Evidence Hub

Sustainable Seismic Strengthening of Historical Masonry Using Basalt Textile-Reinforced Cementitious Matrix (BTRC) Composites for Induced Seismic Actions: A Systematic Review and Numerical Validation for Peru's Ring of Fire

GALVEZ PRP

2026

Abstract

Abstract Background. Unreinforced masonry (URM) represents a dominant structural typology in Peru's architectural heritage, yet its severe seismic vulnerability remains an unresolved engineering challenge given the country's position within the Pacific Ring of Fire, where the subduction of the Nazca Plate beneath the South American Plate routinely generates destructive earthquakes (Mw > 7.0) and associated induced seismicity. Objective. This study systematically reviews and numerically validates the efficacy of Basalt Textile-Reinforced Cementitious Matrix (BTRC) composites as a reversible, sustainable, and compatible seismic strengthening solution for historical masonry structures, with direct applicability to the Peruvian context and normative framework (NTE E.030, NTE E.080). Methods. A systematic literature search was conducted across Scopus, Web of Science, and ScienceDirect databases (2010–2025) following established systematic review methodology. A total of 847 records were identified; after deduplication, title/abstract screening, and full-text eligibility assessment, 58 studies were included. Numerical validation was performed using finite element method (FEM) formulations implemented in Python (NumPy, SciPy) and benchmarked against ETABS 22 API outputs, applying vectorial mechanics principles for civil engineers. Results. BTRC composites increase lateral in-plane shear capacity by 60–180% and out-of-plane flexural strength by 45–220% relative to URM. Time-history…

How strong is the evidence?

Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.

How the five levels of evidence work. The level is assigned automatically from the abstract and can be wrong, so check the paper.

Read the paper

Related studies

Go deeper in Evidence Hub

Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.

Open this study Sign up free