Title:
A Framework to Set Performance Requirements for Structural Component Models: Application to Reinforced Concrete Wall Shear Strength
Author(s):
Matias Rojas-Leon, Saman A. Abdullah, Kristijan Kolozvari, and John W. Wallace
Publication:
Structural Journal
Volume:
121
Issue:
1
Appears on pages(s):
75-88
Keywords:
machine learning; model performance; statistics; structural wall; wall shear
DOI:
10.14359/51739186
Date:
1/1/2024
Abstract:
Numerous models to predict the shear strength of reinforced
concrete structural walls have been proposed in the literature.
Evaluation of the predictive performance of new models relative
to existing models is often challenging because the models were
created with different levels of complexity and calibrated using
different databases. More complex models are expected to have
less variance than simpler models, and target performance metrics
for models of different complexity do not exist. In addition, a
common, comprehensive database should be used to enable direct
comparisons between different models. To address these issues, the
present study applies statistical and machine-learning approaches
to propose a five-step framework to establish target performance
metrics for models with different levels of complexity. Application
of the framework is demonstrated by addressing the problem of
estimating wall shear strength using a comprehensive database of
340 shear-controlled wall tests.
Related References:
Abdullah, S. A., 2019, “Reinforced Concrete Structural Walls: Test Database and Modeling Parameters,” PhD dissertation, University of California, Los Angeles, Los Angeles, CA, 304 pp.
Abdullah, S. A., and Wallace, J. W., 2018, “UCLA-RC Walls Database for Reinforced Concrete Structural Walls,” Proceedings of the 11th National Conference in Earthquake Engineering, Los Angeles, CA.
Abdullah, S. A., and Wallace, J. W., 2021, “New Nonlinear Modeling Parameters and Acceptance Criteria for RC Structural Walls,” The 2021 Annual Conference of Los Angeles Tall Buildings Structural Design Council, Nov. 12, Los Angeles, CA.
ACI Committee 318, 2008, “Building Code Requirements for Structural Concrete (ACI 318-08) and Commentary (ACI 318R-08),” American Concrete Institute, Farmington Hills, MI, 473 pp.
ACI Committee 318, 2011, “Building Code Requirements for Structural Concrete (ACI 318-11) and Commentary (ACI 318R-11),” American Concrete Institute, Farmington Hills, MI, 503 pp.
ACI Committee 318, 2014, “Building Code Requirements for Structural Concrete (ACI 318-14) and Commentary (ACI 318R-14),” American Concrete Institute, Farmington Hills, MI, 520 pp.
ACI Committee 318, 2019, “Building Code Requirements for Structural Concrete (ACI 318-19) and Commentary (ACI 318R-19) (Reapproved 2022),” American Concrete Institute, Farmington Hills, MI, 624 pp.
AIJ, 1999, “Structural Design Guidelines for Reinforced Concrete Buildings,” Architectural Institute of Japan, Tokyo, Japan.
Alzubi, J.; Nayyar, A.; and Kumar, A., 2018, “Machine Learning from Theory to Algorithms: An Overview,” Journal of Physics: Conference Series, 2nd National Conference on Computational Intelligence (NCCI 2018), Bangalore, India, IOP Science, V. 1142, pp. 012012
Amazon Web Services, Inc., 2016, “Amazon Machine Learning Developer Guide,” AWS, Seattle, WA.
ASCE/SEI 43-05, 2005, “Seismic Design Criteria for Structures, Systems, and Components in Nuclear Facilities,” American Society of Civil Engineers, Reston, VA.
Barda, F.; Hanson, J. M.; and Corley, W. G., 1977, “Shear Strength of Low-Rise Walls with Boundary Elements,” Reinforced Concrete Structures in Seismic Zones, SP-53, American Concrete Institute, Farmington Hills, MI, pp. 149-202.
Barret, J., 1974, “The Coefficient of Determination – Some Limitations,” The American Statistician, V. 28, No. 1, pp. 19-20.
Breiman, L., 2001, “Random Forests,” Machine Learning, V. 45, No. 1, pp. 5-32. doi: 10.1023/A:1010933404324
Bzdok, D.; Altman, N.; and Krzywinski, M., 2018, “Points of Significance: Statistics Versus Machine Learning,” Nature Methods, V. 15, No. 4, pp. 233-234. doi: 10.1038/nmeth.4642
CSA A23.3-14, 2014, “Design of Concrete Structures,” CSA Group, Toronto, ON, Canada.
Cardenas, A. E., and Magura, D. D., 1972, “Strength of High-Rise Shear Walls-Rectangular Cross Section,” Special Publication (American Philosophical Society), V. 36, pp. 119-150.
Carrillo, J., and Alcocer, S., 2013, “Shear Strength of Reinforced Concrete Walls for Seismic Design of Low-Rise Housing,” ACI Structural Journal, V. 110, No. 3, May-June, pp. 415-426.
Chen, X. L.; Fu, J. P.; Yao, J. L.; and Gan, J. F., 2018, “Prediction of Shear Strength for Squat RC Walls Using a Hybrid ANN-PSO Model,” Engineering with Computers, V. 34, No. 2, pp. 367-383. doi: 10.1007/s00366-017-0547-5
Dey, A., 2016, “Machine Learning Algorithms: A Review,” International Journal of Science and Research, V. 7, No. 3, pp. 1174-1179.
EN 1998-1:2004, 2004, Eurocode 8, “Design of Structures for Earthquake Resistance, Part 1: General Rules, Seismic Actions and Rules for Buildings,” European Committee for Standardization, Brussels, Belgium.
Feng, D. C.; Wang, W. J.; Mangalathu, S.; and Taciroglu, E., 2021, “Interpretable XGBoost-SHAP Machine-Learning Model for Shear Strength Prediction of Squat RC Walls,” Journal of Structural Engineering, ASCE, V. 147, No. 11, p. 04021173. doi: 10.1061/(ASCE)ST.1943-541X.0003115
Gonfalonieri, A., 2019, “5 Ways to Deal with the Lack of Data in Machine Learning,” KDnuggets, https://www.kdnuggets.com/2019/06/5-ways-lack-data-machine-learning.html. (last accessed Nov. 2, 2023)
Google Developers, 2022, “The Size and Quality of a Data Set,” Google, Mountain View, CA, https://developers.google.com/machine-learning/data-prep/construct/collect/data-size-quality. (last accessed Nov. 2, 2023)
Gulec, C., and Whittaker, A. S., 2011, “Empirical Equations for Peak Shear Strength of Low Aspect Ratio Reinforced Concrete Walls,” ACI Structural Journal, V. 108, No. 1, Jan.-Feb., pp. 80-89.
Gulec, C. K.; Whittaker, A. S.; and Bozidar Stojadinovic, B., 2009, “Peak Shear Strength of Squat Reinforced Concrete Walls with Boundary Barbells or Flanges,” ACI Structural Journal, V. 106, No. 3, May-June, pp. 368-377.
Hoerl, A. E., and Kennard, R. W., 1970, “Ridge Regression: Biased Estimation for Nonorthogonal Problems,” Technometrics, V. 12, No. 1, pp. 55-67. doi: 10.1080/00401706.1970.10488634
Höge, M.; Wöhling, T.; and Nowak, W., 2018, “A Primer for Model Selection: The Decisive Role of Model Complexity,” Water Resources Research, V. 54, No. 3, pp. 1688-1715. doi: 10.1002/2017WR021902
Hwang, S.-J., and Lee, H.-J., 2002, “Strength Prediction for Discontinuity Regions by Softened Strut-and-Tie Model,” Journal of Structural Engineering, ASCE, V. 128, No. 12, pp. 1519-1526. doi: 10.1061/(ASCE)0733-9445(2002)128:12(1519)
Kassem, W., 2015, “Shear Strength of Squat Walls: A Strut-and-Tie Model and Closed-Form Design Formula,” Engineering Structures, V. 84, pp. 430-438. doi: 10.1016/j.engstruct.2014.11.027
Keshtegar, B.; Nehdi, M. L.; Trung, N.-T.; and Kolahchi, R., 2021, “Predicting Load Capacity of Shear Walls Using SVR–RSM Model,” Applied Soft Computing, V. 112, Nov., p. 107739. doi: 10.1016/j.asoc.2021.107739
Kim, J.-H., and Park, H.-G., 2020, “Shear and Shear-Friction Strengths of Squat Walls with Flanges,” ACI Structural Journal, V. 117, No. 6, Nov., pp. 269-280. doi: 10.14359/51728075
Li, H., and Li, B., 2002, “Experimental Study on Seismic Restoring Performance of Reinforced Concrete Shear Walls,” Journal of Building Structures, V. 32, No. 5, pp. 728-732.
Looi, D. T. W., and Su, R. K. L., 2017, “Predictive Seismic Shear Capacity of Rectangular Squat RC Shear Walls in Flexural and Shear Zones,” 16th World Conference on Earthquake Engineering, Santiago, Chile, pp. 9-13.
Moradi, M., and Hariri-Ardebili, M., 2019, “Developing a Library of Shear Walls Database and the Neural Network Based Predictive Meta-Model,” Applied Sciences, V. 9, No. 12, p. 2562. doi: 10.3390/app9122562
Morgan, N., and Bourlard, H., 1989, “Generalization and Parameter Estimation in Feedforward Nets: Some Experiments,” Advances in Neural Information Processing Systems, pp. 630-637
NZS 3101-06, 1995, “New Zealand Concrete Structures Standards,” Standards New Zealand, Wellington, New Zealand.
Rojas-León, M., 2022, “Framework to Set Model Performance Requirements Applied to the RC Wall Shear Strength Problem and Proposition of New Code-Oriented Equation,” PhD dissertation, University of California, Los Angeles, Los Angeles, CA, 260 pp.
Sánchez-Alejandre, A., and Alcocer, S., 2010, “Shear Strength of Squat Reinforced Concrete Walls Subjected to Earthquake Loading – Trends and Models,” Engineering Structures, V. 32, No. 8, pp. 2466-2476. doi: 10.1016/j.engstruct.2010.04.022
Segal, M., and Xiao, Y., 2011, “Multivariate Random Forests,” Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery, V. 1, No. 1, pp. 80-87. doi: 10.1002/widm.12
Segura, C. L., and Wallace, J. W., 2018, “Seismic Performance Limitations and Detailing of Slender Reinforced Concrete Walls,” ACI Structural Journal, V. 115, No. 3, May, pp. 849-859. doi: 10.14359/51701918
Tanaka, J. S., 1987, “How Big Is Big Enough?: Sample Size and Goodness of Fit in Structural Equation Models with Latent Variables,” Child Development, V. 58, No. 1, pp. 134-146. doi: 10.2307/1130296
Tibshirani, R., 1996, “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society, V. 58, No. 1, pp. 267-288.
Wood, S. L., 1990, “Shear Strength of Low-Rise Reinforced Concrete Walls,” ACI Structural Journal, V. 87, No. 1, Jan.-Feb., pp. 99-107.
Zhang, C., and Ma, Y., 2012, Ensemble Machine Learning: Methods and Applications, Springer.
Zheng, S.; Qin, Q.; Yang, W.; Gan, C.; Zhang, Y.; and Ding, S., 2015, “Experimental Research on the Seismic Behaviors of Squat RC Shear Walls Under Offshore Atmospheric Environment,” Journal of Harbin Institute of Technology, V. 47, No. 12, pp. 64-69. (in Chinese)
Zou, H., and Hastie, T., 2005, “Regularization and Variable Selection via Elastic Net,” Journal of the Royal Statistical Society. Series B, Statistical Methodology, V. 67, No. 2, pp. 301-320. doi: 10.1111/j.1467-9868.2005.00503.x