Reduced-order Models for Simulating Coupled Geometric Instabilities in Steel Beam-columns Under Inelastic Cyclic Straining

Numerical models that can simulate coupled geometric instabilities in steel beam-columns are required for the seismic assessment of certain steel moment-resisting frames. Reliably simulating coupled instabilities requires that the steel material is precisely characterized under inelastic cyclic straining. Full continuum finite element (CFE) models can be used in this context, however, reduced order models are preferred for building-level simulations because of their lower computational cost. Concentrated plasticity models are a typical reduced order approach, although findings from recent full-scale experiments conducted on steel beam-columns have highlighted their limitations in simulating component behavior.

The aim of this thesis is to propose accurate reduced-order simulation models for steel beam-column components to simulate coupled buckling at a diminished computational expense compared to full CFE approaches. This goal is achieved by using component macro models that couple CFEs in critical regions with beam-column elements elsewhere. The specific objectives related to the aim of this thesis are to: (i) advance the state-of-the-art in constitutive modeling for structural steels subjected to earthquake-induced inelastic straining; (ii) develop an efficient method of coupling beam-column and CFE domains that incorporates torsion-warping; and (iii) develop general recommendations for modeling wide flange beam-column components subjected to inelastic cyclic straining using the macro model.

This thesis developed a constitutive model for mild structural steels to improve upon the initial yield behavior of a classic metal plasticity model by accounting for the discontinuous yielding phenomenon. Constitutive model calibration methodologies were developed using constrained optimization for the cases when the material's cyclic behavior is known and when it is unknown. Simulated geometric instabilities were shown to be sensitive to the constitutive model's input parameters in steel beam-columns of particular geometries subjected to multiaxis loading. Coupling methods that included torsion-warping were developed using multipoint constraint equations. Findings indicate that including torsion-warping in the coupling formulation is critical for steel beam-columns that are susceptible to coupled local and lateral-torsional instabilities. The proposed macro model is efficient for the investigated range of steel beam-columns: it reduces computational cost metrics in benchmark CFE analyses by around 50 %. It is also accurate, as the average error between benchmark CFE and macro model analyses is only 3 %.

The advances in material modeling made by this thesis can be used to reduce the uncertainty in steel component simulations. This enables analysts to better comprehend component behavior through simulations and improves simulation-assisted design. Component models that explicitly simulate geometric instabilities and material inelastic behavior at lower computational costs are important in probabilistic assessments and uncertainty quantification. They may be especially useful, for example, in benchmarking concentrated plasticity models and in the process of developing new structural systems.

Experimental investigation of the cyclic properties of welds in mild structural steels

Selimcan Ozden

Structural steels have long been used in construction applications. Their mechanical properties and behavior under seismic loading or under fatigue design are of considerable interest for researchers and engineers. Although extensive studies have been conducted on the cyclic behavior of structural steels, they mostly focus on base material responses. However, base material response can be affected by changes in microstructural properties of steels. In steel construction this situation often arises in welding different components where due to thermal loading certain regions change their crystallographic phases and consequently their mechanical properties. This master's thesis is dedicated to the investigation of the lesser known large amplitude cyclic material behavior of these regions termed Heat Affected Zones (HAZ). The behavior of welded connections is of paramount importance for structural integrity. Their behavior under extreme loading conditions can be the determining factors for the failure of joints and subsequent structural collapse. Field observations following significant earthquake events (e.g. 1994 Northrigde incident) has shown that the assumed ductility of the steel construction can be put into question when poorly executed welded structural joints suffer premature fracture. The head-boomed experience from these suggests that failures often arise from combination of ill-conceived detailing and insufficient weld quality (e.g. large defects on low thoughness). As the understanding of this problem progressed, better detailing properties and quality controls have been proposed to avoid brittle nature of joint failure. In this master’s thesis the author investigates the behavior of S355J2+N structural steel as HAZ of welded material and base material subjected to uniaxial cyclic loading and large inelastic strain demands under seismic loading in the experimental and numerical level. HAZ at welded specimens is obtained through thermal loading in the laboratory environment. Uniaxial cyclic loading tests are performed in the laboratory as well. In addition, comparisons between the base and welded metals for the steel class of S355J2+N are provided as well including material parameters for Voce-Chaboche and Updated Voce-Chaboche models. Results of this study reveal that under cyclic loading welded material hardens more than base material. In addition, plateau region exercised in base material disappears in welded material and welded specimens experience rounding around the yield stress which is not the case for base material. Accuracy of simulation of welded material and correctness of model parameters greatly depend on whether Voce-Chaboche or Updated Voce-Chaboche model is applied to the right material being investigated.

2021

Influence of geometrical imperfections and flaws at welds of steel liners on fatigue behavior of pressure tunnels and shafts in anisotropic rock

Alexandre Jean Pachoud

High-head steel lined pressure tunnels and shafts may be considered as critical infrastructures especially when rock overburden is small. In the case of lining failure, catastrophic damages can occur when a large amount of water can reach the steep valley slopes and induce dangerous mud and debris flow. In the last decades, high-strength steel has been used more frequently for such high-head pressure tunnels and shafts mainly for economic and construction reasons. Under the more and more rough operation conditions of storage hydropower plants, high-strength steels, which are generally thinner than lower grade steels, are more prone to fail by fatigue. Design guidelines for the application of high-strength steel for steel liners are still missing. With his research project, Dr Alexandre Pachoud made an important and novel contribution for the design of pressure tunnels and shafts considering the influence of anisotropic behavior of rock as well as the geometrical imperfections and flaws at welds on the fatigue resistance of the steel liners. For the first time, Dr Pachoud studied systematically the influence of rock anisotropy on the deformation of the lining system comprising steel liner, backfill concrete and near as well as far field rock mass. He performed an extensive parametric study with the finite element method (FEM) over a wide range of geometrical and material parameters. Normalized stresses and displacements were analyzed in the steel liner and the far-field rock mass and correction factors to be included in the analytical solution for isotropic rock conditions could be derived. For transversely anisotropic rock mass the analytical solution allows a simple and fast estimation of the maximum stresses in the steel liner by a correction applied to the isotropic analytical solution with a high accuracy. Based on extensive FEM simulations Dr Pachoud derived in a further step parametric correction factors, which allow estimating stress concentrations and structural stresses in steel liners considering geometrical imperfections. Dr Pachoud obtained also Stress Intensity Factors (SIF) for axial cracks in the weld material of the longitudinal joints by means of computational Linear Elastic Fracture Mechanics (LEFM) and could propose new parametric equations for the weld shape correction. For fatigue assessment, a probabilistic model for steel liner crack growth and fracture was developed by using the above mentioned new parametric equations for considering geometrical imperfections. Finally, Dr Pachoud illustrated the implementation of all the developed parametric equations in a probabilistic model for crack growth in the steel liner under dynamic loading with a case study for a high-head power plant. The probabilistic model allows determining the acceptable undetected initial crack sizes in the steel liner depending on the choice of the steel grade, which is crucial for engineering practice using high-strength steels for pressure tunnels and shafts.

EPFL-LCH2017

Numerical modeling and neural networks to identify constitutive parameters from in situ tests

Rafal Filip Obrzud

This study proposes a new advanced algorithm for determining material parameters based on in situ tests. In situ testing gives an opportunity to perform soil characterization in natural stress conditions on a representative soil mass. Most field techniques reduce soil disturbances to minimum, allowing investigating the response of virgin soil. Self-boring pressuremeter tests (SBPT) and standard piezocone tests (CPTU) are widely used to deduce properties of clayey soils through analytical and empirical correlations between soil properties and experimental measurements. Empirical correlations usually require some tuning based on reference laboratory data because first-order estimates for typical correlation coefficients may give unreliable evaluation of soil properties. Analytical correlations are mostly based on cavity expansion methods which are restricted to either fully drained or perfectly undrained problems, so that inverse closed-form solutions for relatively simple constitutive models can be derived. In practice, however, depending on physical and consolidation properties of the soil, partially drained conditions may occur during field testing, leading to an erroneous estimation of clay characteristics. Therefore, elaborating a generic parameter identification framework, which is based on the artificial neural network (NN) technique and which may improve the reliability of soil properties derived from in situ testing, is the main goal of this research. This study explores the possibility of using NNs to solve complex inverse problems including partially drained conditions. In other words, NNs are used to map experimental measurements onto set of soil properties. The development of NN-based inverse models is based on a training data sets which consists of pseudo-experimental measurements derived from numerical simulations of both the SBPT and the CPTU test in normally- and lightly overconsolidated clay type material. The study presents a generic two-level procedure designed for the calibration of constitutive models of soils. It is demonstrated that NN inverse models can be easily integrated into the classical back-analysis. At the first level, the NN approach is applied to achieve the first approximation of parameters. This technique is used to avoid potential pitfalls related to the conventional gradient-based optimization (GBO) technique, considered here as a corrector that improves predicted parameters. Trained NNs as parallel operating systems can provide output variables instantly and without a costly GBO iterative scheme. The proposed framework is verified for the elasto-plastic Modified Cam Clay (MCC) model that can be calibrated based on standard triaxial laboratory tests, i.e. the isotropic consolidation test and the consolidated isotropic drained compression test. The study presents formulations of the input data for the NN predictors, enhanced by a dimensional reduction of experimental data using principal component analysis (PCA). The determination of model characteristics is demonstrated, first on numerical pseudo-experiments and then on the experimental data. Furthermore, the efficiency of the proposed approach in terms of accuracy and computational effort is also discussed. The verified two-level strategy is applied to a numerical procedure of parameter identification for the boundary value problem (BVP) of the SBPT. The coupled hydro-mechanical finite element (FE) formulation allows the generated excess pore water pressure to be dissipated during simulations of the expansion test, followed then by a holding test. Numerical simulations demonstrate that volume changes that may occur in clay during the expansion test due to partial drainage, can cause local soil hardening near the cavity wall and affect parameter interpretation for pressuremeter tests. Therefore, the NN technique is applied to obtain an initial guess for model parameters, taking into account the possible partially drained conditions during the expansion test. Parameter identification based on measurements obtained through the pressuremeter expansion test and two types of holding tests is illustrated on the MCC model. NNs are trained using a set of synthetic test samples, which are generated by means of FE simulations based on constrained random permutations of input model parameters. The measurements obtained through expansion and consolidation tests are normalized by the proposed normalizing formulas so that NN predictors operate independently of the testing depth. Examples of parameter determination are demonstrated on both numerical data and field measurements from the Fucino clay deposit. The efficiency of the combined parameter identification in terms of accuracy, effectiveness and computational effort is also discussed. Finally, an application of NN predictors as a stand-alone support for soil profiling is presented for the piezocone test. By similarity to the SBPT problem, a number of NN inverse models are developed based on the results derived from rigorous FE analyzes. The FE model of piezocone penetration involves numerical formulation for the two-phase material obeying the MCC law and including the large strain theory, as well as the large deformation formulation for contact interface. It is demonstrated that a considerable computational effort related to the generation of the training database can be reduced by optimizing the mesh size and "steady-state" depth in function of soil rigidity index. Due to a severe loss of measurement accuracy observed in the finite elements adhered to the "rough" interface, an equivalent semi-numerical approach is proposed to account for frictional effects in different drainage conditions which are delineated from a number of numerical simulations. The validity of the developed penetration model is verified in detail by means of comparisons with other theoretical solutions and parametric studies synthesized from literature, as well as experimental evidence for both undrained and partially drained scenarios. An extended parametric study including influence analyzes of strength and stress anisotropy, rigidity index and cone roughness on two cone factors provides new insight into the analysis of cone penetration. The shortcomings of the FE model due to the limitations of the applied constitutive model are also discussed. As regards NN models, different configurations of input variables, including standard normalized piezocone metrics and other available soil characteristics are investigated in terms of feasibility of effective NN training. The post-training regression analyzes are performed for numerical data allowing the assessment of the influence of specific input variables on accuracy of parameters predictions. Finally, the developed NN models are applied to predict parameters based on field measurements for a number of characterization sites. Provided examples demonstrate that NN inverse models may constitute an effective complementary support during the first-order quantification of the MCC parameters from piezocone measurements.

EPFL2009