Non-linear modeling parameters for new construction RC columns

Modeling parameters (MP) of reinforced concrete columns are a critical component of performance-based seismic assessment methodologies because in these approaches damage is estimated based on element deformations calculated using non-linear models. To ensure model fidelity and consistency of assessment results, performance-based seismic assessment methods in ASCE 41, ACI 369.1, and ACI 374.3R prescribe modeling parameters calibrated using experimental data. This paper introduces a new set of equations to calculate reinforced concrete column non-linear modeling parameters optimized for design verification of new buildings using response history analysis. Unlike modeling parameters provided in ACI 369.1 and ASCE 41, intended for columns of older non-ductile buildings, the equations for modeling parameters anl and bnl presented in this study were calibrated to simulate the load-deformation envelope of reinforced concrete columns that meet the detailing requirements of modern seismic design codes. Specifically, the proposed equations are intended for use with provisions in ACI 374.3R, Chapter 18 and Appendix A of ACI 318-19 and Chapter 16 of ASCE/SEI 7-16. The proposed equations were calibrated using the ACI Committee 369 column database, which includes column configuration parameters, material properties, and deformation capacity modeling parameters inferred from the measured response of columns under load reversals. Dimension reduction techniques were applied to visualize different clusters of data in 2D space using the negative log-likelihood score. This technique allowed decreasing the non-linearity of the problem by identifying a subset of columns with load-deformation behavior representative of new construction conforming to current codes requirements. A Neural Network model (NN) was calibrated and used to perform parametric variations to identify the most relevant input parameters and characterize their effect on modeling parameters, and to stablish the degree of non-linearity between each input variable and the model output. Developing equations for modeling parameters applicable to a wide range of columns is challenging, so this research considered subsets of the database representative of new construction columns to calibrate simple practical equations. Linear regression models including the most relevant features from the parametric study were calibrated for rectangular and circular columns. The proposed linear regression equations were found to provide better estimates of new construction column modeling parameters than the available tables in ACI 374.3R and ASCE 41-13, and the equations ASCE 41-17

Machine learning tools to improve nonlinear modeling parameters of RC columns

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

Digital Filter Design for Force Signals from Eulerian–Lagrangian Analyses of Wave Impact on Bridges

Finite element (FE) models that simulate wave–superstructure interactions with the coupled Eulerian–Lagrangian (CEL) technique provide a viable and economical solution to estimate wave impact forces on bridge superstructures. One of the main drawbacks of CEL FE models is that they produce solutions distorted by numerical artifacts with very high frequencies that make it difficult to quantify the magnitude of hydrodynamic forces on superstructures. This paper investigated digital filter parameters for horizontal forces extracted from CEL FE models. The optimal filter configuration was evaluated by comparing unfiltered and filtered horizontal force signals with experimentally measured values from a reduced-scale superstructure specimen tested at the O.H. Hinsdale Wave Research Laboratory at Oregon State University. It was found that digital filters with cutoff frequencies below the fundamental frequency of the superstructure produced the best results and that optimizing Eulerian–Lagrangian surface interactions significantly improved the quality of the calculated force signals