[1] Palaniappan, S.K., Singh, M.K., Rangappa, S.M. and Siengchin, S., 2023. Eco-friendly Biocomposites: A Step Towards Achieving Sustainable Development Goals. Composites, 7(12), pp. 7373.
[2] Kumar, M, Saini, J.S. and Bhunia, H., 2020. Performance of mechanical joints prepared from carbon-fiber-reinforced polymer nanocomposites under accelerated environmental aging. Journal of Materials Engineering and Performance 29, pp. 7511-7525.
[3] Kumar, M., Saini, J.S., Bhunia, H. and Chowdhury, S.R., 2021. The behavior of mechanical joints prepared from EB-cured CFRP nanocomposites subjected to hygrothermal aging under bolt preloads. Applied Composite Materials, 28, pp.271-296.
[4] Dhiman, N., Saini, J.S. and Kumar, M., 2023. Hygrothermal effect on strength of thermally cured glass epoxy nanocomposite made joints. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 237(23), pp. 5692-5707.
[5] De'Nève, B. and Shanahan, M.E.R., 1993. Water absorption by an epoxy resin and its effect on the mechanical properties and infra-red spectra. Polymer, 34(24), pp. 5099-5105.
[6] Zheng, Q. and Morgan, R.J., 1993. Synergistic thermal-moisture damage mechanisms of epoxies and their carbon fiber composites. Journal of Composite Materials, 27(15), pp. 1465-1478.
[7] Ray, B.C., 2006. Temperature effect during humid ageing on interfaces of glass and carbon fibers reinforced epoxy composites. Journal of colloid and interface science, 298(1), pp. 111-117.
[8] Suyambulingam, I., Rangappa, S.M. and Siengchin, S., 2023. Advanced Materials and Technologies for Engineering Applications. Applied Science and Engineering Progress, 16(3), pp. 6760-6760.
[9] Tian, W. and Hodgkin, J., 2010. Long‐term aging in a commercial aerospace composite sample: Chemical and physical changes. Journal of Applied Polymer Science, 115(5), pp. 2981-2985.
[10] Dao, B., Hodgkin, J.H., Krstina, J., Mardel, J. and Tian, W., 2007. Accelerated ageing versus realistic ageing in aerospace composite materials. IV. Hot/wet ageing effects in a low temperature cure epoxy composite. Journal of applied polymer science, 106(6), pp. 4264-4276.
[11] Dao, B., Hodgkin, J., Krstina, J., Mardel, J. and Tian, W., 2010. Accelerated aging versus realistic aging in aerospace composite materials. V. The effects of hot/wet aging in a structural epoxy composite. Journal of applied polymer science, 115(2), pp. 901-910.
[12] Alessi, S., Pitarresi, G. and Spadaro, G., 2014. Effect of hydrothermal ageing on the thermal and delamination fracture behaviour of CFRP composites. Composites Part B: Engineering, 67, pp. 145-153.
[13] Hong, B., Xian, G. and Wang, Z., 2018. Durability study of pultruded carbon fiber reinforced polymer plates subjected to water immersion. Advances in Structural Engineering, 21(4), pp. 571-579.
[14] Yin, B.B. and Liew, K.M., 2021. Machine learning and materials informatics approaches for evaluating the interfacial properties of fiber-reinforced composites. Composite Structures, 273, pp. 114328.
[15] Naser, M.Z., Thai, S. and Thai, H.T., 2021. Evaluating structural response of concrete-filled steel tubular columns through machine learning. Journal of Building Engineering, 34, pp. 101888.
[16] Chaabene, W.B., Flah, M. and Nehdi, M.L., 2020. Machine learning prediction of mechanical properties of concrete: Critical review. Construction and Building Materials, 260, pp. 119889.
[17] Ford, E., Maneparambil, K., Rajan, S. and Neithalath, N., 2021. Machine learning-based accelerated property prediction of two-phase materials using microstructural descriptors and finite element analysis. Computational Materials Science, 191, pp. 110328.
[18] Sacco, C., Radwan, A.B., Anderson, A., Harik, R. and Gregory, E., 2020. Machine learning in composites manufacturing: A case study of Automated Fiber Placement inspection. Composite Structures, 250, pp. 112514.
[19] Machello, C., Bazli, M., Rajabipour, A., Rad, H.M., Arashpour, M. and Hadigheh, A., 2023. Using machine learning to predict the long-term performance of fibre-reinforced polymer structures: A state-of-the-art review. Construction and Building Materials, 408, pp. 133692.
[20] Alhusban, M., Alhusban, M. and Alkhawaldeh, A.A., 2023. The Efficiency of Using Machine Learning Techniques in Fiber-Reinforced-Polymer Applications in StructuralEngineering. Sustainability, 16(1), pp. 11.
[21] Kumar, M., Saini, J.S. and Bhunia, H., 2023. Radiation Curing of Fiber Reinforced Polymer Composite Based Mechanical Joints. In Applications of High Energy Radiations: Synthesis and Processing of Polymeric Materials (pp. 107-148). Singapore: Springer Nature Singapore.
[22] Chauhan, S., Singh, M. and Kumar Aggarwal, A., 2021. An effective health indicator for bearing using corrected conditional entropy through diversity-driven multi-parent evolutionary algorithm. Structural Health Monitoring, 20(5), pp. 2525-2539.
[23] Kumar, A. and Kumar, R., 2017. Time-frequency analysis and support vector machine in automatic detection of defect from vibration signal of centrifugal pump. Measurement, 108, pp. 119-133.
[24] Kumar, M., Saini, J.S., Bhunia, H. and Chowdhury, S.R., 2021. Aging of bolted joints prepared from electron‐beam‐cured multiwalled carbon nanotube‐based nanocomposites with variable torques. Polymer Composites, 42(8), pp. 4082-4104.
[25] Kumar, M., Saini, J.S. and Bhunia, H., 2021. Investigations on MWCNT embedded carbon/epoxy composite joints subjected to hygrothermal aging under bolt preloads. Fibers and Polymers, 22(7), pp.1957-1975.