Optimizing the Geometric Configuration and Manufacturing Process of High Mast Illumination Poles

This work presents the development of a high-fidelity model that accounts for the cumulative effect of welding and hot- dip galvanizing on the determining the resulting residual stresses and deformations induced during the manufacturing process of high mast illumination poles (HMIPs). This model is meant to elucidate the root causes of weld toe cracks in HMIPs. A TxDOT pole-to-base plate connection detail was used as the reference model in the analysis. Welding was modeled using the plug-in Abaqus Welding Interface (AWI), which automatically implements a series of sequential thermal and mechanical analyses. Then, the welding stress results were used as initial input to the galvanizing analysis. The cumulative stress results were compared against simulations that only considered the galvanizing process. A parametric study was then conducted to quantify the variation in the residual stresses and equivalent plastic strain magnitudes induced during the welding and galvanizing of HMIPs due to changes in welding and galvanizing practices. The results revealed that the cumulative effects of the different processes involved in the manufacturing of HMIPs contribute to the formation of galvanizing cracks in HMIPs. Also, increasing the dipping submersion speed during galvanizing and lowering the torch temperature magnitude during welding results in fewer zones prone to cracking. Altering the angle of inclination effect did not have a significant impact on the results. Performing variations in the manufacturing practices used for the fabrication of HMIPs can contribute to reducing the extensive inspection procedures conducted post-galvanizing to identify cracks

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

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

Structural Vulnerability of Coastal Bridges Under Extreme Hurricane Conditions

Corresponding data set for Tran-SET Project No. 18STTSA04. Abstract of the final report is stated below for reference: This work presents the results of a numerical study evaluating the response of coastal bridges due to hurricane-induced waves. The analyses were conducted using the Coupled Eulerian-Lagrangian (CEL) approach, available on the commercial finite element software Abaqus, which allows modeling the interaction between water and the bridge. The work concentrated on (1) establishing an approach for modeling the desired wave characteristics (i.e., wave height, and frequency) within the CEL simulation, (2) conducting simulations using actual bridge dimensions of historically damaged bridges, (3) analyzing a range of foundation flexibilities to determine its effect on the uplift and shear forces acting on the superstructure, and (4) comparing results simulations to AASHTO equations that estimate wave forces acting on coastal bridges. The numerical study revolved around two major highway bridges damaged along the U.S Gulf Coast during hurricane Katrina in 2005, (a) the U.S 90 highway bridge over Biloxi Bay and (b) the US. 90 St. Louis-Bay Bridge. The water level elevation was defined at the bottom of the superstructure as post-Katrina investigations revealed that this was a common characteristic for damaged bridges. The analysis revealed that (a) bridge models with flexible foundations provide better force design estimates than models with rigid supports, (b) the force demands are presumably amplified when the natural frequency of the bridge coincides with that of the traveling waves, and (c) CEL force estimates show large peak magnitudes during wave impacts that exceed AASHTO estimates. Further research is required to determine if these peaks are numerical artifacts or a concern for connection design

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

Prediction and multi-objective optimization of workability and compressive strength of recycled self-consolidating mortar using Taguchi design method

Concrete is the most consumed material in the construction industry. Using recycled aggregates (RA) and silica fume (SF) in concrete and mortar could preserve natural aggregates (NA) and reduce CO2 emissions and construction and demolition waste (C&DW) generation. Optimizing the mixture design based on both fresh and hardened properties of recycled self-consolidating mortar (RSCM) has not been performed. In this study, multi-objective optimization of mechanical properties and workability of RSCM containing SF was performed via Taguchi Design Method (TDM) with four main variables including cement content, W/C ratio, SF content and superplasticizer content at three different levels. SF was used to decrease the environmental pollution caused by cement production as well as compensating the negative effect of RA on the mechanical properties of RSCM. The results revealed that TDM could appropriately predict the workability and compressive strength of RSCM. Also, mixture design containing W/C = 0.39, SF = 6%, cement = 750 kg/m3 and SP = 0.33% was found as the optimum mixture having the highest compressive strength and acceptable workability as well as low cost and environmental concerns

Structural Vulnerability of Coastal Bridges Under Extreme Hurricane Conditions [Supporting Dataset]

69A3551747106National Transportation Library (NTL) Curation Note: As this dataset is preserved in a repository outside U.S. DOT control, as allowed by the U.S. DOT's Public Access Plan (https://doi.org/10.21949/1503647) Section 7.4.2 Data, the NTL staff has performed NO additional curation actions on this dataset. The current level of dataset documentation is the responsibility of the dataset creator. NTL staff last accessed this dataset at its repository URL on 2022-11-11. If, in the future, you have trouble accessing this dataset at the host repository, please email [email protected] describing your problem. NTL staff will do its best to assist you at that time.This work presents the results of a numerical study evaluating the response of coastal bridges due to hurricane-induced waves. The analyses were conducted using the Coupled Eulerian-Lagrangian (CEL) approach, available on the commercial finite element software Abaqus, which allows modeling the interaction between water and the bridge. The work concentrated on (1) establishing an approach for modeling the desired wave characteristics (i.e., wave height, and frequency) within the CEL simulation, (2) conducting simulations using actual bridge dimensions of historically damaged bridges, (3) analyzing a range of foundation flexibilities to determine its effect on the uplift and shear forces acting on the superstructure, and (4) comparing results simulations to AASHTO equations that estimate wave forces acting on coastal bridges. The numerical study revolved around two major highway bridges damaged along the U.S Gulf Coast during hurricane Katrina in 2005, (a) the U.S 90 highway bridge over Biloxi Bay and (b) the US. 90 St. Louis-Bay Bridge. The water level elevation was defined at the bottom of the superstructure as post-Katrina investigations revealed that this was a common characteristic for damaged bridges. The analysis revealed that (a) bridge models with flexible foundations provide better force design estimates than models with rigid supports, (b) the force demands are presumably amplified when the natural frequency of the bridge coincides with that of the traveling waves, and (c) CEL force estimates show large peak magnitudes during wave impacts that exceed AASHTO estimates. Further research is required to determine if these peaks are numerical artifacts or a concern for connection design. The total size of the described zip file is 48.5 KB. Files with the .xlsx extension are Microsoft Excel spreadsheet files. These can be opened in Excel or open-source spreadsheet programs. Docx files are document files created in Microsoft Word. These files can be opened using Microsoft Word or with an open source text viewer such as Apache OpenOffice