Machine learning tools to improve nonlinear modeling parameters of RC columns

• URL: http://arxiv.org/abs/2303.16140v1
• Date: Thu, 9 Mar 2023 01:00:38 GMT
• Title: Machine learning tools to improve nonlinear modeling parameters of RC columns
• Abstract summary: This paper addresses two of the most significant barriers to improving nonlinear modeling provisions in seismic evaluation standards. It implements data fitting techniques capable of recognizing interdependencies between input parameters and nonlinear relationships between input parameters and model outputs. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• A Hybrid PEM-GP Framework for Uncertainty-Aware System Identification of Quadcopters [0.0]
  We propose a framework that combines physics-based modeling with data-driven learning, while explicitly accounting for uncertainty.<n>The proposed framework is validated on a Duckiedrone-like experimental setup.
  arXiv  Detail & Related papers  (2026-08-31T08:26:03Z)
• Goodness-of-Fit Tests and Calibration Machine-Learning Algorithms for Logistic Regression with Sparse Data [0.0]
  This thesis studies classical GOF tests for binary logistic regression under both grouped and sparse data.<n>It shows that many of these tests fail to give valid conclusions when exposed to the complexities of actual datasets.
  arXiv  Detail & Related papers  (2026-08-11T17:00:50Z)
• CapTrack: Multifaceted Evaluation of Forgetting in LLM Post-Training [48.70704477452434]
  We introduce textbfCapTrack, a capability-centric framework for analyzing forgetting in large language models.<n>We conduct a large-scale empirical study across post-training algorithms, domains, and model families.<n>We find that forgetting extends beyond parametric knowledge, with pronounced drift in robustness and default behaviors.
  arXiv  Detail & Related papers  (2026-02-19T09:46:24Z)
• ECO: Quantized Training without Full-Precision Master Weights [58.97082407934466]
  Error-Compensating (ECO) eliminates master weights by applying updates directly to quantized parameters.<n>We show that ECO converges to a constant-radius neighborhood of the optimum, while naive master-weight removal can incur an error that is inversely proportional to the learning rate.
  arXiv  Detail & Related papers  (2026-01-29T18:35:01Z)
• One Model to Critique Them All: Rewarding Agentic Tool-Use via Efficient Reasoning [54.580646706013965]
  Reward models (RMs) play a critical role in aligning large language models with human preferences.<n>We introduce ToolRM, a family of lightweight generative RMs tailored for general tool-use scenarios.<n>To build these models, we propose a novel pipeline that constructs pairwise preference data using rule-based scoring and multidimensional sampling.
  arXiv  Detail & Related papers  (2025-10-30T06:08:27Z)
• Uncertainty Quantification for Regression using Proper Scoring Rules [76.24649098854219]
  We introduce a unified UQ framework for regression based on proper scoring rules, such as CRPS, logarithmic, squared error, and quadratic scores.<n>We derive closed-form expressions for the uncertainty measures under practical parametric assumptions and show how to estimate them using ensembles of models.<n>Our broad evaluation on synthetic and real-world regression datasets provides guidance for selecting reliable UQ measures.
  arXiv  Detail & Related papers  (2025-09-30T17:52:12Z)
• Bayesian Model Parameter Learning in Linear Inverse Problems: Application in EEG Focal Source Imaging [49.1574468325115]
  Inverse problems can be described as limited-data problems in which the signal of interest cannot be observed directly.<n>We studied a linear inverse problem that included an unknown non-linear model parameter.<n>We utilized a Bayesian model-based learning approach that allowed signal recovery and subsequently estimation of the model parameter.
  arXiv  Detail & Related papers  (2025-01-07T18:14:24Z)
• Differentiable Calibration of Inexact Stochastic Simulation Models via Kernel Score Minimization [11.955062839855334]
  We propose to learn differentiable input parameters of simulation models using output-level data via kernel score minimization with gradient descent. We quantify the uncertainties of the learned input parameters using a new normality result that accounts for model inexactness.
  arXiv  Detail & Related papers  (2024-11-08T04:13:52Z)
• Investigating the Robustness of Counterfactual Learning to Rank Models: A Reproducibility Study [61.64685376882383]
  Counterfactual learning to rank (CLTR) has attracted extensive attention in the IR community for its ability to leverage massive logged user interaction data to train ranking models. This paper investigates the robustness of existing CLTR models in complex and diverse situations. We find that the DLA models and IPS-DCM show better robustness under various simulation settings than IPS-PBM and PRS with offline propensity estimation.
  arXiv  Detail & Related papers  (2024-04-04T10:54:38Z)
• Learning Sample Difficulty from Pre-trained Models for Reliable Prediction [55.77136037458667]
  We propose to utilize large-scale pre-trained models to guide downstream model training with sample difficulty-aware entropy regularization. We simultaneously improve accuracy and uncertainty calibration across challenging benchmarks.
  arXiv  Detail & Related papers  (2023-04-20T07:29:23Z)
• Learning Residual Model of Model Predictive Control via Random Forests for Autonomous Driving [13.865293598486492]
  One major issue in predictive control (MPC) for autonomous driving is the contradiction between the system model's prediction and computation. This paper reformulates the MPC tracking accuracy as a program (QP) problem optimization as a program (QP) can effectively solve it.
  arXiv  Detail & Related papers  (2023-04-10T03:32:09Z)
• Machine learning based surrogate modeling with SVD enabled training for nonlinear civil structures subject to dynamic loading [0.0]
  This paper proposes a machine learning based surrogate model framework to predict earthquakes. The framework is validated by using it to successfully predict the peak response of one-story and three-story buildings.
  arXiv  Detail & Related papers  (2022-06-12T11:57:58Z)
• ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms [58.684954492439424]
  We propose a novel framework to efficiently test a machine learning model using only a small amount of labeled test data. The idea is to estimate the metrics of interest for a model-under-test using Bayesian neural network (BNN)
  arXiv  Detail & Related papers  (2021-04-11T12:14:04Z)
• Flexible Model Aggregation for Quantile Regression [92.63075261170302]
  Quantile regression is a fundamental problem in statistical learning motivated by a need to quantify uncertainty in predictions. We investigate methods for aggregating any number of conditional quantile models. All of the models we consider in this paper can be fit using modern deep learning toolkits.
  arXiv  Detail & Related papers  (2021-02-26T23:21:16Z)
• A Hypergradient Approach to Robust Regression without Correspondence [85.49775273716503]
  We consider a variant of regression problem, where the correspondence between input and output data is not available. Most existing methods are only applicable when the sample size is small. We propose a new computational framework -- ROBOT -- for the shuffled regression problem.
  arXiv  Detail & Related papers  (2020-11-30T21:47:38Z)
• Reinforcement Learning based dynamic weighing of Ensemble Models for Time Series Forecasting [0.8399688944263843]
  It is known that if models selected for data modelling are distinct (linear/non-linear, static/dynamic) and independent (minimally correlated) models, the accuracy of the predictions is improved. Various approaches suggested in the literature to weigh the ensemble models use a static set of weights. To address this issue, a Reinforcement Learning (RL) approach to dynamically assign and update weights of each of the models at different time instants.
  arXiv  Detail & Related papers  (2020-08-20T10:40:42Z)
• Machine learning for causal inference: on the use of cross-fit estimators [77.34726150561087]
  Doubly-robust cross-fit estimators have been proposed to yield better statistical properties. We conducted a simulation study to assess the performance of several estimators for the average causal effect (ACE) When used with machine learning, the doubly-robust cross-fit estimators substantially outperformed all of the other estimators in terms of bias, variance, and confidence interval coverage.
  arXiv  Detail & Related papers  (2020-04-21T23:09:55Z)
• Online Tensor-Based Learning for Multi-Way Data [1.0953917735844645]
  A new efficient tensor-based feature extraction, named NeSGD, is proposed for online $CANDECOMP/PARAFAC$ decomposition. Results show that the proposed methods significantly improved the classification error rates, were able to assimilate the changes in the positive data distribution over time, and maintained a high predictive accuracy in all case studies.
  arXiv  Detail & Related papers  (2020-03-10T02:04:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.