PSI - Issue 44

Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2022) 000–000 il l li t . i ir t. tr t r l I t rit r i ( )

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ScienceDirect

Procedia Structural Integrity 44 (2023) 1736–1743

XIX ANIDIS Conference, Seismic Engineering in Italy Machine Learning-based Seismic Fragility Curves for RC Bridge Piers Xuguang Wang a,b , Cristoforo Demartino a,b,* , Giorgio Monti c , Giuseppe Quaranta d , Alessandra Fiore e i i i i i l Xuguang Wang a,b , Cristoforo Demartino a, , , i , G , e

a Zhejiang University - University of Illinois at Urbana Champaign Institute, Haining 314400, Zhejiang, PR China b Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA c Department of Structural Engineering and Geotechnics, Sapienza University of Rome, via A. Gramsci 53, 00197 Rome, Italy d Department of Structural Engineering and Geotechnics, Sapienza University of Rome, via Eudossiana 18, 00184 Rome, Italy e Department of Civil Engineering Sciences and Architecture, Polytechnic University of Bari, via Giovanni Amendola 126/B, Bari, Italy a ji i rsit - i rsit f Illi is t r i I stit t , i i , ji , i b rt t f i il ir t l i ri , i rsit f Illi is t r - i , r , I , c rt t f tr t r l i ri t i s, i z i rsit f , i . r s i , , It l d rt t f tr t r l i ri g and Geotechnics, Sapienza University of Rome, via Eudossiana 18, 00184 Rome, Italy e r it t r , l t i i rsit f ri, i i i l / , ri, It l rt t f i il i ri i s

© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the XIX ANIDIS Conference, Seismic Engineering in Italy. Abstract A significant part of ongoing studies in the field of earthquake engineering is directed toward the seismic risk assessment of buildings and infrastructures at a territorial scale. This task is usually accomplished by grouping the structures into homogenous classes in terms of typology, for which seismic fragility curves are then obtained for different limit states via numerical simulations or from the statistical analysis of observational data when available. Particularly, the development of typological fragility curves for bridges under earthquake is useful for assessing the reliability and resilience of transportation networks in seismic areas and can be also effective decision-making support. Within this framework, the proposed study establishes a machine learning-based paradigm for the closed-form prediction of the main statistical parameters required to obtain relevant seismic fragility curves for reinforced concrete bridge piers. Initially, a huge training dataset has been obtained by Monte Carlo simulations and displacement based bridge pier assessments by assuming data representative of the Italian highway transportation network. Next, symbolic nonlinear regression formulae for estimating the main statistical parameters of seismic fragility curves have been generated. With the aid of those formulae, the effort of calculating the seismic fragility curves is greatly reduced since the corresponding main statistical parameters can be directly calculated from a set of commonly available attributes. Therefore, the proposed study provides a helpful tool for the rapid preliminary assessment of damage and risk level of existing highway transportation networks exposed to seismic hazards. © 2022 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the XIX ANIDIS Conference, Seismic Engineering in Italy Keywords: Bridge Piers; Fragility Curve; Interpretable Data-driven Models; Seismic Assessment. t t i ifi t rt f i t i i t fi l f rt i ri i ir t t r t i i ri t f il i i fr tr t r t t rrit ri l l . i t i ll li r i t tr t r i t l i t r f t l , f r i i i fr ilit r r t t i f r iff r t li it t t i ri l i l ti r fr t t ti ti l l i f r ti l t il l . rti l rl , t l t of typological fragility c r f r ri r rt i f l f r i t r li ilit r ili f tr rt ti t r i i i r l ff ti i i - i rt. it i t i fr r , t r t t li i l r i - r i f r t l -f r r i ti f t i t ti ti l r t r r ir t t i r l t i i fr ilit r f r r i f r r t ri i r . I iti ll , tr i i t t t i t rl i l ti i l t- ri i r t i t r r t ti f t It li i tr rt ti t r . t, lic li r r r i f r l f r ti ti t i t ti ti l r t r f i i fr ilit r r t . it t i f t f r l , t ff rt f l l ti t i i fr ilit r i r tl r i t rr i i t ti ti l r t r ir tl l l t fr t f l il l ttri t . r f r , t r t r i l f l t l f r t r i r li i r t f ri l l f i ti i tr rt ti t r to seismic hazards. t r . li I . . Thi i rti l r t - - li ( tt :// r ti . r /li / - - / . ) Peer-review under responsibility of the scientific committ f t I I I f r , i i i ri i It l i rs; r ilit r ; I t r r t l t - ri ls; is i ss ss t. r s: ri

2452-3216 © 2022 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the XIX ANIDIS Conference, Seismic Engineering in Italy - t r . li I . . i i rti l r t - - li ( tt :// r ti . r /li / - - / . ) r r s si ilit f t s i tifi itt f t I I I f r , is i i ri i It l r-r i

2452-3216 © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the XIX ANIDIS Conference, Seismic Engineering in Italy. 10.1016/j.prostr.2023.01.222