RAPID SHEAR CAPACITY PREDICTION OF TRM-STRENGTHENED UNREINFORCED MASONRY WALLS - Interpretable Machine Learning Using a Web App

Aug 4, 2026

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The study develops an efficient, reliable tool for rapid estimation of the shear capacity of a TRM-strengthened unreinforced masonry wall. A data-driven machine learning workflow is built from 113 bibliographic experimental cases with 11 input variables, where outliers are removed using Cook’s distance. Seventeen machine learning models are trained, hyperparameter-tuned, and evaluated, then combined via a voting ensemble. The blended model reaches R2=0.95 and MAPE=8.03%. Interpretation methods identify Am, ft, and n·tf as key predictors, mainly positively affecting shear capacity, and the final system is deployed through a web app.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages25

Reading Time1 h 3 min

AccessFree to download

Abstract

Introduction

Machine learning applications in civil engineering

Motivation and research gap

arXiv :2406 . 16889v1 [ ee ss . SP] 30 Apr 2024 RAPID SHEAR CAPACITY PREDICTION OF TRM-STRENGTHENED UNREINFORCED MASONRY WALLS THROUGH INTERPRETABLE MACHINE LEARNING USING A WEB APP Petros C. Lazaridis, Athanasia K. Thomoglou Department of Civil Engineering Democritus University of Thrace Xanthi, Greece {plazarid, [athomogl}@civil.duth.gr](athomogl}@civil.duth.gr) ABSTRACT The presented study aims to provide an efficient and reliable tool for rapid estimation of the shear capacity of a TRM-strengthened masonry wall. For this purpose, a data-driven methodology based on a machine learning system is proposed using a dataset constituted of experimental results selected from the bibliography. The outlier points were detected using Cook’s distance methodology and removed from the raw dataset, which consisted of 113 examples and 11 input variables. In the processed dataset, 17 Machine Learning methods were trained, optimized through hyperparameter tuning, and compared on the test set. The most effective models are combined into a voting model to leverage the predictive capacity of more than a single regressor. The final blended model shows remarkable predicting capacity with the determination factor (R2 )equal to 0 .95 and the mean absolute percentage error equal to 8.03% . In sequence, machine learning interpretation methods are applied to find how the predictors influence the target output. Am , ft , and n · tf were identified as the most significant predictors with a mainly positive influence on the shear capacity. Finally, the built ML system is employed in a user-friendly web app for easy access and usage by professionals and researchers. Keywords Shear Capacity · TRM · Machine Learning · Neural Networks · URM 1 Introduction The machine learning (ML) technique is known worldwide as a method to evaluate and predict the mechanical performance of civil engineering structures [1] . Neural networks (NN) have been successfully used to approximate the failure surface of such brittle anisotropic materials. Previous studies have demonstrated that advanced metamodels, which employ full-field strain maps and loading scenarios, can accurately predict the mechanical properties of masonry elements. This approach can effectively reduce the need for expensive finite element simulations of masonry and can also support experimental testing in identifying masonry material properties both in situ and in the laboratory [2, 3] . Other mechanical properties, such as the compressive strength of building elements, blast loading or detection of cracks, have been investigated with ML [4, 5] . In recent times, deep learning techniques have been employed to detect crack regions of historical masonry structures more precisely and sensitively using remote sensing technologies, with segmentation and object detection methods [6] . Machine learning (ML) algorithms and artificial neural networks (ANNs) have been developed to predict various properties of construction materials. For example, they can predict the shear strength of rectangular hollow reinforced concrete (RC) columns, the flexural response of steel-fiber-reinforced concrete (SFRC) beams, and the bond strength between masonry substrates and textile-reinforced mortar (TRM) strengthening system. Recent studies have reported that they can analyze the slump and uncover the internal dependencies within fiber-reinforced rubberized recycled aggregate concrete [7, 8, 9, 10, 11] . In brief, the applications in question underwent a review process and were subsequently classified into four distinct categories, namely: structural response and performance prediction, experimental data interpretation, image and text retrieval, and pattern recognition in structural health monitoring data [12, 13, 14] . The accurate categorization of damages sustained by a building is a time-intensive process, with the premature analysis of the damage rate being crucial for mitigating the risks of potential ac

FAQs

How is the dataset for shear capacity prediction constructed and cleaned?

A dataset is built from 113 experimental results selected from the bibliography with 11 input variables. Outliers are detected using Cook’s distance and removed before model training.

Which machine learning approach achieves the best predictive performance?

Seventeen machine learning methods are trained and optimized via hyperparameter tuning. The most effective models are combined into a voting ensemble, yielding R2=0.95 and MAPE=8.03%.

How do the authors make the model interpretable?

After training, machine learning interpretation methods are applied to determine how predictors influence shear capacity. The most significant predictors identified are Am, ft, and n·tf, with a mainly positive influence.

Aug 4, 2026

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