{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400135,"version":"3.55.0"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T00:00:00Z","timestamp":1779408000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T00:00:00Z","timestamp":1785110400000},"content-version":"vor","delay-in-days":66,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100009392","name":"Prince Sattam bin Abdulaziz University","doi-asserted-by":"publisher","award":["(PSAU\/2025\/01\/34831)"],"award-info":[{"award-number":["(PSAU\/2025\/01\/34831)"]}],"id":[{"id":"10.13039\/100009392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-026-54289-3","type":"journal-article","created":{"date-parts":[[2026,5,22]],"date-time":"2026-05-22T04:16:37Z","timestamp":1779423397000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable machine learning-based investigation for influential parameters towards interfacial capacity of spliced glass fiber reinforced polymer bars"],"prefix":"10.1038","volume":"16","author":[{"given":"Ali","family":"Alatify","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdulaziz","family":"Alqurashi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muath","family":"Alsuhaibani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,22]]},"reference":[{"key":"54289_CR1","first-page":"857","volume":"103","author":"R Aly","year":"2006","unstructured":"Aly, R., Benmokrane, B. & Ebead, U. Tensile Lap Splicing of Fiber-Reinforced Polymer. ACI Struct. J. 103, 857\u2013864 (2006).","journal-title":"ACI Struct. J."},{"key":"54289_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/S0950-0618(99)00037-9","author":"B Tighiouart","year":"1999","unstructured":"Tighiouart, B., Benmokrane, B. & Mukhopadhyaya, P. Bond strength of glass FRP rebar splices in beams under static loading. Constr. Build. Mater. https:\/\/doi.org\/10.1016\/S0950-0618(99)00037-9 (1999).","journal-title":"Constr. Build. Mater."},{"issue":"6","key":"54289_CR3","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1061\/(ASCE)CC.1943-5614.0000139","volume":"14","author":"M Harajli","year":"2010","unstructured":"Harajli, M. & Abouniaj, M. Bond performance of GFRP bars in tension: Experimental evaluation and assessment of ACI 440 Guidelines. J. Compos. Constr. 14(6), 659\u2013668 (2010).","journal-title":"J. Compos. Constr."},{"key":"54289_CR4","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1016\/j.engstruct.2017.06.040","volume":"147","author":"BH Tekle","year":"2017","unstructured":"Tekle, B. H., Khennane, A. & Kayali, O. Bond of spliced GFRP reinforcement bars in alkali activated cement concrete. Eng. Struct. 147, 740\u2013751 (2017).","journal-title":"Eng. Struct."},{"key":"54289_CR5","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1061\/(ASCE)CC.1943-5614.0000359","volume":"17","author":"MR Esfahani","year":"2013","unstructured":"Esfahani, M. R., Rakhshanimehr, M. & Mousavi, S. R. Bond strength of lap-spliced GFRP bars in concrete beams. J. Compos. Constr. 17, 314\u2013323 (2013).","journal-title":"J. Compos. Constr."},{"key":"54289_CR6","first-page":"257","volume":"111","author":"AC Pay","year":"2014","unstructured":"Pay, A. C., Canbay, E. & Frosch, R. J. Bond strength of spliced fiber-reinforced polymer reinforcement. ACI Struct. J. 111, 257\u2013266 (2014).","journal-title":"ACI Struct. J."},{"key":"54289_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.engstruct.2022.114552","author":"C Wu","year":"2022","unstructured":"Wu, C., Hwang, H.-J. & Ma, G. Effect of stirrups on the bond behavior of lap spliced GFRP bars in concrete beams. Eng. Struct. https:\/\/doi.org\/10.1016\/j.engstruct.2022.114552 (2022).","journal-title":"Eng. Struct."},{"key":"54289_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-025-10479-2","author":"N Concha","year":"2025","unstructured":"Concha, N., Bayhonan, J. M., Paano, J., Sultan, A. D. & Tunay, M. Parametric analysis and machine learning model for compressive strength of Rice Husk Ash concrete. Arch. Comput. Methods. Eng. https:\/\/doi.org\/10.1007\/s11831-025-10479-2 (2025).","journal-title":"Arch. Comput. Methods. Eng."},{"key":"54289_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.111533","author":"T Shafighfard","year":"2025","unstructured":"Shafighfard, T., Asgarkhani, N., Kazemi, F. & Yoo, D.-Y. Transfer learning on stacked machine-learning model for predicting pull-out behavior of steel fibers from concrete. Eng. Appl. Artif. Intell. https:\/\/doi.org\/10.1016\/j.engappai.2025.111533 (2025).","journal-title":"Eng. Appl. Artif. Intell."},{"key":"54289_CR10","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-12359-y","author":"A\u00d6 \u00c7ift\u00e7io\u011flu","year":"2025","unstructured":"\u00c7ift\u00e7io\u011flu, A. \u00d6., Delikanl\u0131, A., Shafighfard, T. & Bagherzadeh, F. Machine learning based shear strength prediction in reinforced concrete beams using Levy flight enhanced decision trees. Sci. Rep. https:\/\/doi.org\/10.1038\/s41598-025-12359-y (2025).","journal-title":"Sci. Rep."},{"key":"54289_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.compstruc.2025.107657","author":"F Kazemi","year":"2025","unstructured":"Kazemi, F., \u00c7ift\u00e7io\u011flu, A. \u04e6, Shafighfard, T., Asgarkhani, N. & Jankowski, R. RAGN-R: A multi-subject ensemble machine-learning method for estimating mechanical properties of advanced structural materials. Comput. Struct. https:\/\/doi.org\/10.1016\/j.compstruc.2025.107657 (2025).","journal-title":"Comput. Struct."},{"key":"54289_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2022.128360","author":"AIB Farouk","year":"2022","unstructured":"Farouk, A. I. B., Zhu, J., Ding, J. & Haruna, S. Prediction and uncertainty quantification of ultimate bond strength between UHPC and reinforcing steel bar using a hybrid machine learning approach. Constr. Build. Mater. https:\/\/doi.org\/10.1016\/j.conbuildmat.2022.128360 (2022).","journal-title":"Constr. Build. Mater."},{"key":"54289_CR13","doi-asserted-by":"publisher","first-page":"1581","DOI":"10.1617\/s11527-014-0256-0","volume":"48","author":"EM Golafshani","year":"2015","unstructured":"Golafshani, E. M., Rahai, A. & Sebt, M. H. Artificial neural network and genetic programming for predicting the bond strength of GFRP bars in concrete. Mater. Struct. 48, 1581\u20131602 (2015).","journal-title":"Mater. Struct."},{"key":"54289_CR14","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.conbuildmat.2012.04.046","volume":"36","author":"EM Golafshani","year":"2012","unstructured":"Golafshani, E. M., Rahai, A., Sebt, M. H. & Akbarpour, H. Prediction of bond strength of spliced steel bars in concrete using artificial neural network and fuzzy logic. Constr. Build. Mater. 36, 411\u2013418 (2012).","journal-title":"Constr. Build. Mater."},{"key":"54289_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.engstruct.2023.117382","author":"P-F Zhang","year":"2024","unstructured":"Zhang, P.-F., Iqbal, M., Zhang, D., Zhao, X.-L. & Zhao, Q. Bond strength prediction of FRP bars to seawater sea sand concrete based on ensemble learning models. Eng. Struct. https:\/\/doi.org\/10.1016\/j.engstruct.2023.117382 (2024).","journal-title":"Eng. Struct."},{"key":"54289_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.dibe.2023.100311","author":"A Kumar","year":"2024","unstructured":"Kumar, A., Arora, H. C., Kumar, P., Kapoor, N. R. & Nehdi, M. L. Machine learning based graphical interface for accurate estimation of FRP-concrete bond strength under diverse exposure conditions. Dev. Built Environ. https:\/\/doi.org\/10.1016\/j.dibe.2023.100311 (2024).","journal-title":"Dev. Built Environ."},{"key":"54289_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.engstruct.2022.114311","author":"Z Li","year":"2022","unstructured":"Li, Z., Qi, J., Hu, Y. & Wang, J. Estimation of bond strength between UHPC and reinforcing bars using machine learning approaches. Eng. Struct. https:\/\/doi.org\/10.1016\/j.engstruct.2022.114311 (2022).","journal-title":"Eng. Struct."},{"key":"54289_CR18","doi-asserted-by":"publisher","DOI":"10.1617\/s11527-018-1137-8","author":"M Rakhshanimehr","year":"2018","unstructured":"Rakhshanimehr, M., Mousavi, S. R., Esfahani, M. R. & Shahri, S. F. Establishment and experimental validation of an updated predictive equation for the development and lap-spliced length of GFRP bars in concrete. Mater. Struct. https:\/\/doi.org\/10.1617\/s11527-018-1137-8 (2018).","journal-title":"Mater. Struct."},{"key":"54289_CR19","doi-asserted-by":"publisher","first-page":"869","DOI":"10.1016\/j.conbuildmat.2018.09.045","volume":"189","author":"N Zemour","year":"2018","unstructured":"Zemour, N., Asadian, A., Ahmed, E. A., Khayat, K. H. & Benmokrane, B. Experimental study on the bond behavior of GFRP bars in normal and self-consolidating concrete. Constr. Build. Mater. 189, 869\u2013881 (2018).","journal-title":"Constr. Build. Mater."},{"issue":"9","key":"54289_CR20","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1139\/l07-046","volume":"34","author":"R Aly","year":"2007","unstructured":"Aly, R. Stress along tensile lap-spliced fibre reinforced polymer reinforcing bars in concrete. Can. J. Civ. Eng. 34(9), 1149\u20131158 (2007).","journal-title":"Can. J. Civ. Eng."},{"key":"54289_CR21","doi-asserted-by":"crossref","unstructured":"Y. Al-Salloum, L. Alaoud, H. Elsanadedy, A. Albidah, T. Almusallam and H. Abbas, Bond Performance of GFRP Bar-Splicing in Reinforced Concrete Beams.\u00a0J. Compos. Constr. 26(2), (2022).","DOI":"10.1061\/(ASCE)CC.1943-5614.0001190"},{"key":"54289_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2022.129943","author":"H Abbas","year":"2023","unstructured":"Abbas, H., Elsanadedy, H., Alaoud, L., Almusallam, T. & Al-Salloum, Y. Effect of confining stirrups and bar gap in improving bond behavior of glass fiber reinforced polymer (GFRP) bar lap splices in RC beams. Constr. Build. Mater. https:\/\/doi.org\/10.1016\/j.conbuildmat.2022.129943 (2023).","journal-title":"Constr. Build. Mater."},{"key":"54289_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.jenvman.2020.111915","author":"MP Fabani","year":"2021","unstructured":"Fabani, M. P. et al. Producing non-traditional flour from watermelon rind pomace: Artificial neural network (ANN) modeling of the drying process. J. Environ. Manage. https:\/\/doi.org\/10.1016\/j.jenvman.2020.111915 (2021).","journal-title":"J. Environ. Manage."},{"issue":"5","key":"54289_CR24","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1016\/j.jtte.2016.09.007","volume":"3","author":"M Mazari","year":"2016","unstructured":"Mazari, M. & Rodriguez, D. D. Prediction of pavement roughness using a hybrid gene expression programming-neural network technique. Journal of Traffic and Transportation Engineering (English Edition) 3(5), 448\u2013455 (2016).","journal-title":"Journal of Traffic and Transportation Engineering (English Edition)"},{"key":"54289_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2021.e06136","author":"S Sada","year":"2021","unstructured":"Sada, S. & Ikpeseni, S. Evaluation of ANN and ANFIS modeling ability in the prediction of AISI 1050 steel machining performance. Heliyon https:\/\/doi.org\/10.1016\/j.heliyon.2021.e06136 (2021).","journal-title":"Heliyon"},{"key":"54289_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.engstruct.2020.111221","author":"R Solhmirzaei","year":"2020","unstructured":"Solhmirzaei, R., Salehi, H., Kodur, V. & Naser, M. Machine learning framework for predicting failure mode and shear capacity of ultra high performance concrete beams. Eng. Struct. https:\/\/doi.org\/10.1016\/j.engstruct.2020.111221 (2020).","journal-title":"Eng. Struct."},{"key":"54289_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.engfracmech.2020.107488","author":"X Xi","year":"2021","unstructured":"Xi, X., Yin, Z., Yang, S. & Li, C.-Q. Using artificial neural network to predict the fracture properties of the interfacial transition zone of concrete at the meso-scale. Eng. Fract. Mech. https:\/\/doi.org\/10.1016\/j.engfracmech.2020.107488 (2021).","journal-title":"Eng. Fract. Mech."},{"key":"54289_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.engstruct.2020.111743","author":"J Rahman","year":"2021","unstructured":"Rahman, J., Ahmed, K. S. & Imtiaz, N. Data-driven shear strength prediction of steel fiber reinforced concrete beams using machine learning approach. Eng. Struct. https:\/\/doi.org\/10.1016\/j.engstruct.2020.111743 (2021).","journal-title":"Eng. Struct."},{"key":"54289_CR29","doi-asserted-by":"crossref","unstructured":"T. Chen and C. Guestrin, XGBoost: A Scalable Tree Boosting System. In: International Conference on Knowledge Discovery and Data Mining.\u00a0(2016).","DOI":"10.1145\/2939672.2939785"},{"key":"54289_CR30","doi-asserted-by":"crossref","unstructured":"A. Holzinger, A. Saranti,. C. Molnar, P. Biecek and W. Samek, Explainable AI Methods - A Brief Overview. In: xxAI - Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020. (Revised and Extended Papers, 2022).","DOI":"10.1007\/978-3-031-04083-2"},{"key":"54289_CR31","unstructured":"S. M. Lundberg and S.-I. Lee, A Unified Approach to Interpreting Model Predictions. In: 31st Conferenceon NeuralInformation Processing Systems (NIPS2017). (2017)."},{"key":"54289_CR32","doi-asserted-by":"publisher","DOI":"10.1016\/j.acags.2024.100178","author":"JAS Antonini","year":"2024","unstructured":"Antonini, J. A. S. Machine learning model interpretability using SHAP values: Application to Igneous Rock Classification task. Applied Computing and Geosciences https:\/\/doi.org\/10.1016\/j.acags.2024.100178 (2024).","journal-title":"Applied Computing and Geosciences"},{"key":"54289_CR33","unstructured":"S. M. Lundberg, G. G. Erion and S.-I. Lee. Consistent Individualized Feature Attribution for Tree Ensembles. 3 (Cornell University, 2019)."},{"issue":"1","key":"54289_CR34","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","volume":"2","author":"SM Lundberg","year":"2020","unstructured":"Lundberg, S. M. et al. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2(1), 56\u201367 (2020).","journal-title":"Nat. Mach. Intell."},{"key":"54289_CR35","volume-title":"Guide for the design and construction of structural concrete reinforced with fiber-reinforced polymer (FRP) bars (ACI 440.1R-15)","author":"ACI","year":"2015","unstructured":"ACI. Guide for the design and construction of structural concrete reinforced with fiber-reinforced polymer (FRP) bars (ACI 440.1R-15) (American Concrete Institute, 2015)."},{"key":"54289_CR36","doi-asserted-by":"publisher","first-page":"587","DOI":"10.1016\/j.engstruct.2016.10.029","volume":"131","author":"M Mousavi","year":"2017","unstructured":"Mousavi, M. Bond strength and development length of steel bar in unconfined self-consolidating concrete. Eng. Struct. 131, 587\u2013598 (2017).","journal-title":"Eng. Struct."},{"key":"54289_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.conbuildmat.2022.129295","author":"PR Pan","year":"2022","unstructured":"Pan, P. R. Experimental investigation on bond performance of helically ribbed GFRP rebar embedded in Ultra-high performance concrete. Constr. Build. Mater. https:\/\/doi.org\/10.1016\/j.conbuildmat.2022.129295 (2022).","journal-title":"Constr. Build. Mater."},{"key":"54289_CR38","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1016\/j.istruc.2022.04.088","volume":"41","author":"NC Concha","year":"2022","unstructured":"Concha, N. C. Neural network model for bond strength of FRP bars in concrete. Structures 41, 306\u2013317 (2022).","journal-title":"Structures"},{"key":"54289_CR39","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-08720-2","volume-title":"Modern numerical nonlinear optimization","author":"N Andrei","year":"2022","unstructured":"Andrei, N. Modern numerical nonlinear optimization (Springer Nature, 2022)."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-54289-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-54289-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-54289-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T23:47:11Z","timestamp":1785109631000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-54289-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,22]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["54289"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-54289-3","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,22]]},"assertion":[{"value":"27 March 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"23302"}}