Citation :

Sapna Shah, Meenal Mategaonkar, Ami Munshi, "Development of a Data Preprocessing and Feature Engineering Pipeline for Shear Strength of Shear Wall," International Journal of Civil Engineering, vol. 13, no. 10, pp. 46-61, 2026. Crossref, https://doi.org/10.14445/23488352/IJCE-V13I10P103

Abstract

Concrete shear wall plays a crucial role in effective distribution of seismic forces, thereby adding significant value to earthquake resistance behavior for a structural frame. Hence, understanding failure patterns and deriving shear strength of shear wall has been a significant point of attention for researchers to understand and develop an effective system for structures located especially in seismically active zones. Current experimental dataset, which includes geometrical and material parameters that are usually limited in size, is found to be insufficient for prediction. Hence, in this study, experimental data from two sources, one comprising 252 shear wall data and other 182, have been combined and augmented using mathematical methods, including quantile jittering and noise injection. This is followed by data outlier treatment via quantile capping, winzoraization, and Interquartile Range (IQR) methods. The resulting dataset now comprises 1736 data points, one output variable, and 13 predictors, which is available for subsequent feature engineering. Web thickness of wall tw is the most influential parameter in feature engineering conducted using univariate feature selection and RFE (regressive feature elimination with random forest). The contribution of tw is found to be significant in Shapley Additive Expansion (SHAP) as well. The 13 predictors have been further reduced to 10 orthogonal components using Principal Component Analysis (PCA). This extensive dataset provides a strong basis for machine learning applications and model development that aim to forecast shear strength with greater precision and generalizability. A reliable tool for creating precise predictive models to understand shear wall behavior has been provided by this dataset.

Keywords

Data augmentation, Shear wall, Shear strength, SHAP, Feature engineering.

References

1. Weng Y. Kam, Stefano Pampanin, and Ken Elwood, “Seismic Performance of Reinforced Concrete Buildings in the 22 February Christchurch (Lyttelton) Earthquake,” Bulletin of the New Zealand Society for Earthquake Engineering, vol. 44, no. 4, pp. 239-278, 2011.
   [CrossRef] [Google Scholar] [Publisher Link]
2. Ahed Habib et al., “Seismic Behavior of Squat Reinforced Concrete Shear Walls: A State-of-the-Art Review,” SDHM Structural Durability and Health Monitoring, vol. 19, no. 3, pp. 417-439, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
3. Yu-Cheng Hsu et al., “Shear Strength Prediction of Reinforced Concrete Walls with Opening and Boundary Elements,” Journal of Structural Engineering, vol. 149, no. 7, 2023.
   [CrossRef] [Google Scholar] [Publisher Link]
4. Khuong Le Nguyen et al., “Machine Learning Approaches for Lateral Strength Estimation in Squat Shear Walls: A Comparative Study and Practical Implications,” Expert Systems with Applications, vol. 239, pp. 1-19, 2024.
   [CrossRef] [Google Scholar] [Publisher Link]
5. Sujith Mangalathu et al., “Data-Driven Machine-Learning-based Seismic Failure mode Identification of Reinforced Concrete Shear Walls,” Engineering Structures, vol. 208, 2020.
   [CrossRef] [Google Scholar] [Publisher Link]
6. Siming Tian, Xiangyong Ni, and Yang Wang, “Seismic Shear Strength Prediction of Reinforced Concrete Shear Walls by Stacking Multiple Machine Learning Models,” Applied Sciences, vol. 15, no. 5, pp. 1-23, 2025.
   [CrossRef] [Google Scholar] [Publisher Link]
7. Ahmed Gondia et al., “Mechanics-Guided Genetic Programming Expression for Shear-Strength Prediction of Squat Reinforced Concrete Walls with Boundary Elements,” Journal of Structural Engineering, vol. 146, no. 11, pp. 2020.
   [CrossRef] [Google Scholar] [Publisher Link]
8. Hosein Naderpour et al., “Shear Strength Prediction of Reinforced Concrete Shear Wall using ANN, GMDH-NN and GEP,” Journal of Soft Computing in Civil Engineering, vol. 6, no. 1, pp. 66-87, 2022.
   [CrossRef] [Google Scholar] [Publisher Link]
9. De-Cheng Feng et al., “Interpretable XGBoost-SHAP Machine-Learning Model for Shear Strength Prediction of Squat RC Walls,” Journal of Structural Engineering, vol. 147, no. 11, 2021.
   [CrossRef] [Google Scholar] [Publisher Link]
10. Behrooz Keshtegar et al., “Novel Hybrid Machine Learning Model for Predicting Shear Strength of Reinforced Concrete Shear Walls,” Engineering with Computers, vol. 38, no. 5, pp. 3915-3926, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
11. Leonardo M. Massone, and Francisco Melo, “General Solution for Shear Strength Estimate of RC Elements based on Panel Response,” Engineering Structures, vol. 132, pp. 239-252, 2018.
    [CrossRef] [Google Scholar] [Publisher Link]
12. Gyu Il Kim, and Kyungyong Chung, “Extraction of Features for Time Series Classification Using Noise Injection,” Sensors, vol. 24, no. 19, pp. 1-15, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
13. Sumeyra Demir et al., “Data Augmentation for Time Series Regression: Applying Transformations, Autoencoders and Adversarial Networks to Electricity Price Forecasting,” Applied Energy, vol. 304, pp. 1-19, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
14. Hongyi Zhang et al., “mixup: Beyond Empirical Risk Minimization,” Proceedings of the International Conference on Learning, pp. 1-13, 2018.
    [CrossRef] [Google Scholar] [Publisher Link]
15. Lin Sun et al., “Feature Selection using Fisher Score and Multilabel Neighborhood Rough Sets for Multilabel Classification,” Information Sciences, vol. 578, pp. 887-912, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
16. Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2^nd ed., Springer, pp. 1-745, 2009.
    [CrossRef] [Google Scholar] [Publisher Link]
17. X. L. Chen et al., “Prediction of Shear Strength for Squat RC Walls using a Hybrid ANN–PSO Model,” Engineering with Computers, vol. 34, no. 2, pp. 367-383, 2018.
    [CrossRef] [Google Scholar] [Publisher Link]
18. Scott M Lundberg, and Su-In Lee, “A Unified Approach to Interpreting Model Predictions,” Advances in Neural Information Processing System, vol. 30, pp. 1-10, 2017.
    [Google Scholar] [Publisher Link]
19. ACI CODE-318-25: Building Code for Structural Concrete—Code Requirements and Commentary (ACI CODE-318-25), American Concrete Institute, pp. 1-702, 2025.
    [Google Scholar] [Publisher Link]
20. Eurocode 8: Design of Structures for Earthquake Resistance – Part 1: General Rules, Seismic Actions and Rules for Buildings, European Committee for Standardization, pp. 1-10, 2005.
    [Google Scholar] [Publisher Link]
21. De-Cheng Feng et al., “Machine Learning-Based Compressive Strength Prediction for Concrete: An Adaptive Boosting Approach,” Construction and Building Materials, vol. 230, pp. 1-20, 2021.
    [CrossRef] [Google Scholar] [Publisher Link]
22. IIT Kanpur (National Information Centre of Earthquake Engineering), Earthquake Tip 23: Why are buildings with shear walls preferred in seismic regions? Indian Institute of Technology Kanpur, Kanpur, India. Available from NICEE Earthquake Tips series, 2005.
    [Publisher Link]
23. Ian T. Jolliffe, and Jorge Cadima, “Principal Component Analysis: A Review and Recent Developments,” Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences, vol. 374, no. 2065, pp. 1-16, 2016.
    [CrossRef] [Google Scholar] [Publisher Link]    
24. Frank J. Vecchio, and Michael P. Collins, “The Modified Compression-Field Theory for Reinforced Concrete Elements Subjected to Shear,” ACI Journal Proceedings, vol. 83, no. 2, pp. 219-231, 1986.
    [CrossRef] [Google Scholar] [Publisher Link]
25. I-Tung Yang, and Handy Prayogo, “Interpretable Data-Augmented Machine Learning Framework for Robust Failure Mode Classification of RC Shear Wall,” Journal of Building Engineering, vol. 112, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
26. Guoxu Zhao et al., “Automated Design of Shear Wall Structures using Reinforcement Learning with Code Compliance,” Engineering Structures, vol. 353, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
27. Kevin Karanja Kuria, and Orsolya Kegyes-Brassai, “Digital Twin-based Machine Learning Framework for Predicting Nonlinear Seismic Response of Reinforced Concrete Shear Walls using Analytical Data,” Scientific Reports, vol. 16, pp. 1-18, 2026.
    [CrossRef] [Google Scholar] [Publisher Link]
28. Mohsen Zaker Esteghamati et al., “Beyond Development: Challenges in Deploying Machine-Learning Models for Structural Engineering Applications,” Journal of Structural Engineering, vol. 151, no. 6, pp. 1-28, 2025.
    [CrossRef] [Google Scholar] [Publisher Link]
29. Mehmet Tan, Gulsen Taskinkaya, and Zeynep Tuna Deger, “Machine Learning-Based Framework for Failure Forecast and Shear Strength Estimation of Non-Conforming RC Shear Walls,” Journal of Earthquake Engineering, vol. 28, no. 12, pp. 3387-3409, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
30.  SeyedeZahra Golazad et al., “From Raw to Refined: Data Preprocessing for Construction Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL) Models,” Automation in Construction, vol. 168, pp. 1-53, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
31. Chao-Lie Ning, and Bing Li, “Probabilistic Development of Shear Strength Model for Reinforced Concrete squat Walls,” Earthquake Engineering Structural Dynamic, vol. 46, no. 6, pp. 877-897, 2017.
    [CrossRef] [Google Scholar] [Publisher Link]