Artificial Neural Network (ANN) model for predicting blast-induced tunnel response in Steel Fiber Reinforced Concrete (SFRC) structures

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dc.citation.volume23
dc.contributor.authorAli M
dc.contributor.authorChen L
dc.contributor.authorFeng B
dc.contributor.authorRusho MA
dc.contributor.authorJelodar MB
dc.contributor.authorTasán Cruz DM
dc.contributor.authorSamandari N
dc.date.accessioned2025-10-22T00:33:15Z
dc.date.available2025-10-22T00:33:15Z
dc.date.issued2025-12-01
dc.description.abstractThis study presents an Artificial Neural Network (ANN)-based predictive framework for evaluating the blast-induced response of Steel Fiber Reinforced Concrete (SFRC) tunnel structures. As underground infrastructure is increasingly exposed to dynamic and extreme loading conditions, particularly from accidental or intentional explosions, accurate and efficient prediction tools are essential. In this research, a comprehensive dataset comprising 299 data points was developed, including approximately 120 experimental results from published blast and structural tests, and 179 high-fidelity numerical simulations. This combined dataset ensured both physical reliability and broad coverage of loading scenarios. The model incorporates nine critical input parameters: Peak Overpressure (MPa), Impulse (kPa·ms), Tunnel Diameter (m), Wall Thickness (m), Compressive Strength (MPa), Tensile Strength (MPa), Fiber Volume Fraction (%), Soil Stiffness (MPa/m), and Standoff Distance (m). The target output variable is the tunnel's Maximum Displacement (mm) under blast loading. A three-hidden-layer ANN architecture was optimized through rigorous hyperparameter tuning. The best-performing model, with 16 neurons in each hidden layer, achieved high predictive accuracy, with R² values of 0.983 (training), 0.956 (validation), and 0.948 (testing). Error metrics including RMSE (2.12–3.14 mm), MAE (1.92–3.52 mm), and MAPE (1.95 %–3.12 %) further confirmed the model’s robustness. Validation against experimental data from literature demonstrated excellent agreement, verifying the model's practical applicability. Additionally, sensitivity analysis identified Peak Overpressure and Standoff Distance as the most influential factors affecting displacement. The proposed ANN framework offers a computationally efficient and accurate tool for assessing SFRC tunnel performance under blast loading, supporting the design of safer and more resilient underground structures.
dc.description.confidentialfalse
dc.edition.editionDecember 2025
dc.identifier.citationAli M, Chen L, Feng B, Rusho MA, Jelodar MB, Tasán Cruz DM, Samandari N. (2025). Artificial Neural Network (ANN) model for predicting blast-induced tunnel response in Steel Fiber Reinforced Concrete (SFRC) structures. Case Studies in Construction Materials. 23.
dc.identifier.doi10.1016/j.cscm.2025.e05332
dc.identifier.eissn2214-5095
dc.identifier.elements-typejournal-article
dc.identifier.issn2214-5095
dc.identifier.numbere05332
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/73709
dc.languageEnglish
dc.publisherElsevier Ltd
dc.publisher.urihttps://www.sciencedirect.com/science/article/pii/S2214509525011301
dc.relation.isPartOfCase Studies in Construction Materials
dc.rights(c) The author/sen
dc.rights.licenseCC BY-NC-NDen
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectArtificial Neural Network (ANN)
dc.subjectSteel Fiber Reinforced Concrete (SFRC)
dc.subjectBlast Loading
dc.subjectTunnel Response Prediction
dc.subjectData-Driven Structural Analysis
dc.titleArtificial Neural Network (ANN) model for predicting blast-induced tunnel response in Steel Fiber Reinforced Concrete (SFRC) structures
dc.typeJournal article
pubs.elements-id503792
pubs.organisational-groupOther
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