Abstract
The recent surge in earthquake engineering is the use of machine learning methods to develop predictive models for structural behavior. Complex black-box models are typically used for decision-making to achieve high accuracy; however, as important as high accuracy, it is essential for engineers to understand how the model makes the decision and verify that the model is physically meaningful. With this motivation, this study proposes a glass-box (interpretable) classification model to predict the seismic failure mode of conventional reinforced concrete shear (structural) walls. Reported experimental damage information of 176 conventional shear walls tested under reverse cyclic loading was designated as class types, whereas key design properties (e.g., compressive strength of concrete, axial load ratio, and web reinforcement ratio) of shear walls were used as the basic classification features. The trade-off between model complexity and model interpretability was discussed using eight Machine Learning (ML) methods. The results showed that the decision tree (DT) method was a more convenient classifier with higher interpretability with a higher classification accuracy than its counterparts. Also, to enhance the practicality of the model, a feature reduction was conducted to reduce the complexity of the proposed classifier with higher classification performance, and the most relevant features were identified, namely compressive strength of concrete, wall aspect ratio, transverse boundary, and web reinforcement ratio. The ability of the final DT model to predict the failure modes was validated with a classification rate of around 90%. The proposed model aims to provide engineers interpretable, robust, and rapid predictions in seismic performance assessment.
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Notes
All MATLAB codes for the proposed approach are available upon request.
References
Wasti ST, Guney O (2006) (Eds.), Advances in earthquake engineering for urban risk reduction, vol. 66 (Springer)
Verderame GM, Lunio L (2009) Gaetano M (2009) Elastic Period of Existing RC-MRF Buildings. Eurocode 8:79–94
Priestley BMJN, Verma R, Xiao Y (1994) Seismic shear strength of reinforced concrete columns. J Struct Eng ASCE 120(8):2310
Zhu L, Elwood KJ, Haukaas T (2007) Classification and seismic safety evaluation of existing reinforced concrete columns. J Struct Eng ASCE 133:1316
ASCE, ASCE Standard, ASCE/SEI, 41-17, Seismic Evaluation and Retrofit of Existing Buildings. ASCE.: Standard (American Society of Civil Engineers) (2017)
ACI, Standard Requirements for Seismic Evaluation and Retrofit of Existing Concrete Buildings (ACI 369.1M-17) and Commentary (American Concrete Institute) (2017)
Alcantara P, Imai H (2000) Failure mode classification of reinforced concrete columns by the analysis of the strain distribution in the main reinforcement. In: Proceedings of the 12th world conference on earthquake engineering, Auckland, NZ
Yoshikawa H, Miyagi T (2001) Ductility and Failure Modes of Singly Reinforced Concrete Columns, Modeling of Inelastic Behavior of RC Structures Under Seismic Loads pp 35–368
Massone LM, Wallace JW (2004) Load-deformation responses of slender reinforced concrete walls. ACI Struct J 101(1):103
Orakcal K, Massone LM, Wallace JW (2009) Shear strength of lightly reinforced wall piers and spandrels. ACI Struct J 106(4):455
Salonikios TN, Kappos AJ (1999) Cyclic load behavior of low-slenderness reinforced concrete walls: design basis and test results. ACI Struct J 96(4):649–660
Sittipunt C, Wood SL, Lukkunaprasit P, Pattararattanakul P (2001) Cyclic behavior of reinforced concrete structural walls with diagonal web reinforcement. Struct J 98(4):554
Wood S (1991) Observed Behavior of Slender RC Walls Subjected to Cyclic Loadings, Inelast Response Des Earthq-Res Concr Struct pp 334–344
Fang E (1992) Failure Modes of RC Tall Shear Walls. Concr Shear Earthq pp 125–133
Greifenhagen C, Papas D, Lestuzzi P (2005) Static-cyclic tests on reinforced concrete shear walls with low reinforcement ratios. Gen Inf pp 1–113
Abbasnia R, Bagheri M (2011) A new rule-based strategy to determine the failure modes of structural walls. J Phys: Conf Ser (IOP Publishing) 305:012127
Grammatikou S, Biskinis D, Fardis MN (2015) Strength, deformation capacity and failure modes of RC walls under cyclic loading. Bull Earthq Eng 13(11):3277
Sudret B, Mai C, Konakli K (2014) Assessment of the lognormality assumption of seismic fragility curves using non-parametric representations. Earthq Eng Struct Dyn 43:2075
Jiang Y, Luo J, Liao G, Zhao Y (2015) An efficient method for generation of uniform support vector and its application in structural failure function fitting. Struct Saf 54:1
Bourinet JM, Deheeger F, Lemaire M (2011) Assessing small failure probabilities by combined subset simulation and Support Vector Machines. Struct Saf 33(6):343
Salazar F, Toledo MA, Oñate E, Morán R (2015) An empirical comparison of machine learning techniques for dam behaviour modelling. Struct Saf 56:9
Zhang Y, Burton HV, Sun H, Shokrabadi M (2018) A machine learning framework for assessing post-earthquake structural safety. Struct Saf 72:1
Mangalathu S, Sun H, Nweke CC, Yi Z, Burton HV (2020) Classifying earthquake damage to buildings using machine learning. Earthq Spectra 36(1):183
Siam A, Ezzeldin M, El-Dakhakhni W (2019) Machine learning algorithms for structural performance classifications and predictions: application to reinforced masonry shear walls. Structures 22:252–265
Huang H, Burton HV (2019) Classification of in-plane failure modes for reinforced concrete frames with infills using machine learning. J Build Eng 25:100767
Mangalathu S, Js Jeon (2018) Classification of failure mode and prediction of shear strength for reinforced concrete beam-column joints using machine learning techniques. Eng Struct 160:85
Mangalathu S, Jang H, Hwang SH, Jeon JS (2020) Data-driven machine-learning-based seismic failure mode identification of reinforced concrete shear walls. Eng Struct 208:110331
Molnar C (2019) Interpretable machine learning. Lulu. com, United States
Ribeiro MT, Singh S, Guestrin C (2016) Why should i trust you?: explaining the predictions of any classifier, CoRR abs/1602.04938. http://arxiv.org/abs/1602.04938
Barredo Arrieta A, Díaz-Rodríguez N, Del Ser J, Bennetot A, Tabik S, Barbado A, Garcia S, Gil-Lopez S, Molina D, Benjamins R, Chatila R, Herrera F (2020) Explainable explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inf Fusion 58(2019):82. https://doi.org/10.1016/j.inffus.2019.12.012
Lipton ZC (2018) The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery. Queue 16(3):31–57. https://doi.org/10.1145/3236386.3241340
Robnik-Šikonja M, Kononenko I (2008) Explaining classifications for individual instances. IEEE Trans Knowl Data Eng 20(5):589. https://doi.org/10.1109/TKDE.2007.190734
Quinlan J (1987) Simplifying decision trees. Int J Man-Mach Stud 27(3):221. https://doi.org/10.1016/S0020-7373(87)80053-6
Zhang R, Nie F, Li X, Wei X (2019) Feature selection with multi-view data: a survey. Inf Fusion 50:158. https://doi.org/10.1016/j.inffus.2018.11.019
Bastian CD, Rabitz H (2018) High dimensional model representation as a glass box in supervised machine learning, arXiv preprint arXiv:1807.10320
Pena FC, Guerra-Gomez J (2018) Opening the black-box: towards more interactive and interpretable machine learning, In Proceedings of the Machine Learning from User Interaction for Visualization and Analytics Workshop at IEEE VIS
Gilpin LH, Bau D, Yuan BZ, Bajwa A, Specter M, Kagal L (2018) In 2018 IEEE 5th international conference on data science and advanced analytics (DSAA), pp 80–89
Murdoch WJ, Singh C, Kumbier K, Abbasi-Asl R, Yu B (2019) Interpretable machine learning: definitions, methods, and applications. arXiv preprint arXiv:1901.04592
Schmidt J, Marques MR, Botti S, Marques MA (2019) Recent advances and applications of machine learning in solid-state materials science. Npj Comput Mater. https://doi.org/10.1038/s41524-019-0221-0
Gulec CK, Whittaker AS (2011) Empirical equations for peak shear strength of low aspect ratio reinforced concrete walls. ACI Struct J 108(1):80–89
NEES. Nees: Shear wall database (2017). https://datacenterhub.org/resources/260. Accessed: 2020-04-11
Massone L, Wallace J (2006) Rc wall shear–flexure interaction: analytical and experimental responses. Ph.D. thesis, PhD Dissertation
Salonikios TN (2007) Analytical prediction of the inelastic response of RC walls with low aspect ratio. J Struct Eng 133(6):844
Deger ZT, Basdogan C (2019) Empirical expressions for deformation capacity of reinforced concrete structural walls. ACI Struct J 116(6):53
Izenman AJ (2008) Modern multivariate statistical techniques regression, classification, and manifold learning (Springer Texts in Statistics)
Cohen J (1960) A coefficient of agreement for nominal scales. Edu Psychol Meas 20(1):37. https://doi.org/10.1177/001316446002000104
Cover T, Hart P (2006) Nearest neighbor pattern classification. IEEE Trans Inf Theor 13(1):21
Breiman L (2001) Random forests. Mach Learn 45(1):5
Yoshikawa Y, Iwata T (2021) Gaussian process regression with interpretable sample-wise feature weights. IEEE Trans Neural Netw Learn Syst
Linardatos P, Papastefanopoulos V, Kotsiantis S (2021) Explainable ai: a review of machine learning interpretability methods. Entropy 23(1):18
Duda R, Hart P, Stork D (2001) Pattern classification, 2nd edn. Wiley, New York
Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene selection for cancer classification using support vector machines. Mach Learn 46(1–3):389
Acknowledgements
The project has been supported by funds from the Scientific and Technological Research Council of Turkey (TUBITAK) under Project No: 218M535. Opinions, findings, and conclusions in this paper are those of the authors and do not necessarily represent those of the funding agency.
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Deger, Z.T., Taskin Kaya, G. Glass-box model representation of seismic failure mode prediction for conventional reinforced concrete shear walls. Neural Comput & Applic 34, 13029–13041 (2022). https://doi.org/10.1007/s00521-022-07159-8
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DOI: https://doi.org/10.1007/s00521-022-07159-8