{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T12:11:15Z","timestamp":1780056675097,"version":"3.54.0"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T00:00:00Z","timestamp":1776816000000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s00521-026-11995-3","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T04:29:39Z","timestamp":1776832179000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A novel porosity-informed hybrid neural network framework for estimating the compressive strength of concrete"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5607-9334","authenticated-orcid":false,"given":"Ahed","family":"Habib","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salah","family":"Altoubat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0667-3148","authenticated-orcid":false,"given":"M. Talha","family":"Junaid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1651-616X","authenticated-orcid":false,"given":"Moussa","family":"Leblouba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4334-6920","authenticated-orcid":false,"given":"Samer","family":"Barakat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0657-0090","authenticated-orcid":false,"given":"Mohamad","family":"Maalej","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,22]]},"reference":[{"issue":"6","key":"11995_CR1","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1080\/19648189.2020.1762749","volume":"26","author":"J Abell\u00e1n Garc\u00eda","year":"2022","unstructured":"Abell\u00e1n Garc\u00eda J, Fern\u00e1ndez G\u00f3mez J, Torres Castellanos N (2022) Properties prediction of environmentally friendly ultra-high-performance concrete using artificial neural networks. Eur J Environ Civil Eng 26(6):2319\u20132343","journal-title":"Eur J Environ Civil Eng"},{"issue":"4","key":"11995_CR2","first-page":"3","volume":"118","author":"J Abell\u00e1n-Garc\u00eda","year":"2021","unstructured":"Abell\u00e1n-Garc\u00eda J (2021) Artificial neural network model for strength prediction of ultra-high-performance concre. ACI Mater J 118(4):3\u201314","journal-title":"ACI Mater J"},{"issue":"4","key":"11995_CR3","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1108\/02644401111131902","volume":"28","author":"A Ahangar-Asr","year":"2011","unstructured":"Ahangar-Asr A, Faramarzi A, Javadi AA, Giustolisi O (2011) Modelling mechanical behaviour of rubber concrete using evolutionary polynomial regression. Eng Comput 28(4):492\u2013507","journal-title":"Eng Comput"},{"issue":"8","key":"11995_CR4","doi-asserted-by":"publisher","DOI":"10.3390\/buildings11080324","volume":"11","author":"A Ahmad","year":"2021","unstructured":"Ahmad A, Chaiyasarn K, Farooq F, Ahmad W, Suparp S, Aslam F (2021) Compressive strength prediction via gene expression programming (GEP) and artificial neural network (ANN) for concrete containing RCA. Buildings 11(8):324","journal-title":"Buildings"},{"issue":"5","key":"11995_CR5","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1680\/adcr.12.00052","volume":"25","author":"S Ahmad","year":"2013","unstructured":"Ahmad S, Azad AK (2013) An exploratory study on correlating the permeability of concrete with its porosity and tortuosity. Adv Cem Res 25(5):288\u2013294","journal-title":"Adv Cem Res"},{"issue":"3","key":"11995_CR6","first-page":"137","volume":"9","author":"A AL Houri","year":"2025","unstructured":"AL Houri A, Habib A, Al-Sadoon ZA (2025) Artificial intelligence-based design and analysis of passive control structures: an overview. J Soft Comput Civil Eng 9(3):137\u2013168","journal-title":"J Soft Comput Civil Eng"},{"issue":"15","key":"11995_CR7","doi-asserted-by":"publisher","first-page":"11807","DOI":"10.1007\/s00521-019-04663-2","volume":"32","author":"PG Asteris","year":"2020","unstructured":"Asteris PG, Mokos VG (2020) Concrete compressive strength using artificial neural networks. Neural Comput Appl 32(15):11807\u201311826","journal-title":"Neural Comput Appl"},{"key":"11995_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.rineng.2022.100794","volume":"17","author":"C Cao","year":"2023","unstructured":"Cao C (2023) Prediction of concrete porosity using machine learning. Results Eng 17:100794","journal-title":"Results Eng"},{"key":"11995_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2023.134465","volume":"411","author":"Z Chao","year":"2024","unstructured":"Chao Z, Wang H, Hu S, Wang M, Xu S, Zhang W (2024) Permeability and porosity of light-weight concrete with plastic waste aggregate: experimental study and machine learning modelling. Constr Build Mater 411:134465","journal-title":"Constr Build Mater"},{"key":"11995_CR10","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1016\/j.conbuildmat.2012.11.072","volume":"40","author":"X Chen","year":"2013","unstructured":"Chen X, Wu S, Zhou J (2013) Influence of porosity on compressive and tensile strength of cement mortar. Constr Build Mater 40:869\u2013874","journal-title":"Constr Build Mater"},{"key":"11995_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.jai.2024.05.001","author":"Z Chen","year":"2024","unstructured":"Chen Z, Zhao W, Deng L, Ding Y, Wen Q, Li G, Xie Y (2024) Large-scale self-normalizing neural networks. J Autom Intell. https:\/\/doi.org\/10.1016\/j.jai.2024.05.001","journal-title":"J Autom Intell"},{"key":"11995_CR12","doi-asserted-by":"crossref","unstructured":"Cheng AS, Yen T, Liu YW, Sheen YN (2008) Relation between porosity and compressive strength of slag concrete. In: Structures Congress 2008: Crossing Borders. pp. 1\u20138","DOI":"10.1061\/41016(314)310"},{"issue":"5","key":"11995_CR13","doi-asserted-by":"publisher","first-page":"711","DOI":"10.3846\/13923730.2014.897989","volume":"22","author":"MY Cheng","year":"2016","unstructured":"Cheng MY, Cao MT (2016) Estimating strength of rubberized concrete using evolutionary multivariate adaptive regression splines. J Civ Eng Manag 22(5):711\u2013720","journal-title":"J Civ Eng Manag"},{"key":"11995_CR14","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1617\/s11527-009-9468-0","volume":"43","author":"M Geso\u011flu","year":"2010","unstructured":"Geso\u011flu M, G\u00fcneyisi E, \u00d6zturan T, \u00d6zbay E (2010) Modeling the mechanical properties of rubberized concretes by neural network and genetic programming. Mater Struct 43:31\u201345","journal-title":"Mater Struct"},{"issue":"8","key":"11995_CR15","doi-asserted-by":"publisher","first-page":"3129","DOI":"10.1108\/EC-09-2021-0527","volume":"39","author":"A Habib","year":"2022","unstructured":"Habib A, Yildirim U (2022) Estimating mechanical and dynamic properties of rubberized concrete using machine learning techniques: a comprehensive study. Eng Comput 39(8):3129\u20133178","journal-title":"Eng Comput"},{"issue":"347","key":"11995_CR16","doi-asserted-by":"publisher","first-page":"e289","DOI":"10.3989\/mc.2022.13621","volume":"72","author":"A Habib","year":"2022","unstructured":"Habib A, Yildirim U (2022) Simplified modeling of rubberized concrete properties using multivariable regression analysis. Mater Constr 72(347):e289\u2013e289","journal-title":"Mater Constr"},{"issue":"06","key":"11995_CR17","doi-asserted-by":"publisher","DOI":"10.1142\/S0219455423500608","volume":"23","author":"A Habib","year":"2023","unstructured":"Habib A, Yildirim U (2023) Influence of isolator properties and earthquake characteristics on the seismic behavior of RC structure equipped with quintuple friction pendulum bearings. Int J Struct Stab Dyn 23(06):2350060","journal-title":"Int J Struct Stab Dyn"},{"key":"11995_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.soildyn.2024.108732","volume":"182","author":"A Habib","year":"2024","unstructured":"Habib A, Yildirim U (2024) Proposing unsupervised clustering-based earthquake records selection framework for computationally efficient nonlinear response history analysis of structures equipped with multi-stage friction pendulum bearings. Soil Dyn Earthquake Eng 182:108732","journal-title":"Soil Dyn Earthquake Eng"},{"key":"11995_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-024-09497-2","author":"A Habib","year":"2024","unstructured":"Habib A, Barakat S, Al-Toubat S, Junaid MT, Maalej M (2024) Developing machine learning models for identifying the failure potential of fire-exposed FRP-strengthened concrete beams. Arab J Sci Eng. https:\/\/doi.org\/10.1007\/s13369-024-09497-2","journal-title":"Arab J Sci Eng"},{"issue":"4","key":"11995_CR20","doi-asserted-by":"publisher","first-page":"5383","DOI":"10.1007\/s13369-022-07435-8","volume":"48","author":"A Habib","year":"2023","unstructured":"Habib A, Yildirim U, Habib M (2023) Applying kernel principal component analysis for enhanced multivariable regression modeling of rubberized concrete properties. Arab J Sci Eng 48(4):5383\u20135396","journal-title":"Arab J Sci Eng"},{"key":"11995_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/s13369-024-08776-2","author":"M Habib","year":"2024","unstructured":"Habib M, Okayli M (2024) Evaluating the sensitivity of machine learning models to data preprocessing technique in concrete compressive strength estimation. Arab J Sci Eng. https:\/\/doi.org\/10.1007\/s13369-024-08776-2","journal-title":"Arab J Sci Eng"},{"issue":"1","key":"11995_CR22","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1007\/s43621-024-00500-2","volume":"5","author":"M Habib","year":"2024","unstructured":"Habib M, Habib A, Albzaie M, Farghal A (2024) Sustainability benefits of AI-based engineering solutions for infrastructure resilience in arid regions against extreme rainfall events. Discover Sust 5(1):278","journal-title":"Discover Sust"},{"key":"11995_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.cose.2022.102631","volume":"116","author":"O Ibitoye","year":"2022","unstructured":"Ibitoye O, Shafiq MO, Matrawy A (2022) Differentially private self-normalizing neural networks for adversarial robustness in federated learning. Computers Secur 116:102631","journal-title":"Computers Secur"},{"issue":"2","key":"11995_CR24","first-page":"532","volume":"22","author":"MV Kamath","year":"2024","unstructured":"Kamath MV, Prashanth S, Kumar M, Tantri A (2024) Machine-learning-algorithm to predict the high-performance concrete compressive strength using multiple data. J Eng Des Technol 22(2):532\u2013560","journal-title":"J Eng Des Technol"},{"issue":"3","key":"11995_CR25","doi-asserted-by":"publisher","DOI":"10.3390\/ma14030647","volume":"14","author":"M Karimaei","year":"2021","unstructured":"Karimaei M, Dabbaghi F, Dehestani M, Rashidi M (2021) Estimating compressive strength of concrete containing untreated coal waste aggregates using ultrasonic pulse velocity. Materials 14(3):647","journal-title":"Materials"},{"issue":"2","key":"11995_CR26","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/S0008-8846(01)00665-2","volume":"32","author":"EP Kearsley","year":"2002","unstructured":"Kearsley EP, Wainwright PJ (2002) The effect of porosity on the strength of foamed concrete. Cem Concr Res 32(2):233\u2013239","journal-title":"Cem Concr Res"},{"key":"11995_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2020.119427","volume":"256","author":"M Khormani","year":"2020","unstructured":"Khormani M, Jaari VRK, Aghayan I, Ghaderi SH, Ahmadyfard A (2020) Compressive strength determination of concrete specimens using X-ray computed tomography and finite element method. Constr Build Mater 256:119427","journal-title":"Constr Build Mater"},{"key":"11995_CR28","unstructured":"Klambauer G, Unterthiner T, Mayr A, Hochreiter S (2017) Self-normalizing neural networks.\u00a0Adv Neural Inf Process Syst.\u00a030"},{"issue":"2","key":"11995_CR29","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1680\/jmacr.19.00194","volume":"73","author":"LG Li","year":"2021","unstructured":"Li LG, Feng JJ, Zhu J, Chu SH, Kwan AKH (2021) Pervious concrete: effects of porosity on permeability and strength. Mag Concr Res 73(2):69\u201379","journal-title":"Mag Concr Res"},{"issue":"1","key":"11995_CR30","volume":"2016","author":"Z Liu","year":"2016","unstructured":"Liu Z, Zhao K, Hu C, Tang Y (2016) Effect of water\u2010cement ratio on pore structure and strength of foam concrete. Adv Mater Sci Eng 2016(1):9520294","journal-title":"Adv Mater Sci Eng"},{"issue":"11","key":"11995_CR31","doi-asserted-by":"publisher","first-page":"4294","DOI":"10.1016\/j.conbuildmat.2011.05.005","volume":"25","author":"C Lian","year":"2011","unstructured":"Lian C, Zhuge Y, Beecham S (2011) The relationship between porosity and strength for porous concrete. Constr Build Mater 25(11):4294\u20134298","journal-title":"Constr Build Mater"},{"issue":"6","key":"11995_CR32","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1016\/0008-8846(71)90018-4","volume":"1","author":"DG Manning","year":"1971","unstructured":"Manning DG, Hope BB (1971) The effect of porosity on the compressive strength and elastic modulus of polymer impregnated concrete. Cem Concr Res 1(6):631\u2013644","journal-title":"Cem Concr Res"},{"issue":"8","key":"11995_CR33","doi-asserted-by":"publisher","DOI":"10.3390\/polym14081583","volume":"14","author":"A Nafees","year":"2022","unstructured":"Nafees A, Khan S, Javed MF, Alrowais R, Mohamed AM, Mohamed A, Vatin NI (2022) Forecasting the mechanical properties of plastic concrete employing experimental data using machine learning algorithms: DT, MLPNN, SVM, and RF. Polymers 14(8):1583","journal-title":"Polymers"},{"key":"11995_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.dibe.2024.100374","volume":"17","author":"V Revilla-Cuesta","year":"2024","unstructured":"Revilla-Cuesta V, Faleschini F, Pellegrino C, Skaf M, Ortega-L\u00f3pez V (2024) Water transport and porosity trends of concrete containing integral additions of raw-crushed wind-turbine blade. Dev Built Environ 17:100374","journal-title":"Dev Built Environ"},{"key":"11995_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.jobe.2021.103425","volume":"44","author":"V Revilla-Cuesta","year":"2021","unstructured":"Revilla-Cuesta V, Faleschini F, Zanini MA, Skaf M, Ortega-L\u00f3pez V (2021) Porosity-based models for estimating the mechanical properties of self-compacting concrete with coarse and fine recycled concrete aggregate. J Build Eng 44:103425","journal-title":"J Build Eng"},{"issue":"6","key":"11995_CR36","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1179\/174367607X228089","volume":"106","author":"N Shafiq","year":"2007","unstructured":"Shafiq N, Nuruddin MF, Kamaruddin I (2007) Comparison of engineering and durability properties of fly ash blended cement concrete made in UK and Malaysia. Adv Appl Ceram 106(6):314\u2013318","journal-title":"Adv Appl Ceram"},{"issue":"2","key":"11995_CR37","doi-asserted-by":"publisher","DOI":"10.3390\/buildings11020044","volume":"11","author":"FA Silva","year":"2021","unstructured":"Silva FA, Delgado JM, Cavalcanti RS, Azevedo AC, Guimar\u00e3es AS, Lima AG (2021) Use of nondestructive testing of ultrasound and artificial neural networks to estimate compressive strength of concrete. Buildings 11(2):44","journal-title":"Buildings"},{"issue":"1","key":"11995_CR38","doi-asserted-by":"publisher","DOI":"10.3390\/buildings12010065","volume":"12","author":"J Sun","year":"2022","unstructured":"Sun J, Wang J, Zhu Z, He R, Peng C, Zhang C, Wang X (2022) Mechanical performance prediction for sustainable high-strength concrete using bio-inspired neural network. Buildings 12(1):65","journal-title":"Buildings"},{"key":"11995_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2022.126578","volume":"323","author":"VQ Tran","year":"2022","unstructured":"Tran VQ, Dang VQ, Ho LS (2022) Evaluating compressive strength of concrete made with recycled concrete aggregates using machine learning approach. Constr Build Mater 323:126578","journal-title":"Constr Build Mater"},{"key":"11995_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2020.120126","volume":"263","author":"CC Vu","year":"2020","unstructured":"Vu CC, Pl\u00e9 O, Weiss J, Amitrano D (2020) Revisiting the concept of characteristic compressive strength of concrete. Constr Build Mater 263:120126","journal-title":"Constr Build Mater"},{"issue":"10","key":"11995_CR41","doi-asserted-by":"publisher","first-page":"993","DOI":"10.1016\/j.cemconcomp.2011.07.005","volume":"33","author":"A Younsi","year":"2011","unstructured":"Younsi A, Turcry P, Rozi\u00e8re E, A\u00eet-Mokhtar A, Loukili A (2011) Performance-based design and carbonation of concrete with high fly ash content. Cem Concr Compos 33(10):993\u20131000","journal-title":"Cem Concr Compos"},{"key":"11995_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.cemconcomp.2023.105378","volume":"146","author":"Z Zhuang","year":"2024","unstructured":"Zhuang Z, Mu S, Guo Z, Liu G, Zhang J, Miao C (2024) Diffusion-reaction models for concrete exposed to chloride-sulfate attack based on porosity and water saturation. Cem Concr Compos 146:105378","journal-title":"Cem Concr Compos"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-11995-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-026-11995-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-026-11995-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T11:51:10Z","timestamp":1780055470000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-026-11995-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,22]]},"references-count":42,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["11995"],"URL":"https:\/\/doi.org\/10.1007\/s00521-026-11995-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,22]]},"assertion":[{"value":"21 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 April 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare that they have no conflicts of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interests"}}],"article-number":"327"}}