mixtures of RAP and aggregates using indirect tensile, triaxial and flexural tests. Constr. Build. Mater. 171, 161\u2013169 (2018).","journal-title":"Constr. Build. Mater."},{"key":"79588_CR11","first-page":"1285","volume":"15","author":"EE Putri","year":"2010","unstructured":"Putri, E. E., Rao, N. & Mannan, M. Evaluation of the modulus of elasticity and resilient modulus for highway subgrades. Electron. J. Geotech. Eng. 15, 1285\u20131293 (2010).","journal-title":"Electron. J. Geotech. Eng."},{"key":"79588_CR12","doi-asserted-by":"crossref","unstructured":"Zhou, F., Hu, S., Chen, D. H. & Scullion, T. Overlay tester: simple performance test for fatigue cracking, Transportation Research Record, vol. no. 1, pp. 1\u20138, 2007. (2001).","DOI":"10.3141\/2001-01"},{"issue":"1","key":"79588_CR13","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1080\/14680629.2017.1374994","volume":"20","author":"V Radhakrishnan","year":"2019","unstructured":"Radhakrishnan, V., Dudipala, R. R., Maity, A. & Sudhakar Reddy, K. Evaluation of rutting potential of asphalts using resilient modulus test parameters. Road. Mater. Pavement Des. 20 (1), 20\u201335 (2019).","journal-title":"Road. Mater. Pavement Des."},{"key":"79588_CR14","doi-asserted-by":"crossref","unstructured":"Loulizi, A., Flintsch, G. W., Al-Qadi, I. L. & Mokarem, D. Comparing resilient modulus and dynamic modulus of hot-mix asphalt as material properties for flexible pavement design, Transportation Research Record, vol. no. 1, pp. 161\u2013170, 2006. (1970).","DOI":"10.1177\/0361198106197000117"},{"key":"79588_CR15","doi-asserted-by":"crossref","unstructured":"Khan, K. et al. Prediction Models for Evaluating Resilient Modulus of Stabilized Aggregate Bases in Wet and Dry Alternating Environments: ANN and GEP Approaches, Materials, vol. 15, no. 13, p. 4386, (2022).","DOI":"10.3390\/ma15134386"},{"key":"79588_CR16","unstructured":"Olidis, C. & Hein, D. Guide for the mechanistic-empirical design of new and rehabilitated pavement structures materials characterization: Is your agency ready, in 2004 annual conference of the transportation association of Canada, (2004)."},{"key":"79588_CR17","unstructured":"Berg, K. Durability and strength of activated reclaimed Iowa Class C fly ash aggregate in road bases, 1998."},{"key":"79588_CR18","unstructured":"Nunan, T. A. & Humphrey, D. A REVIEW AND EXPERIMENTATION OF GRAVEL STABILIZATION METHODS (EXECUTIVE SUMMARY, 1990)."},{"issue":"1","key":"79588_CR19","doi-asserted-by":"publisher","first-page":"29","DOI":"10.3141\/1687-04","volume":"1687","author":"MM Zaman","year":"1999","unstructured":"Zaman, M. M., Zhu, J. H. & Laguros, J. G. Durability effects on resilient moduli of stabilized aggregate base. Transp. Res. Rec. 1687 (1), 29\u201338 (1999).","journal-title":"Transp. Res. Rec."},{"key":"79588_CR20","doi-asserted-by":"crossref","unstructured":"Guthrie, W. S., Michener, J. E., Wilson, B. T. & Eggett, D. L. Effects of environmental factors on construction of soil\u2013cement pavement layers, Transportation research record, 2104, 1, pp. 71\u201379, (2009).","DOI":"10.3141\/2104-08"},{"key":"79588_CR21","unstructured":"Khoury, N. N. Durability of Cementitiously Stabilized Aggregate Bases for Pavement Application (The University of Oklahoma, 2005)."},{"key":"79588_CR22","unstructured":"George, K. P. & Davidson, D. T. Development of a freeze-thaw test for design of soil-cement. Highway Res. Record no 36, (1963)."},{"key":"79588_CR23","doi-asserted-by":"crossref","unstructured":"Butalia, T. S., Huang, J., Kim, D. G. & Croft, F. Effect of moisture content and pore water pressure buildup on resilient modulus of cohesive soils in Ohio, ASTM Special Technical Publication, 1437, pp. 70\u201384, (2003).","DOI":"10.1520\/STP12523S"},{"key":"79588_CR24","doi-asserted-by":"crossref","unstructured":"Khoury, N. N. & Zaman, M. M. Correlation between resilient modulus, moisture variation, and soil suction for subgrade soils, Transportation research record, vol. 1874, no. 1, pp. 99\u2013107, (2004).","DOI":"10.3141\/1874-11"},{"key":"79588_CR25","doi-asserted-by":"crossref","unstructured":"Diagne, M., Tinjum, J. M. & Nokkaew, K. The effects of recycled clay brick content on the engineering properties, weathering durability, and resilient modulus of recycled concrete aggregate, Transportation Geotechnics, 3, pp. 15\u201323, (2015).","DOI":"10.1016\/j.trgeo.2014.12.003"},{"issue":"2","key":"79588_CR26","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1061\/(ASCE)MT.1943-5533.0000574","volume":"25","author":"N Khoury","year":"2013","unstructured":"Khoury, N., Brooks, R., Boeni, S. Y. & Yada, D. Variation of resilient modulus, strength, and modulus of elasticity of stabilized soils with postcompaction moisture contents. J. Mater. Civ. Eng. 25 (2), 160\u2013166 (2013).","journal-title":"J. Mater. Civ. Eng."},{"issue":"5","key":"79588_CR27","doi-asserted-by":"publisher","first-page":"506","DOI":"10.1061\/(ASCE)MT.1943-5533.0000055","volume":"22","author":"NN Khoury","year":"2010","unstructured":"Khoury, N. N. & Brooks, R. Performance of a stabilized aggregate base subject to different durability procedures. J. Mater. Civ. Eng. 22 (5), 506\u2013514 (2010).","journal-title":"J. Mater. Civ. Eng."},{"key":"79588_CR28","doi-asserted-by":"crossref","unstructured":"Ahmad, M. et al. Application of machine learning algorithms for the evaluation of seismic soil liquefaction potential, vol. 15, pp. 490\u2013505, (2021).","DOI":"10.1007\/s11709-020-0669-5"},{"key":"79588_CR29","doi-asserted-by":"crossref","unstructured":"Ahmad, M. et al. Prediction of liquefaction-induced lateral displacements using Gaussian process regression, vol. 12, no. 4, p. 2022. (1977).","DOI":"10.3390\/app12041977"},{"key":"79588_CR30","doi-asserted-by":"crossref","unstructured":"Ahmad, F. et al. Prediction of slope stability using Tree Augmented Naive-Bayes classifier: Modeling and performance evaluation, vol. 19, no. 5, pp. 4526\u20134546, (2022).","DOI":"10.3934\/mbe.2022209"},{"key":"79588_CR31","doi-asserted-by":"crossref","unstructured":"Ahmad, F., Tang, X., Hu, J., Ahmad, M. & Gordan, B. J. Improved Prediction of Slope Stability under Static and Dynamic Conditions Using Tree-Based Models, vol. 1, p. 3, (2023).","DOI":"10.32604\/cmes.2023.025993"},{"key":"79588_CR32","doi-asserted-by":"crossref","unstructured":"Ahmad, F. et al. Stability risk assessment of slopes using logistic model tree based on updated case histories, 20, 12, pp. 21229\u201321245, (2023).","DOI":"10.3934\/mbe.2023939"},{"key":"79588_CR33","doi-asserted-by":"crossref","unstructured":"Ahmad, M. et al. Unconfined compressive strength prediction of stabilized expansive clay soil using machine learning techniques, pp. 1\u201315, (2023).","DOI":"10.1007\/s41939-023-00203-7"},{"key":"79588_CR34","doi-asserted-by":"crossref","unstructured":"Barkhordari, M. S., Barkhordari, M. M., Armaghani, D. J., Mohamad, E. T. & Gordan, B. Straightforward slope stability prediction under seismic conditions using machine learning algorithms, (2023).","DOI":"10.21203\/rs.3.rs-3575223\/v1"},{"key":"79588_CR35","doi-asserted-by":"crossref","unstructured":"Asteris, P. G. et al. Slope stability classification under seismic conditions using several tree-based intelligent techniques, vol. 12, no. 3, p. 1753, (2022).","DOI":"10.3390\/app12031753"},{"key":"79588_CR36","doi-asserted-by":"crossref","unstructured":"Li, D., Liu, Z., Armaghani, D. J., Xiao, P. & Zhou, J. J. M. Novel ensemble tree solution for rockburst prediction using deep forest, vol. 10, no. 5, p. 787, (2022).","DOI":"10.3390\/math10050787"},{"key":"79588_CR37","doi-asserted-by":"crossref","unstructured":"Armaghani, D. J., Hajihassani, M., Mohamad, E. T. & Marto, A. and S. J. A. J. o. G. Noorani, blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization, 7, pp. 5383\u20135396, (2014).","DOI":"10.1007\/s12517-013-1174-0"},{"key":"79588_CR38","doi-asserted-by":"crossref","unstructured":"Armaghani, D. J. et al. Development of hybrid intelligent models for predicting TBM penetration rate in hard rock condition, vol. 63, pp. 29\u201343, (2017).","DOI":"10.1016\/j.tust.2016.12.009"},{"key":"79588_CR39","doi-asserted-by":"crossref","unstructured":"Hu, D. et al. Surface Settlement Prediction of Rectangular Pipe-Jacking Tunnel Based on the Machine-Learning Algorithm, vol. 15, no. 1, p. 04023061, (2024).","DOI":"10.1061\/JPSEA2.PSENG-1453"},{"key":"79588_CR40","doi-asserted-by":"crossref","unstructured":"Gu, X., Chen, X., Lu, P., Lan, X. & Li, X. and Y. J. T. J. o. S. Du, SiMaLSTM-SNP: novel semantic relatedness learning model preserving both Siamese networks and membrane computing, vol. 80, no. 3, pp. 3382\u20133411, (2024).","DOI":"10.1007\/s11227-023-05592-7"},{"key":"79588_CR41","doi-asserted-by":"crossref","unstructured":"Karballaeezadeh, N. et al. Intelligent road inspection with advanced machine learning; hybrid prediction models for smart mobility and transportation maintenance systems, vol. 13, no. 7, p. 1718, (2020).","DOI":"10.3390\/en13071718"},{"key":"79588_CR42","unstructured":"Jalali, H. et al. Prediction of vertical displacement for a buried pipeline subjected to normal fault using a hybrid FEM-ANN approach, pp. 1\u201316, (2024)."},{"key":"79588_CR43","doi-asserted-by":"crossref","unstructured":"Maalouf, M., Khoury, N., Laguros, J. G. & Kumin, H. Support vector regression to predict the performance of stabilized aggregate bases subject to wet\u2013dry cycles, International journal for numerical and analytical methods in geomechanics, 36, 6, pp. 675\u2013696, (2012).","DOI":"10.1002\/nag.1023"},{"key":"79588_CR44","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1016\/j.knosys.2014.08.012","volume":"71","author":"M Maalouf","year":"2014","unstructured":"Maalouf, M. & Homouz, D. Kernel ridge regression using truncated newton method. Knowl. Based Syst. 71, 339\u2013344 (2014).","journal-title":"Knowl. Based Syst."},{"key":"79588_CR45","first-page":"37","volume":"1","author":"A Ghanizadeh","year":"2016","unstructured":"Ghanizadeh, A. & Rahrovan, M. Application of artifitial neural network to predict the resilient modulus of stabilized base subjected to wet dry cycles. Comput. Mater. Civ. Eng. 1, 37\u201347 (2016).","journal-title":"Comput. Mater. Civ. Eng."},{"key":"79588_CR46","unstructured":"Ghanizadeh, A., Tavana, A., Amlashi & Abbasou, H. Prediction of Resilient Modulus of Stabilized Aggregate Base Subjected To Wet-Dry Cycles Using Adaptive Neuro-Fuzzy Inference System (ANFIS), Road, vol. 25, no. 90, pp. 65\u201375, (2017)."},{"issue":"1","key":"79588_CR47","first-page":"80","volume":"5","author":"AR Ghanizadeh","year":"2021","unstructured":"Ghanizadeh, A. R., Heidarabadizadeh, N. & Heravi, F. Gaussian process regression (gpr) for auto-estimation of resilient modulus of stabilized base materials. J. Soft Comput. Civil Eng. 5 (1), 80\u201394 (2021).","journal-title":"J. Soft Comput. Civil Eng."},{"key":"79588_CR48","doi-asserted-by":"crossref","unstructured":"Khoury, N., I. J., M. J. & Zaman, P. E. Influences of various cementitious agents on the performance of stabilized aggregate base subjected to wet-dry cycles, 8, 4, pp. 265\u2013276, (2007).","DOI":"10.1080\/10298430701342874"},{"issue":"5","key":"79588_CR49","doi-asserted-by":"publisher","first-page":"3478","DOI":"10.1109\/TII.2020.3008223","volume":"17","author":"L Ren","year":"2020","unstructured":"Ren, L. et al. A data-driven auto-CNN-LSTM prediction model for lithium-ion battery remaining useful life. IEEE Trans. Industr. Inf. 17 (5), 3478\u20133487 (2020).","journal-title":"IEEE Trans. Industr. Inf."},{"key":"79588_CR50","doi-asserted-by":"crossref","unstructured":"Gers, F. A., Schmidhuber, J. & Cummins, F. Learning to forget: Continual prediction with LSTM, Neural computation, vol. 12, no. 10, pp. 2451\u20132471, (2000).","DOI":"10.1162\/089976600300015015"},{"key":"79588_CR51","doi-asserted-by":"crossref","unstructured":"Chen, K., Zhou, Y. & Dai, F. A LSTM-based method for stock returns prediction: A case study of China stock market, in 2015 IEEE international conference on big data (big data), pp. 2823\u20132824: IEEE. (2015).","DOI":"10.1109\/BigData.2015.7364089"},{"key":"79588_CR52","doi-asserted-by":"publisher","first-page":"4779","DOI":"10.1007\/s00521-020-05535-w","volume":"33","author":"J Wang","year":"2021","unstructured":"Wang, J., Li, J., Wang, X., Wang, J. & Huang, M. Air quality prediction using CT-LSTM. Neural Comput. Appl. 33, 4779\u20134792 (2021).","journal-title":"Neural Comput. Appl."},{"key":"79588_CR53","doi-asserted-by":"crossref","unstructured":"Alhirmizy, S. & Qader, B. Multivariate time series forecasting with LSTM for Madrid, Spain pollution, in 2019 international conference on computing and information science and technology and their applications (ICCISTA), pp. 1\u20135: IEEE. (2019).","DOI":"10.1109\/ICCISTA.2019.8830667"},{"key":"79588_CR54","doi-asserted-by":"crossref","unstructured":"Schmidhuber, J., Gers, F. & Eck, D. Learning nonregular languages: a comparison of simple recurrent networks and LSTM, neural computation, 14, 9, pp. 2039\u20132041, (2002).","DOI":"10.1162\/089976602320263980"},{"key":"79588_CR55","doi-asserted-by":"crossref","unstructured":"Hochreiter, S. & Schmidhuber, J. Long short-term memory, neural computation, 9, 8, pp. 1735\u20131780, (1997).","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"79588_CR56","doi-asserted-by":"publisher","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","volume":"404","author":"A Sherstinsky","year":"2020","unstructured":"Sherstinsky, A. Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Phys. D: Nonlinear Phenom. 404, 132306 (2020).","journal-title":"Phys. D: Nonlinear Phenom."},{"issue":"19","key":"79588_CR57","doi-asserted-by":"publisher","first-page":"13089","DOI":"10.1007\/s00521-021-06004-8","volume":"33","author":"PG Asteris","year":"2021","unstructured":"Asteris, P. G., Koopialipoor, M., Armaghani, D. J., Kotsonis, E. A. & Louren\u00e7o, P. B. Prediction of cement-based mortars compressive strength using machine learning techniques. Neural Comput. Appl. 33 (19), 13089\u201313121 (2021).","journal-title":"Neural Comput. Appl."},{"issue":"8","key":"79588_CR58","doi-asserted-by":"publisher","first-page":"3437","DOI":"10.1007\/s00521-020-05214-w","volume":"33","author":"HB Ly","year":"2021","unstructured":"Ly, H. B. et al. Estimation of axial load-carrying capacity of concrete-filled steel tubes using surrogate models. Neural Comput. Appl. 33 (8), 3437\u20133458 (2021).","journal-title":"Neural Comput. Appl."},{"key":"79588_CR59","doi-asserted-by":"publisher","first-page":"124450","DOI":"10.1016\/j.conbuildmat.2021.124450","volume":"303","author":"PG Asteris","year":"2021","unstructured":"Asteris, P. G., Skentou, A. D., Bardhan, A., Samui, P. & Louren\u00e7o, P. B. Soft computing techniques for the prediction of concrete compressive strength using non-destructive tests. Constr. Build. Mater. 303, 124450 (2021).","journal-title":"Constr. Build. Mater."},{"key":"79588_CR60","doi-asserted-by":"publisher","first-page":"113297","DOI":"10.1016\/j.engstruct.2021.113297","volume":"248","author":"PG Asteris","year":"2021","unstructured":"Asteris, P. G., Lemonis, M. E., Le, T. T. & Tsavdaridis, K. D. Evaluation of the ultimate eccentric load of rectangular CFSTs using advanced neural network modeling. Eng. Struct. 248, 113297 (2021).","journal-title":"Eng. Struct."},{"key":"79588_CR61","doi-asserted-by":"crossref","unstructured":"Arora, H. C. et al. Axial Capacity of FRP-Reinforced Concrete Columns: Computational Intelligence-Based Prognosis for Sustainable Structures, Buildings, vol. 12, no. 12, p. 2137, (2022).","DOI":"10.3390\/buildings12122137"},{"issue":"9","key":"79588_CR62","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.1061\/(ASCE)MT.1943-5533.0000494","volume":"24","author":"AH Gandomi","year":"2012","unstructured":"Gandomi, A. H., Babanajad, S. K., Alavi, A. H. & Farnam, Y. Novel approach to strength modeling of concrete under triaxial compression. J. Mater. Civ. Eng. 24 (9), 1132\u20131143 (2012).","journal-title":"J. Mater. Civ. Eng."},{"key":"79588_CR63","doi-asserted-by":"crossref","unstructured":"Bui, X. N. et al. Prediction of slope failure in open-pit mines using a novel hybrid artificial intelligence model based on decision tree and evolution algorithm, scientific reports, 10, 1, pp. 1\u201317, (2020).","DOI":"10.1038\/s41598-020-66904-y"},{"key":"79588_CR64","doi-asserted-by":"publisher","first-page":"1267","DOI":"10.1007\/s12665-013-2531-8","volume":"71","author":"A Manouchehrian","year":"2014","unstructured":"Manouchehrian, A., Gholamnejad, J. & Sharifzadeh, M. Development of a model for analysis of slope stability for circular mode failure using genetic algorithm. Environ. Earth Sci. 71, 1267\u20131277 (2014).","journal-title":"Environ. Earth Sci."},{"key":"79588_CR65","doi-asserted-by":"crossref","unstructured":"Suman, S., Khan, S., Das, S. & Chand, S. Slope stability analysis using artificial intelligence techniques, Natural Hazards, vol. 84, pp. 727\u2013748, (2016).","DOI":"10.1007\/s11069-016-2454-2"},{"issue":"1","key":"79588_CR66","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/0169-2070(92)90008-W","volume":"8","author":"J Armstrong","year":"1992","unstructured":"Armstrong, J. & Collopy, F. The selection of Error measures for Generaliz-ing about forecasting methods: empirical comparisons. Int. J. Forecast. 8 (1), 69\u201380 (1992).","journal-title":"Int. J. Forecast."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-79588-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-79588-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-79588-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,13]],"date-time":"2024-11-13T20:12:30Z","timestamp":1731528750000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-024-79588-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,13]]},"references-count":66,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["79588"],"URL":"https:\/\/doi.org\/10.1038\/s41598-024-79588-5","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-4879119\/v1","asserted-by":"object"}]},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,13]]},"assertion":[{"value":"8 August 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 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"}}],"article-number":"27928"}}