Seismic retrofitting of substandard frame buildings using steel shear walls

The use of steel shear panels represents an effective strategy to enhance the seismic performance of substandard framed buildings not designed to resist earthquakes. The seismic response of framed structures equipped with steel walls can be predicted using finite element models with accurate shell elements for representing the steel panels. However, such a detailed numerical description requires significant computational resources, especially for nonlinear dynamic analysis of large retrofitted buildings with steel infill plates. Besides, the design of steel shear walls for seismic retrofitting has been addressed mainly by trial-and-error methods in previous research and practical applications. Therefore, there is a clear need for more simplified and efficient numerical models for accurate simulations of steel shear walls under earthquake loading and enhanced seismic retrofitting design procedures with automatic selection of the retrofitting components. In this research, an 8-noded macroelement formulation is first proposed incorporating six nonlinear springs with asymmetric constitutive relationships. To improve the macroelement performance, material parameters are calibrated via genetic algorithms (GAs) based on the numerical results from validated shell element models. Subsequently, simple functions for macroelement material parameters in terms of steel plate geometrical properties are determined using multiple linear regressions. Applications to numerical examples have confirmed the accuracy and computational efficiency of the proposed macroelement with calibrated material properties. An improved optimal seismic retrofitting design procedure utilising steel shear wall macroelements is developed based on the capacity spectrum method. The proposed approach regards the selection and design of infill plates as a multi-objective optimisation problem with constraints solved by GA procedures. Nonlinear regression for equivalent viscous damping of steel shear walls is also carried out to determine the hysteretic damping ratio as a function of plate dimensions and drift demand. Afterwards, the proposed optimal design strategy is applied to the seismic retrofitting of a deficient 4-storey RC frame building. Seismic assessment is finally conducted for the retrofitted structure, where a significant enhancement of the seismic performance is observed.Open Acces

On-the-fly adaptivity for nonlinear twoscale simulations using artificial neural networks and reduced order modeling

A multi-fidelity surrogate model for highly nonlinear multiscale problems is proposed. It is based on the introduction of two different surrogate models and an adaptive on-the-fly switching. The two concurrent surrogates are built incrementally starting from a moderate set of evaluations of the full order model. Therefore, a reduced order model (ROM) is generated. Using a hybrid ROM-preconditioned FE solver, additional effective stress-strain data is simulated while the number of samples is kept to a moderate level by using a dedicated and physics-guided sampling technique. Machine learning (ML) is subsequently used to build the second surrogate by means of artificial neural networks (ANN). Different ANN architectures are explored and the features used as inputs of the ANN are fine tuned in order to improve the overall quality of the ML model. Additional ANN surrogates for the stress errors are generated. Therefore, conservative design guidelines for error surrogates are presented by adapting the loss functions of the ANN training in pure regression or pure classification settings. The error surrogates can be used as quality indicators in order to adaptively select the appropriate -- i.e. efficient yet accurate -- surrogate. Two strategies for the on-the-fly switching are investigated and a practicable and robust algorithm is proposed that eliminates relevant technical difficulties attributed to model switching. The provided algorithms and ANN design guidelines can easily be adopted for different problem settings and, thereby, they enable generalization of the used machine learning techniques for a wide range of applications. The resulting hybrid surrogate is employed in challenging multilevel FE simulations for a three-phase composite with pseudo-plastic micro-constituents. Numerical examples highlight the performance of the proposed approach

A Prediction Model for the Calculation of Effective Stiffness Ratios of Reinforced Concrete Columns

Nonlinear dynamic analyses of reinforced concrete (RC) frame buildings require the use of effective stiffness of members to capture the effect of cracked section stiffness. In the design codes and practices, the effective stiffness of RC sections is given as an empirical fraction of the gross stiffness. However, a more precise estimation of the effective stiffness is important as it affects the distribution of forces and various demands and response parameters in nonlinear dynamic analyses. In this study, an evolutionary computation method called gene expression programming (GEP) was used to predict the effective stiffness ratios of RC columns. Constitutive relationships were obtained by correlating the effective stiffness ratio with the four mechanical and geometrical parameters. The model was developed using a database of 226 samples of nonlinear dynamic analysis results collected from another study by the author. Subsequent parametric and sensitivity analyses were performed and the trends of the results were confirmed. The results indicate that the GEP model provides precise estimations of the effective stiffness ratios of the RC frame

Metamodeling choices for seismic vulnerability assessment of BRB-retrofitted low-ductility RC frames

Damage incurred in low-ductility reinforced concrete (RC) buildings during recent earthquakes continues to underline their structural vulnerability under seismic shaking. Among the viable seismic retrofitting procedures, passive control systems such as buckling-restrained braces (BRBs) have emerged as an efficient strategy for structural damage mitigation through stable energy dissipation while providing additional strength and stiffness to low-ductility buildings. Although quantifying the beneficial effects of BRBs for vulnerability reduction through seismic fragility curves has been suitably investigated in literature, almost all such studies consider a deterministic description of the BRB device. This study illustrates a metamodeling framework rooted in statistical learning techniques for efficient seismic vulnerability assessment of BRB-retrofitted low-ductility RC frames. The framework develops multidimensional probabilistic seismic demand models for response prediction of a case study retrofitted frames as a function of ground motion characteristics as well as the design parameters of the BRB device. These demand models when compared against damage states capacity estimates subsequently yields vector-based seismic fragility functions that provide notable advantages over unidimensional fragility curves in terms of efficiency as well as generality. Additionally, uncertainties stemming from a multitude of sources can also be conveniently captured and propagated through the different stages of statistical model development. The proposed study aims to help researchers, stakeholders, and even device manufactures by providing a convenient tool for vulnerability evaluation of retrofitted structures with reasonable accuracy and enhanced efficiency of computation

Reliability and Reliability-based Sensitivity Analyses of Steel Moment-Resisting Frame Structure subjected to Extreme Actions

The ground external columns of buildings are vulnerable to the extreme actions such as a vehicle collision. This event is a common scenario of buildings' damages. In this study, a nonlinear model of 2-story steel moment-resisting frame (SMRF) is made in OpenSees software. This paper aims investigating the reliability analysis of aforementioned structure under heavy vehicle impact loadings by Monte Carlo Simulation (MCS) in MATLAB software. To reduce computational costs, meta-model techniques such as Kriging, Polynomial Response Surface Methodology (PRSM) and Artificial Neural Network (ANN) are applied and their efficiency is assessed. At first, the random variables are defined. Then, the sensitivity analyses are performed using MCS and Sobol's methods. Finally, the failure probabilities and reliability indices of studied frame are presented under impact loadings with various collision velocities at different performance levels and thus, the behavior of selected SMRF is compared by using fragility curves. The results showed that the random variables such as mass and velocity of vehicle and yield strength of used materials were the most effective parameters in the failure probability computation. Among the meta-models, Kriging can estimate the failure probability with the least error, sample number with minimum computer processing time, in comparison with MCS

Pipeline health monitoring

Worldwide, BP operates many thousand kilometres of pipelines carrying valuable yet toxic and corrosive fluids. The structural integrity of these pipelines is crucial, as any failure may result in environmental damage, economic losses and injuries to personnel. Convention- ally, pipeline integrity is assessed on a time basis. This inherently limits the amount of infor- mation available about its structural health, as any damage which develops in unexpected circumstances or while the pipeline is not being inspected may remain undetected. Such lack of information hinders the reliability of any prognosis and of Risk-Based Inspection and Maintenance strategies, increases the risk of unexpected critical damage development and pipeline failure, and forces the use of costly time-based maintenance, following the safe-life design approach. Conversely, if sufficient information about pipeline integrity were avail- able to produce reliable prognoses, then it would become possible to dramatically reduce the risk of unexpected failures and to utilise cost-efficient condition-based maintenance, which prescribes the replacement of a pipeline only when it is about to suffer critical dam- age and has therefore reached the actual end of its operational life. In this way, pipeline networks would become safer and more reliable while at the same time more productive and less costly. This thesis introduces and demonstrates a Structural Health Monitoring ap- proach that has the potential to fill the integrity information gap and ultimately enable the use of condition-based pipeline maintenance. This approach, embodied by a practical au- tomated pipeline damage detection procedure, complements permanently installed guided wave sensors to create a complete pipeline health monitoring solution. Utilising experimen- tal data from a permanently installed guided wave sensor installed on a purpose-built NPS 8 Schedule 40 pipe loop facility at BP’s Naperville Campus, it is shown that the procedure is very effective at detecting and quantifying actual damage, thereby achieving the intended aim of this thesis.Open Acces

An Assessment to Benchmark the Seismic Performance of a Code-Conforming Reinforced-Concrete Moment-Frame Building

This report describes a state-of-the-art performance-based earthquake engineering methodology that is used to assess the seismic performance of a four-story reinforced concrete (RC) office building that is generally representative of low-rise office buildings constructed in highly seismic regions of California. This “benchmark” building is considered to be located at a site in the Los Angeles basin, and it was designed with a ductile RC special moment-resisting frame as its seismic lateral system that was designed according to modern building codes and standards. The building’s performance is quantified in terms of structural behavior up to collapse, structural and nonstructural damage and associated repair costs, and the risk of fatalities and their associated economic costs. To account for different building configurations that may be designed in practice to meet requirements of building size and use, eight structural design alternatives are used in the performance assessments. Our performance assessments account for important sources of uncertainty in the ground motion hazard, the structural response, structural and nonstructural damage, repair costs, and life-safety risk. The ground motion hazard characterization employs a site-specific probabilistic seismic hazard analysis and the evaluation of controlling seismic sources (through disaggregation) at seven ground motion levels (encompassing return periods ranging from 7 to 2475 years). Innovative procedures for ground motion selection and scaling are used to develop acceleration time history suites corresponding to each of the seven ground motion levels. Structural modeling utilizes both “fiber” models and “plastic hinge” models. Structural modeling uncertainties are investigated through comparison of these two modeling approaches, and through variations in structural component modeling parameters (stiffness, deformation capacity, degradation, etc.). Structural and nonstructural damage (fragility) models are based on a combination of test data, observations from post-earthquake reconnaissance, and expert opinion. Structural damage and repair costs are modeled for the RC beams, columns, and slabcolumn connections. Damage and associated repair costs are considered for some nonstructural building components, including wallboard partitions, interior paint, exterior glazing, ceilings, sprinkler systems, and elevators. The risk of casualties and the associated economic costs are evaluated based on the risk of structural collapse, combined with recent models on earthquake fatalities in collapsed buildings and accepted economic modeling guidelines for the value of human life in loss and cost-benefit studies. The principal results of this work pertain to the building collapse risk, damage and repair cost, and life-safety risk. These are discussed successively as follows. When accounting for uncertainties in structural modeling and record-to-record variability (i.e., conditional on a specified ground shaking intensity), the structural collapse probabilities of the various designs range from 2% to 7% for earthquake ground motions that have a 2% probability of exceedance in 50 years (2475 years return period). When integrated with the ground motion hazard for the southern California site, the collapse probabilities result in mean annual frequencies of collapse in the range of [0.4 to 1.4]x10 -4 for the various benchmark building designs. In the development of these results, we made the following observations that are expected to be broadly applicable: (1) The ground motions selected for performance simulations must consider spectral shape (e.g., through use of the epsilon parameter) and should appropriately account for correlations between motions in both horizontal directions; (2) Lower-bound component models, which are commonly used in performance-based assessment procedures such as FEMA 356, can significantly bias collapse analysis results; it is more appropriate to use median component behavior, including all aspects of the component model (strength, stiffness, deformation capacity, cyclic deterioration, etc.); (3) Structural modeling uncertainties related to component deformation capacity and post-peak degrading stiffness can impact the variability of calculated collapse probabilities and mean annual rates to a similar degree as record-to-record variability of ground motions. Therefore, including the effects of such structural modeling uncertainties significantly increases the mean annual collapse rates. We found this increase to be roughly four to eight times relative to rates evaluated for the median structural model; (4) Nonlinear response analyses revealed at least six distinct collapse mechanisms, the most common of which was a story mechanism in the third story (differing from the multi-story mechanism predicted by nonlinear static pushover analysis); (5) Soil-foundation-structure interaction effects did not significantly affect the structural response, which was expected given the relatively flexible superstructure and stiff soils. The potential for financial loss is considerable. Overall, the calculated expected annual losses (EAL) are in the range of $52,000 to$97,000 for the various code-conforming benchmark building designs, or roughly 1% of the replacement cost of the building ($8.8M). These losses are dominated by the expected repair costs of the wallboard partitions (including interior paint) and by the structural members. Loss estimates are sensitive to details of the structural models, especially the initial stiffness of the structural elements. Losses are also found to be sensitive to structural modeling choices, such as ignoring the tensile strength of the concrete (40 EAL) or the contribution of the gravity frames to overall building stiffness and strength (15 change in EAL). Although there are a number of factors identified in the literature as likely to affect the risk of human injury during seismic events, the casualty modeling in this study focuses on those factors (building collapse, building occupancy, and spatial location of building occupants) that directly inform the building design process. The expected annual number of fatalities is calculated for the benchmark building, assuming that an earthquake can occur at any time of any day with equal probability and using fatality probabilities conditioned on structural collapse and based on empirical data. The expected annual number of fatalities for the code-conforming buildings ranges between 0.05*10 -2 and 0.21*10 -2 , and is equal to 2.30*10 -2 for a non-code conforming design. The expected loss of life during a seismic event is perhaps the decision variable that owners and policy makers will be most interested in mitigating. The fatality estimation carried out for the benchmark building provides a methodology for comparing this important value for various building designs, and enables informed decision making during the design process. The expected annual loss associated with fatalities caused by building earthquake damage is estimated by converting the expected annual number of fatalities into economic terms. Assuming the value of a human life is$3.5M, the fatality rate translates to an EAL due to fatalities of $3,500 to$5,600 for the code-conforming designs, and $79,800 for the non-code conforming design. Compared to the EAL due to repair costs of the code-conforming designs, which are on the order of$66,000, the monetary value associated with life loss is small, suggesting that the governing factor in this respect will be the maximum permissible life-safety risk deemed by the public (or its representative government) to be appropriate for buildings. Although the focus of this report is on one specific building, it can be used as a reference for other types of structures. This report is organized in such a way that the individual core chapters (4, 5, and 6) can be read independently. Chapter 1 provides background on the performance-based earthquake engineering (PBEE) approach. Chapter 2 presents the implementation of the PBEE methodology of the PEER framework, as applied to the benchmark building. Chapter 3 sets the stage for the choices of location and basic structural design. The subsequent core chapters focus on the hazard analysis (Chapter 4), the structural analysis (Chapter 5), and the damage and loss analyses (Chapter 6). Although the report is self-contained, readers interested in additional details can find them in the appendices

Probabilistic seismic demand modeling of local level response parameters of an RC frame

Probabilistic methods to evaluate the seismic vulnerability of reinforced concrete (RC) frames are largely used in the context of performance based design and assessment, often describing the structural response using global engineering demand parameters (EDPs) such as the maximum interstory drift. While such EDPs are able to synthetically describe the structural behavior, the use of local EDPs is necessary to provide a more realistic and thorough description of failure mechanisms of low-ductility frames lacking seismic details. The objective of this paper is to investigate viable probabilistic seismic demand models of local EDPs, which may be used in developing fragility curves for the assessment of the low-ductility RC frames. The present work explores adequate regression models, probability distributions and uncertainty variation of the demand models. In addition, the adequacy of several ground motion intensity measures (IMs) to be used for predictive modeling of local EDPs is investigated. A realistic benchmark three-story RC frame representative of non-ductile buildings is used as a case study to identify key considerations