Estimating the drift capacity (DC) of reinforced concrete (RC) structural walls is crucial to assess seismic performance of buildings in earthquake-prone regions. Numerous investigations have been conducted on the subject. Nevertheless, most of these investigations focus on walls without prior damage and a single expected earthquake. Recent tests conducted on RC walls suggest that displacement history does not affect DC, if the maximum amplitude of previous cycles is smaller than a threshold, regardless of the number of applied cycles. How to define such threshold and how prior cycles with amplitudes exceeding it affect DC remains unclear. This study assessed the effect of cyclic displacement history—or displacement cycles—on the DC of RC structural walls, using a statistical analysis. Results indicate that DC decreases as the intensity of cyclic displacement history increases. Nevertheless, this decrease is considerably smaller than differences between measured values of DC and estimates obtained using available empirical formulations and a calibrated machine learning algorithm.
AbdullahSAWallaceJW (2019) Drift capacity of reinforced concrete structural walls with special boundary elements. ACI Structural Journal116(1): 183–194. Available at: https://doi.org/10.14359/51710864
2.
AbdullahSAWallaceJW (2021) Drift capacity at axial failure of RC structural walls and wall piers. Journal of Structural Engineering147(6). American Society of Civil Engineers (ASCE). Available at: https://doi.org/10.1061/(ASCE)ST.1943-541X.0003009
3.
ACI 318-19 (2019) Building code requirements for structural concrete. (ACI 318-19) and commentary (ACI 318R-19). American Concrete Institute (ACI).
4.
AladsaniMABurtonHAbdullahSAWallaceJW (2022) Explainable machine learning model for predicting drift capacity of reinforced concrete walls. ACI Structural Journal119(3): 191–204. Available at: https://doi.org/10.14359/51734484
5.
BakerALL (1956) The ultimate-load theory applied to the design of reinforced & prestressed concrete frames (ed Concrete Publications).
BrownRHJirsaJO (1971) Reinforced concrete beams under load reversals. ACI Structural Journal68(5): 380–390. Available at: https://doi.org/10.14359/11338
10.
BurnsNH (1962) Load-deformation characteristics of beam-column connections in reinforced concrete. Dissertation. University of Illinois, Urbana, IL.
11.
CalviGMPriestleyMJNKowalskyMJ (2007) Displacement-Based Seismic Design of Structures. Rome: IUSS Press.
12.
CecenH (1979) Response of Ten story, reinforced concrete model frames to simulated earthquakes. Dissertation, University of Illinois, Urbana-champaign.
13.
Chacón-ValeroEHubeMASanta MaríaH (2025) Seismic performance of damaged slender reinforced concrete walls with unconfined boundaries. Journal of Building Engineering101: 111819. Available at: https://doi.org/10.1016/j.jobe.2025.111819
14.
ChenXLFuJPYaoJLGanJF (2018) Prediction of shear strength for squat RC walls using a hybrid ANN–PSO model. Engineering with Computers34(2): 367–383. Available at: https://doi.org/10.1007/s00366-017-0547-5
15.
ColmenaresJSanta MaríaHHubeMA (2025) Experimental assessment of residual seismic capacity of unconfined slender reinforced concrete walls after constant drift loading protocols. Journal of Building Engineering111: 113351. Available at: https://doi.org/10.1016/j.jobe.2025.113351
16.
DegerZTTaskinGWallaceJW (2024) No more black-boxes: estimate deformation capacity of non-ductile RC shear walls based on generalized additive models. Bulletin of Earthquake Engineering. Available at: https://doi.org/10.1007/s10518-024-01968-z
17.
FEMA 306 (1998) Evaluation of Earthquake Damaged Concrete and Masonry Wall Buildings. Basic Procedures Manual. Applied Technology Council.
18.
FengDCWangWJMangalathuSTacirogluE (2021) Interpretable XGBoost-SHAP machine-learning model for shear strength prediction of squat RC walls. Journal of Structural Engineering147(11): 04021173. Available at: https://doi.org/10.1061/(ASCE)ST.1943-541X.0003115
19.
HoultR (2022) Universal plastic hinge length for reinforced concrete walls. ACI Structural Journal119(4): 75–83. Available at: https://doi.org/10.14359/51734650
JungA (2022) Machine Learning: The Basics. Machine Learning: Foundations, Methodologies, and Applications. Singapore: Springer. Available at: https://doi.org/10.1007/978-981-16-8193-6
22.
KohaviR (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence. Morgan Kaufmann Publishers, pp. 1137–1143.
23.
LybasJMSozenMA (1977) Effect of Beam Strength and Stiffness on Dynamic Behavior of Reinforced Concrete Coupled Walls. Urbana-champaign.
24.
MalhotraPK (2002) Cyclic-demand spectrum. Earthquake Engineering and Structural Dynamics31(7): 1441–1457. Available at: https://doi.org/10.1002/eqe.171
25.
MonicalJPujolS (2024) A study of the response of reinforced concrete frames with and without masonry infill walls to earthquake motions. Structures63: 106345. Available at: https://doi.org/10.1016/j.istruc.2024.106345
26.
MoscosoJFHubeMASanta MaríaHS (2021) Residual seismic capacity of reinforced concrete walls with unconfined boundaries. ACI Structural Journal118(5): 205–220. Available at: https://doi.org/10.14359/51732830
27.
NelsonJM (2000) Damage model calibration for reinforced concrete bridge columns. Master of Science Dissertation. University of Washington, Seattle, WA.
28.
NguyenDDTranVLHaDHNguyenV-Q (2021) A machine learning-based formulation for predicting shear capacity of squat flanged RC walls. Structures29: 1734–1747. Available at: https://doi.org/10.1007/s10518-024-01944-7
PerušIBiskinisDFajfarPFardisMNGrammatikouSLignosDG (2013) The SERIES Database of RC elements. In: Proceedings of the 2Nd European Conference on Earthquake Engineering and Seismology, Istanbul.
31.
PollalisWPujolS (2020) Development and Splice Lengths for High-Strength Reinforcement Volume II: Drift Capacity of Structural Walls with Lap Splices.
32.
PollalisWKerbyCPujolS (2024) On estimating the drift capacity of reinforced concrete walls with lap splices at their bases. Bulletin of Earthquake Engineering. Available at: https://doi.org/10.1007/s10518-024-01944-7
33.
PriestleyMJNKowalskyMJ (1998) Aspects of drift and ductility capacity of rectangular cantilever structural walls. Bulletin of the New Zealand Society for Earthquake Engineering31(2): 73–85. Available at: https://doi.org/10.5459/bnzsee.31.2.73-85
34.
PujolSSozenMARamirezJA (2006) Displacement history effects on drift capacity of reinforced concrete columns. ACI Structural Journal103(2): 253–262. Available at: https://doi.org/10.14359/15183
35.
PuranamAWangYPujolS (2018) Estimating drift capacity of reinforced concrete structural walls. ACI Structural Journal115(6): 1563–1574. Available at: https://doi.org/10.14359/51702444
36.
RoyHEHSozenMA (1965) Ductility of concrete. ACI Symposium Publication12: 213–235.
TakahashiSYoshidaKIchinoseTSanadaYMatsumotoKSuwadaH (2013) Flexural drift capacity of reinforced concrete wall with limited confinement. ACI Structural Journal110(1): 95–104. Available at: https://doi.org/10.14359/51684333
44.
PanagiotakosTBFardisMN (2001) Deformations of reinforced concrete members at yielding and ultimate. ACI Structural Journal98(2): 135–148. Available at: https://doi.org/10.14359/10181
45.
UstaMAlhmoodACarrilloJ (2019) Shear Strength of Structural Walls Subjected to Load Cycles. Concrete International41(5): 42–48.
46.
WangYPujolS (2020) Effects of longitudinal reinforcement discontinuities on the seismic response of structural walls. Bull Earthquake Eng18: 6735–6760. Available at: https://doi.org/10.1007/s10518-020-00963-4
47.
WightJKSozenMA (1975) Strength decay of RC columns under shear reversals. Journal of the Structural Division101(5): 1053–1065.
ZhangHChengXLiYDuX (2022) Prediction of failure modes, strength, and deformation capacity of RC shear walls through machine learning. Journal of Building Engineering50: 104145. Available at: https://doi.org/10.1016/j.jobe.2022.104145
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
