i-manager Publications

Advanced Computational Approaches for Structural Integrity Assessment: Multi-Scale Modelling and Experimental Validation

Charmie Rodriguez*

Department of Civil Engineering, Cebu Technological University, Cebu, Philippines.

Periodicity:July - September'2024

Abstract

Structural integrity assessment plays a crucial role in engineering, aerospace, and automotive industries, ensuring the reliability and durability of materials and components under various loading conditions. This research presents a multi-scale computational approach integrating finite element analysis (FEA), damage mechanics, and experimental validation techniques to enhance the predictive modelling of structural failure mechanisms. The study focuses on fracture mechanics, fatigue behaviour, and the influence of material microstructures on macroscopic failure patterns. Advanced machine learning algorithms are incorporated to optimize computational efficiency and improve failure prediction accuracy. The results demonstrate that integrating microstructural modelling with experimental data significantly enhances predictive capabilities, leading to safer and more reliable structural designs. The paper also highlights emerging challenges and future directions in computational structural integrity assessments.

Keywords

Structural Integrity, Finite Element Analysis, Multi-Scale Modelling, Fracture Mechanics, Fatigue Behaviour, Machine Learning in Material Science.

How to Cite this Article?

Rodriguez, C. (2024). Advanced Computational Approaches for Structural Integrity Assessment: Multi-Scale Modelling and Experimental Validation. i-manager's Journal on Structural Engineering, 13(2), 35-47.

References

[1]. Alshaikh, I. M., Bakar, B. A., Alwesabi, E. A., & Akil, H. M. (2020, June). Experimental investigation of the progressive collapse of reinforced concrete structures: An overview. In Structures, 25, 881-900. Elsevier.

[2]. Benjeddou, A. (2000). Advances in piezoelectric finite element modeling of adaptive structural elements: A survey. Computers & Structures, 76(1-3), 347-363.

[3]. Castaldo, P., Gino, D., Marano, G. C., & Mancini, G. (2022). Aleatory uncertainties with global resistance safety factors for non-linear analyses of slender reinforced concrete columns. Engineering Structures, 255, 113920.

[4]. Chen, Y., Zhang, L., & Li, X. (2023). Machine learning applications in structural failure prediction. Engineering Failure Analysis, 52(3), 421-439.

[5]. Figueira, G., & Almada-Lobo, B. (2014). Hybrid simulation–optimization methods: A taxonomy and discussion. Simulation Modelling Practice and Theory, 46, 118-134.

[6]. Frangopol, D. M. (1995). Reliability-based optimum structural design. In Probabilistic Structural Mechanics Handbook: Theory and Industrial Applications (pp. 352-387).

[7]. Ghosn, M., Frangopol, D. M., McAllister, T. P., Shah, M., Diniz, S. M. C., Ellingwood, B. R., & Zhao, X. L. (2016). Reliability-based performance indicators for structural members. Journal of Structural Engineering, 142(9), F4016002.

[8]. Gu, X. L., Zhang, B., Wang, Y., & Wang, X. L. (2021). Experimental investigation and numerical simulation on progressive collapse resistance of RC frame structures considering beam flange effects. Journal of Building Engineering, 42, 102797.

[9]. Hartono, W., Kristiawan, S. A., Handayani, D., & Sutopo, W. (2025). Risk Assessment with Monte Carlo Simulation to Improve Bridge Construction Safety. Engineering Proceedings, 84(1), 56.

[10]. Jayasinghe, S. C., Mahmoodian, M., Sidiq, A., Nanayakkara, T. M., Alavi, A., Mazaheri, S., & Setunge, S. (2024). Innovative digital twin with artificial neural networks for real-time monitoring of structural response: A port structure case study. Ocean Engineering, 312, 119187.

[11]. Li, B., Zhang, J., Qu, Y., Chen, D., & Chen, F. (2024). Data-driven predicting of bond strength in corroded BFRP concrete structures. Case Studies in Construction Materials, 21, e03638.

[12]. Liu, X., Athanasiou, C. E., Padture, N. P., Sheldon, B. W., & Gao, H. (2020). A machine learning approach to fracture mechanics problems. Acta Materialia, 190, 105-112.

[13]. Melchers, R. E., & Beck, A. T. (2018). Structural Reliability Analysis and Prediction. John Wiley & Sons.

[14]. Mengesha, G. (2024). Integrating AI in Structural Health Monitoring (SHM): A Systematic Review on Advances, Challenges, and Future Directions. Structural Health Monitoring (SHM): A Systematic Review on Advances, Challenges, and Future.

[15]. Nguyen, T. T., Yvonnet, J., Zhu, Q. Z., Bornert, M., & Chateau, C. (2016). A phase-field method for computational modeling of interfacial damage interacting with crack propagation in realistic microstructures obtained by microtomography. Computer Methods in Applied Mechanics and Engineering, 312, 567-595.

[16]. Ni, P., Li, J., Hao, H., Yan, W., Du, X., & Zhou, H. (2020). Reliability analysis and design optimization of nonlinear structures. Reliability Engineering & System Safety, 198, 106860.

[17]. Qin, W., Chen, E. J., Wang, F., Liu, W., & Zhou, C. (2024). Data-driven models in reliability analysis for tunnel structure: A systematic review. Tunnelling and Underground Space Technology, 152, 105928.

[18]. Raghu Prasad, B. K., Bharatkumar, B. H., Ramachandra Murthy, D. S., Narayanan, R., & Gopalakrishnan, S. (2005). Fracture mechanics model for analysis of plain and reinforced high-performance concrete beams. Journal of Engineering Mechanics, 131(8), 831-838.

[19]. Rusek, J., Firek, K., & Słowik, L. (2020). Extracting structure of bayesian network from data in predicting the damage of prefabricated reinforced concrete buildings in mining areas. Eksploatacja i Niezawodność, 22(4), 658-666.

[20]. Saatci, S., & Vecchio, F. J. (2009). Nonlinear finite element modeling of reinforced concrete structures under impact loads. ACI Structural Journal, 106(5), 717.

[21]. Sabato, A., Dabetwar, S., Kulkarni, N. N., & Fortino, G. (2023). Noncontact sensing techniques for AI-aided structural health monitoring: A systematic review. IEEE Sensors Journal, 23(5), 4672-4684.

[22]. Sedmak, A. (2018). Computational fracture mechanics: An overview from early efforts to recent achievements. Fatigue & Fracture of Engineering Materials & Structures, 41(12), 2438-2474.

[23]. Swait, T. J., Rauf, A., Grainger, R., Bailey, P. B., Lafferty, A. D., Fleet, E. J., & Hayes, S. A. (2012). Smart composite materials for self-sensing and self-healing. Plastics, Rubber and Composites, 41(4-5), 215-224.

[24]. Vecchio, F. J. (2001). Non-linear finite element analysis of reinforced concrete: At the crossroads?. Structural Concrete, 2(4), 201-212.

[25]. Zhang, H., Mullen, R. L., & Muhanna, R. L. (2010). Interval Monte Carlo methods for structural reliability. Structural Safety, 32(3), 183-190.

[26]. Zhao, J., & DeJong, M. (2023). Three-dimensional probabilistic assessment of tunneling induced structural damage using Monte-Carlo method and hybrid finite element model. Computers and Geotechnics, 154, 105122.

Advanced Computational Approaches for Structural Integrity Assessment: Multi-Scale Modelling and Experimental Validation

Abstract

Keywords

How to Cite this Article?

References

If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Options for accessing this content:

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Pdf	35	35	200	20
Online	15	15	200	15
Pdf & Online	35	35	400	25