Crack-Based Estimation of Seismic Damage Level in Confined Masonry Walls in the Lima Metropolitan Area Using Deep Learning Techniques

Diaz, Miguel; Lopez, Luis; Amancio, Michel; Inocente, Italo; Salinas, Jhianpiere; Isuhuaylas, Sergio; Flores, Erika; Moscoso, Edisson

doi:10.3390/app15115875

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Open AccessArticle

Crack-Based Estimation of Seismic Damage Level in Confined Masonry Walls in the Lima Metropolitan Area Using Deep Learning Techniques

by

Miguel Diaz

^1,*

,

Luis Lopez

¹,

Michel Amancio

¹,

Italo Inocente

²

,

Jhianpiere Salinas

¹,

Sergio Isuhuaylas

¹,

Erika Flores

¹ and

Edisson Moscoso

¹

Centro Peruano Japones de Investigaciones Sísmicas y Mitigación de Desastres, Facultad de Ingeniería Civil, Universidad Nacional de Ingeniería, Av. Tupac Amaru 1150, Lima 15333, Peru

²

Graduate School of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba 263-8522, Japan

^*

Author to whom correspondence should be addressed.

Appl. Sci. 2025, 15(11), 5875; https://doi.org/10.3390/app15115875

Submission received: 29 March 2025 / Revised: 29 April 2025 / Accepted: 12 May 2025 / Published: 23 May 2025

(This article belongs to the Section Civil Engineering)

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Abstract

Damage assessment methods fall into contact and non-contact approaches. Contact methods, like physical measurements, material sampling, and ultrasonic testing, provide detailed data but are time-consuming and require specialized equipment. In contrast, non-contact methods assess damage remotely, allowing for faster, safer, and large-scale evaluations, especially useful in post-disaster scenarios. However, there are currently no standardized non-contact methods for assessing damage levels in confined masonry walls after damaging seismic events in Peru. On the other hand, an experimental database of cyclic loading tests on confined masonry walls is available, supporting numerical simulations with calibrated mathematical models to estimate damage levels. This research extends the application of this database by analyzing the crack pattern imagery from the tested walls and correlating it with the lateral deformation (drift) to identify the damage levels. A high-accuracy crack measurement technique was developed, combining a convolutional neural network to generate a binary crack mask and a binary search algorithm to extract polylines and convert them into length measurements, achieving a detection accuracy of 78%. The measured crack patterns were normalized into an index, which was then correlated with the amplitude of the lateral deformation in each hysteretic loop. Finally, a relationship was established between drift and the damage level index. These findings contribute to the development of a rapid, non-contact damage assessment method for confined masonry walls in seismic-prone regions.

Keywords: confined masonry walls; crack pattern identification; damage level index

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MDPI and ACS Style

Diaz, M.; Lopez, L.; Amancio, M.; Inocente, I.; Salinas, J.; Isuhuaylas, S.; Flores, E.; Moscoso, E. Crack-Based Estimation of Seismic Damage Level in Confined Masonry Walls in the Lima Metropolitan Area Using Deep Learning Techniques. Appl. Sci. 2025, 15, 5875. https://doi.org/10.3390/app15115875

AMA Style

Diaz M, Lopez L, Amancio M, Inocente I, Salinas J, Isuhuaylas S, Flores E, Moscoso E. Crack-Based Estimation of Seismic Damage Level in Confined Masonry Walls in the Lima Metropolitan Area Using Deep Learning Techniques. Applied Sciences. 2025; 15(11):5875. https://doi.org/10.3390/app15115875

Chicago/Turabian Style

Diaz, Miguel, Luis Lopez, Michel Amancio, Italo Inocente, Jhianpiere Salinas, Sergio Isuhuaylas, Erika Flores, and Edisson Moscoso. 2025. "Crack-Based Estimation of Seismic Damage Level in Confined Masonry Walls in the Lima Metropolitan Area Using Deep Learning Techniques" Applied Sciences 15, no. 11: 5875. https://doi.org/10.3390/app15115875

APA Style

Diaz, M., Lopez, L., Amancio, M., Inocente, I., Salinas, J., Isuhuaylas, S., Flores, E., & Moscoso, E. (2025). Crack-Based Estimation of Seismic Damage Level in Confined Masonry Walls in the Lima Metropolitan Area Using Deep Learning Techniques. Applied Sciences, 15(11), 5875. https://doi.org/10.3390/app15115875

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Crack-Based Estimation of Seismic Damage Level in Confined Masonry Walls in the Lima Metropolitan Area Using Deep Learning Techniques

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