PREDICTIVE ALGORITHMS FOR STRUCTURAL INTEGRITY IN SCULPTURES

Authors

  • Dr. Pragati Pandit Assistant Professor, Department of Information Technology, Jawahar Education Society's Institute of Technology, Management and Research, Nashik, India
  • Damanjeet Aulakh Centre of Research Impact and Outcome, Chitkara University, Rajpura- 140417, Punjab, India
  • Dr. S Nithya Assistant Professor, Department of Computer Science and Engineering, Presidency University, Bangalore, Karnataka, India
  • Sudhanshu Dev Chitkara Centre for Research and Development, Chitkara University, Himachal Pradesh, Solan, 174103, India
  • Neha Assistant Professor, School of Business Management, Noida international University 203201
  • Dr.Hradayesh kumar Assistant professor Department of Arts, Mangalayatan University, Aligarh

DOI:

https://doi.org/10.29121/shodhkosh.v6.i2s.2025.6715

Keywords:

Predictive Algorithms, Structural Integrity, Sculpture Conservation, Finite-Element Analysis, Machine Learning, Material Degradation Modeling

Abstract [English]

In computational modeling has provided avenues to evaluate and maintain the structural integrity of sculptures, especially those that are exposed to environmental factors or aging materials, or complicated distributions of loads. Finite-element analysis, machine learning, and sensor-based data collection together in predictive algorithms provide a proactive approach of determining the risk of failure before it can be seen to be deteriorating. These algorithms create probabilistic models and simulate stress propagation, micro-fracture development and deformation at different conditions by combining high-resolution 3D scans with material performance data of the past. The ability to predict stability of the sculptures over a long period of time, without any invasive processes, enables the conservators, engineers, and artists to make the sculptures safer and more accurately preserved. The use of predictive algorithms is not restricted to the field of diagnostics but can also be used in decision-making during restoration and preventative maintenance. The adaptive models are fed by real-time monitoring systems which have accelerators, strain gauge, and environmental sensors which continuously feed the data into this adaptive model, which in turn improves its predictions with time. Anomaly detection and regression-based forecasting are methods of machine-learning that can subsequently categorize high-risk areas and predict schedules of possible structural failure. This information-driven modeling and conservation practice synergy does not only reduce the cost of restoration, but also aids to keep artistic integrity of sculptures low as well by minimizing cases of unwarranted interventions. Due to the development of predictive algorithms, their great potentials lie in the fact that sculpture conservation can be made a more precise, efficient, and scientifically based discipline.

References

Abdelmalek-Lee, E., and Burton, H. (2023). A Dual Kriging–Xgboost Model for Reconstructing Building Seismic Responses Using Strong Motion Data. Bulletin of Earthquake Engineering, 1–27. DOI: https://doi.org/10.1007/s10518-023-01624-y

Ao, Y., Li, S., and Duan, H. (2025). Artificial Intelligence-Aided Design (AIAD) for Structures and Engineering: A State-of-The-Art Review and Future Perspectives. Archives of Computational Methods in Engineering, 32, 4197–4224. https://doi.org/10.1007/s11831-025-10264-1 DOI: https://doi.org/10.1007/s11831-025-10264-1

Aziz, M. T., Osabel, D. M., Kim, Y., Kim, S., Bae, J., and Tsavdaridis, K. D. (2025). State-of-the-Art Artificial Intelligence Techniques in Structural Engineering: A Review of Applications and Prospects. Results in Engineering, 28. https://doi.org/10.1016/j.rineng.2025.107882 DOI: https://doi.org/10.1016/j.rineng.2025.107882

Esteghamati, M. Z., and Flint, M. M. (2021). Developing Data-Driven Surrogate Models for Holistic Performance-Based Assessment of Mid-Rise RC Frame Buildings at Early Design. Engineering Structures, 245, 112971. DOI: https://doi.org/10.1016/j.engstruct.2021.112971

Gao, C., and Elzarka, H. (2021). The Use of Decision Tree Based Predictive Models for Improving the Culvert Inspection Process. Advanced Engineering Informatics, 47, 101203. DOI: https://doi.org/10.1016/j.aei.2020.101203

Hu, S., Wang, W., Alam, M. S., Zhu, S., and Ke, K. (2023). Machine Learning-Aided Peak Displacement and Floor Acceleration-Based Design of Hybrid Self-Centering Braced Frames. Journal of Building Engineering, 72, 106429. DOI: https://doi.org/10.1016/j.jobe.2023.106429

Jayasinghe, S., Mahmoodian, M., Alavi, A., Sidiq, A., Sun, Z., Shahrivar, F., Setunge, S., and Thangarajah, J. (2025). Application of Machine Learning for Real-Time Structural Integrity Assessment of Bridges. Civil Engineering, 6(1), Article 2. https://doi.org/10.3390/civileng6010002 DOI: https://doi.org/10.3390/civileng6010002

Jin, T., Cheng, X., Xu, S., Lai, Y., and Zhang, Y. (2023). Deep Learning Aided Inverse Design of the Buckling-Guided Assembly for 3D Frame Structures. Journal of the Mechanics and Physics of Solids, 179, 105398. DOI: https://doi.org/10.1016/j.jmps.2023.105398

Ko, H., Witherell, P., Lu, Y., Kim, S., and Rosen, D. W. (2021). Machine Learning and Knowledge Graph Based Design Rule Construction for Additive Manufacturing. Additive Manufacturing, 37, 101620. DOI: https://doi.org/10.1016/j.addma.2020.101620

Latif, I., Banerjee, A., and Surana, M. (2022). Explainable Machine Learning Aided Optimization of Masonry Infilled Reinforced Concrete Frames. Structures, 44, 1751–1766. DOI: https://doi.org/10.1016/j.istruc.2022.08.115

Mousavi, M., Ayati, M., Hairi-Yazdi, M. R., and Siahpour, S. (2023). Robust Linear Parameter Varying Fault Reconstruction of Wind Turbine Pitch Actuator Using Second-Order Sliding Mode Observer. Journal of Electrical and Computer Engineering Innovations, 11, 229–241.

Rashidi Nasab, A., and Elzarka, H. (2023). Optimizing Machine Learning Algorithms for Improving Prediction of Bridge Deck Deterioration: A Case Study of Ohio Bridges. Buildings, 13(6), 1517. https://doi.org/10.3390/buildings13061517 DOI: https://doi.org/10.3390/buildings13061517

Siahpour, S., Ayati, M., Haeri-Yazdi, M., and Mousavi, M. (2022). Fault Detection and Isolation of Wind Turbine Gearbox Via Noise-Assisted Multivariate Empirical Mode Decomposition Algorithm. Energy Equipment and Systems, 10, 271–286.

Yang, L., and Huang, E. (2025). Structural Health Monitoring Data Analysis Using Deep Learning Techniques. In Proceedings of the 2024 8th International Conference on Electronic Information Technology and Computer Engineering. Association for Computing Machinery, 913–927. https://doi.org/10.1145/3711129.3711286 DOI: https://doi.org/10.1145/3711129.3711286

Zhao, P., Liao, W., Huang, Y., and Lu, X. (2024). Beam Layout Design of Shear Wall Structures Based on Graph Neural Networks. Automation in Construction, 158, 105223. DOI: https://doi.org/10.1016/j.autcon.2023.105223

Downloads

Published

2025-12-16

How to Cite

Pandit, P., Aulakh, D., S Nithya, Dev, S., Neha, & kumar, H. (2025). PREDICTIVE ALGORITHMS FOR STRUCTURAL INTEGRITY IN SCULPTURES. ShodhKosh: Journal of Visual and Performing Arts, 6(2s), 44–55. https://doi.org/10.29121/shodhkosh.v6.i2s.2025.6715