Machine Learning Tools to Improve Nonlinear Modeling Parameters of RC Columns

Aug 3, 2026

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Modeling parameters are critical for accurate nonlinear simulations of concrete structures during earthquake ground motions, particularly when collapse must be captured. This study targets two barriers in seismic evaluation standards: determining the most likely failure mode of structural components and applying data-fitting methods that capture relationships between inputs and nonlinear model outputs. Using Scikit-learn and Pytorch, calibrated equations and black-box models estimate nonlinear modeling parameters a and b for reinforced-concrete columns under ASCE 41 and ACI 369.1, and predict likely failure modes.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages40

Reading Time1 h 41 min

AccessFree to download

Abstract

Introduction

Seismic standards and nonlinear modeling parameters

Failure mode identification and modeling-parameter challenges

Motivation for machine learning in codes and standards

Machine learning tools to improve nonlinear modeling parameters of RC columns Hamid Khodadadi Koodiania, Elahe Jafaria, Arsalan Majlesia, Mohammad Shahinb, Adolfo Matamorosa, Adel Alaeddinib a Civil Engineering Department, The University of Texas at San Antonio, San Antonio, USA b Mechanical Engineering Department, The University of Texas at San Antonio, San Antonio, USA Abstract Modeling parameters are essential to the fidelity of nonlinear models of concrete structures subjected to earthquake ground motions, especially when simulating seismic events strong enough to cause collapse. This paper addresses two of the most significant barriers to improving nonlinear modeling provisions in seismic evaluation standards using experimental data sets: identifying the most likely mode of failure of structural components, and implementing data fitting techniques capable of recognizing interdependencies between input parameters and nonlinear relationships between input parameters and model outputs. Machine learning tools in the Scikit-learn and Pytorch libraries were used to calibrate equations and black-box numerical models for nonlinear modeling parameters (MP) a and b of reinforced concrete columns defined in the ASCE 41 and ACI 369.1 standards, and to estimate their most likely mode of failure. It was found that machine learning regression models and machine learning black-boxes were more accurate than current provisions in the ACI 369.1/ASCE 41 Standards. Among the regression models, Regularized Linear Regression was the most accurate for estimating MP a, and Polynomial Regression was the most accurate for estimating MP b. The two black-box models evaluated, namely the Gaussian Process Regression and the Neural Network (NN), provided the most accurate estimates of MPs a and b. The NN model was the most accurate machine learning tool of all evaluated. A multi-class classification tool from the Scikit-learn machine learning library correctly identified column mode of failure with 79% accuracy for rectangular columns and with 81% accuracy for circular columns, a substantial improvement over the classification rules in ASCE 41-13. Keywords: ACI 369.1, ASCE 41, Classification, Concrete Columns, Machine Learning, Modes of Failure, Modeling Parameters, Nonlinear Response. Introduction ASCE 41 [1] is a standard for seismic evaluation of building structures widely used in the US. Chapter 10 of ASCE 41 provides guidelines for creating nonlinear models of reinforced concrete structures that replicate the ACI 369.1 [2] Standard with minor changes. Nonlinear analysis procedures in the ACI 369.1/ASCE 41 Standards rely on lateral force versus lateral deformation envelopes, shown in Figure 1, to simulate nonlinear element behavior. Element modeling parameters (MPs) and acceptance criteria (AC) provided in ACI 369.1/ASCE 41 are crucial components of nonlinear evaluation procedures as they provide an objective basis to construct numerical models and evaluate structural performance. Two critical parameters that define the shape of load-deformation envelopes are nonlinear modeling parameters 'a' and 'b', defined as the plastic deformation at incipient lateral-strength degradation (loss of lateral load capacity) and at incipient axial degradation (loss of the ability to carry axial load in columns), respectively. These two parameters are shown in figure 1. Parameter c in Figure 1 corresponds to the residual lateral strength and is outside the scope of this study. Defining modeling parameters for a broad range of elements is challenging because physical behavior is driven by different sets of input variables relevant to the controlling mode for each particular element. Evaluation standards like ACI 369.1/ASCE 41 approach this problem by specifying rules for failure mode classification for each element type (i.e. beams, columns, walls), or by adopting equations that provide the most accurate modeling parameter estimates for all e

FAQs

Which barriers does the study address for improving nonlinear modeling provisions in seismic standards?

It addresses failure-mode identification for structural components and data-fitting techniques that can capture dependencies between input parameters and nonlinear relationships to model outputs.

How are nonlinear modeling parameters a and b defined in the study?

Parameter a represents plastic deformation at the onset of lateral-strength degradation, while parameter b represents deformation at the onset of axial degradation (loss of axial load-carrying ability) in RC columns.

Which machine learning approaches performed best in estimating modeling parameters and failure modes?

Regularized Linear Regression was most accurate for estimating MP a, Polynomial Regression for MP b, and the Neural Network black-box gave the most accurate estimates overall. A multi-class Scikit-learn classifier identified column failure modes with about 79% accuracy for rectangular columns and 81% for circular columns.

Aug 3, 2026

Upload by Logic