1,721,021 research outputs found
Quantum Imprecise Bayesian Networks (QIBN) for Modelling Socio-Ecological-Technical Systems under Uncertainty
No full textMachine Learning Based Seismic Structural Health Monitoring and Reconnaissance
Get PDFCivil structures, including bridges, tunnels, and skyscrapers, are becoming susceptible to losing their intended functionality as they deteriorate through the service life. Furthermore, this situation is exemplified in the face of natural hazards and extreme events. Therefore, monitoring and rapid reconnaissance of the condition and health states of such structures is important for effective decision-making towards building more resilient infrastructure systems. Traditionally, such monitoring and reconnaissance efforts require onsite human inspection. However, given the growth of buildings and other infrastructure in urban centers, such an inspection process is infeasible because of limited human resources, financial burden, and time consuming efforts.In this dissertation, methods to automate the monitoring and reconnaissance processes are proposed. First, methods are introduced to automate the data collection process, where information, that are highly relevant to the health states of structures as well as the entire infrastructure systems, are collected. Second, algorithms are introduced to automate the data processing, where results regarding the health states of structures and infrastructure systems are obtained. Such results are essential for the decision-making process to increase resiliency of the infrastructure systems. The most important technique to automate the above processes is Artificial Intelligence (AI), in particular, Machine Learning (ML) algorithms.In the case of a single structure, the process of observing its response and determining its health state is called Structural Health Monitoring (SHM). A novel SHM framework utilizing Deep Learning (DL) is proposed. The framework is based on Long Short-Term Memory (LSTM) Encoder-Decoder architecture, which is a variant of the Recurrent Neural Network (RNN), applied to Time Series (TS) data. The TS data is processed through the LSTM network, where the information in the TS data is condensed into a Latent Space Vector (LSV), which is processed through traditional ML algorithms to output the structural health conditions, including the overall health conditions, the locations and severity of damage. To enforce the encoding (i.e., condensation) process of the TS into the LSV without information loss, an Encoder-Decoder architecture is proposed. Moreover, a method for fast prediction of the structural responses, which uses variants of the LSTM network, as well as a novel network called Temporal Convolutional Network (TCN), is proposed, and these models (variants) are compared against each other in terms of the accuracy of predicting the structural response. The proposed models are anticipated to complement/replace the traditional physical simulations for faster prediction of the structural response when immediate results are required, e.g., for rapid decision-making.On the data collection side of a single structure, the quality of data obtained from the sensor network is critical to the diagnosis (i.e., determination of the health conditions of the structure). If the sensors are not placed on locations that are sensitive enough to the structural damage, the collected data is not useful for the purpose of diagnosis. In this dissertation, an Optimal Sensor Placement (OSP) method is proposed. The causal relationship among the sensor recordings is identified and quantified through Directed Information (DI). In this method, the sensors are added sequentially, i.e., one sensor at a time, until the specified number of sensors (typically based on expert opinion and availability of resources) is satisfied. The new sensor is added at a location where the causal relationship with the existing sensors is the lowest to ensure low redundancy of the information stored in the array of sensors.For the case of infrastructure on a regional (e.g., city) scale, a method to effectively collect reconnaissance results following an earthquake event is proposed. Social media posts by people near the source of the earthquake, news reports, as well as information from official resources, e.g., United States Geological Survey (USGS), are collected automatically following the earthquake event. Such information is subsequently summarized as a briefing, which provides valuable reference for further detailed reconnaissance (field investigation) and emergency response. The Natural Language Processing (NLP) method is adopted in the formulation of these briefings. Moreover, a practical method to quantify the regional recovery state (a step towards quantifying a metric for the resilience of the affected community) following the earthquake event is proposed. This is based on the number of relevant posts collected from the social media. The recovery is quantified as the averaged recovery states of several key aspects, e.g., water supply to the community, electricity supply to the community, and availability/resumption of the functionality of essential facilities, e.g., medical services by hospitals
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Hardware-in-the-Loop Modeling and Simulation Methods for Daylight Systems in Buildings
Get PDFThis dissertation introduces hardware-in-the-loop modeling and simulation techniques to the daylighting community, with specific application to complex fenestration systems. No such application of this class of techniques, optimally combining mathematical-modeling and physical-modeling experimentation, is known to the author previously in the literature. Daylighting systems in buildings have a large impact on both the energy usage of a building as well as the occupant experience within a space. As such, a renewed interest has been placed on designing and constructing buildings with an emphasis on daylighting in recent times as part of the "green movement.'' Within daylighting systems, a specific subclass of building envelope is receiving much attention: complex fenestration systems (CFSs). CFSs are unique as compared to regular fenestration systems (e.g. glazing) in the regard that they allow for non-specular transmission of daylight into a space. This non-specular nature can be leveraged by designers to "optimize'' the times of the day and the days of the year that daylight enters a space. Examples of CFSs include: Venetian blinds, woven fabric shades, and prismatic window coatings. In order to leverage the non-specular transmission properties of CFSs, however, engineering analysis techniques capable of faithfully representing the physics of these systems are needed. Traditionally, the analysis techniques available to the daylighting community fall broadly into three classes: simplified techniques, mathematical-modeling and simulation, and physical-modeling and experimentation. Simplified techniques use "rules-of-thumb'' heuristics to provide insights for simple daylighting systems. Mathematical-modeling and simulation use complex numerical models to provide more detailed insights into system performance. Finally, physical-models can be instrumented and excited using artificial and natural light sources to provide performance insight into a daylighting system. Each class of techniques, broadly speaking however, has advantages and disadvantages with respect to the cost of execution (e.g. money, time, expertise) and the fidelity of the provided insight into the performance of the daylighting system. This varying tradeoff of cost and insight between the techniques determines which techniques are employed for which projects. Daylighting systems with CFS components, however, when considered for simulation with respect to these traditional technique classes, defy high fidelity analysis. Simplified techniques are clearly not applicable. Mathematical-models must have great complexity in order to capture the non-specular transmission accurately, which greatly limit their applicability. This leaves physical modeling, the most costly, as the preferred method for CFS. While mathematical-modeling and simulation methods do exist, they are in general costly and and still approximations of the underlying CFS behavior. Meaning in fact, measurements of CFSs are currently the only practical method to capture the behavior of CFSs. Traditional measurements of CFSs transmission and reflection properties are conducted using an instrument called a goniophotometer and produce a measurement in the form of a Bidirectional Scatter Distribution Function (BSDF) based on the Klems Basis. This measurement must be executed for each possible state of the CFS, hence only a subset of the possible behaviors can be captured for CFSs with continuously varying configurations. In the current era of rapid prototyping (e.g. 3D printing) and automated control of buildings including daylighting systems, a new analysis technique is needed which can faithfully represent these CFSs which are being designed and constructed at an increasing rate. Hardware-in-the-loop modeling and simulation is a perfect fit to the current need of analyzing daylighting systems with CFSs. In the proposed hardware-in-the-loop modeling and simulation approach of this dissertation, physical-models of real CFSs are excited using either natural or artificial light. The exiting luminance distribution from these CFSs is measured and used as inputs to a Radiance mathematical-model of the interior of the space, which is proposed to be lit by the CFS containing daylighting system. Hence, the components of the total daylighting and building system which are not mathematically-modeled well, the CFS, are physically excited and measured, while the components which are modeled properly, namely the interior building space, are mathematically-modeled. In order to excite and measure CFSs behavior, a novel parallel goniophotometer, referred to as the CUBE 2.0, is developed in this dissertation. The CUBE 2.0 measures the input illuminance distribution and the output luminance distribution with respect to a CFS under test. Further, the process is fully automated allowing for deployable experiments on proposed building sites, as well as in laboratory based experiments. In this dissertation, three CFSs, two commercially available and one novel—Twitchell's Textilene 80 Black, Twitchell's Shade View Ebony, and Translucent Concrete Panels (TCP)—are simulated on the CUBE 2.0 system for daylong deployments at one minute time steps. These CFSs are assumed to be placed in the glazing space within the Reference Office Radiance model, for which horizontal illuminance on a work plane of 0.8 m height is calculated for each time step. While Shade View Ebony and TCPs are unmeasured CFSs with respect to BSDF, Textilene 80 Black has been previously measured. As such a validation of the CUBE 2.0 using the goniophotometer measured BSDF is presented, with measurement errors of the horizontal illuminance between +3% and -10%. These error levels are considered to be valid within experimental daylighting investigations. Non-validated results are also presented in full for both Shade View Ebony as well as TCP. Concluding remarks and future directions for HWiL simulation close the dissertation
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Structural Behavior of Bent Cap Beams in As-built and Retrofitted Reinforced Concrete Box-Girder Bridges
Get PDFResearch on resilient infrastructure systems is expanding. As we experience more infrastructure deterioration in the US, numerous efforts are ongoing for building the nation's new infrastructure and maintaining the existing one. Bridges are key components of infrastructure that are vulnerable to earthquakes and are undergoing retrofit or complete replacement. Thus, optimized seismic design of new bridges and informed retrofit decisions are indispensable. A specific design issue that is concerned with the structural response of bent cap beams in as-built and retrofitted box-girder bridges under gravity and seismic loads is tackled in this dissertation. The lack of proper account of box-girder slabs contribution to the integral bent cap can lead to an uneconomical seismic design of new bridges or unfavorable mode of failure in retrofitting existing ones. A combined experimental and computational research was undertaken in this study to investigate the structural behavior and seismic response of bent cap beams in as-built and retrofitted reinforced concrete box-girder bridges under the combined effect of vertical and lateral loading. In particular, the contribution of the box-girder slabs to the stiffness and strength of the integral bent caps was evaluated for optimized design and enhanced capacity estimation. The computational part of the study consisted of two phases: pre-test and post-test analyses. The experimental program involved testing two 1/4-scale column-bent cap beam-box girder subassembly using quasi-static and Hybrid Simulation (HS) testing methods. The test specimens were adopted from a typical California bridge that is modified from the Caltrans Academy Bridge, and were designed in light of the most recent AASHTO and Caltrans provisions. The pre-test analysis phase of the computational research utilized one-, two-, and three-dimensional finite element models to carry out different linear and nonlinear static and time history analyses for both of the full prototype bridge and the test specimen. The pre-test analysis successfully verified the expected subassembly behavior and provided beneficial input for the experimental program. The first stage of the experimental program involved quasi-static cyclic loading tests of the first specimen in as-built and repaired conditions. Bidirectional cyclic loading tests in both transverse and longitudinal directions were conducted under constant gravity load. A rapid repair scheme was adopted for the tested specimen using a Carbon Fiber Reinforced Polymer (CFRP) column jacket. A similar quasi-static cyclic test to the as-built specimen was carried out for the repaired specimen for comparison purposes and to verify the essentially elastic status of the bent cap beam. The second stage of the experimental study embraced the HS testing technique for providing the lateral earthquake loading to the test specimens. A new practical approach that utilized readily available laboratory data acquisition systems as a middleware for feasible HS communication was achieved as part of this study. The proper communication among the HS components and the verification of the HS system were first performed using tests conducted on standalone hydraulic actuators. A full specimen HS trial test was conducted using the previously tested repaired specimen to validate the whole HS system. The last phase in the experimental program involved retrofitting the column of the second specimen using CFRP jacketing before any testing to increase the demands on the bent cap beam for further investigation into its inelastic range of structural response. The retrofitted second specimen was then tested using multi-degree of freedom HS under constant gravity load using several scales of unidirectional and bidirectional near-fault ground motions. The post-test analysis was the final stage of this study. The results from the as-built first specimen cyclic tests were used to calibrate the most detailed three-dimensional finite element model, which was previously developed as part of the pre-test analysis stage. The calibrated model was used to explore the effect of reducing the bent cap reinforcement on the overall system behavior and to investigate the box-girder contribution at higher levels of bent cap seismic demand. Based on the computational and experimental results obtained in this study, the effective slab width for integral bent caps was revisited. The study concluded that the slab reinforcement within an effective width, especially in tension, should be included for accurate bent cap capacity estimation. The study was finalized with an illustrative design example to investigate the design implications of the revised effective slab width and bent cap capacity estimation on the optimization of the bent cap design for a full-scale bridge
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Optimization Algorithms for Performance-based Design and Structural Model Updating
Get PDFMany problems in structural engineering can be defined as optimization problems and be solved by adopting proper optimization algorithms. This dissertation focuses on two optimization problems in structural engineering, namely, Performance-based Design Optimization (PDO) and Structural Model Updating (SMU).In recent years, there have been many studies on the sustainability and resilience of building systems during their life-cycle. In particular, the holistic design framework using a Performance-Based Engineering (PBE) approach combined with the Multi-Attribute Utility Theory (MAUT), namely PBE-MAUT, is developed to provide a robust evaluation of the building performance in terms of sustainability and resiliency attributes. In PDO, the PBE-MAUT allows engineers to rank the design alternatives through the Generalized Expected Utility (GEU) (objective function to be maximized) containing the information about the risk attitude (or perception) of the decision makers. This dissertation proposes the PDO framework for seismic hazard using two meta-heuristic algorithms, namely, Genetic Algorithm (GA), abbreviated as PDO-GA and Bayesian Optimization Algorithm (BOA), abbreviated as PD-BOA. In this framework, probabilistic approaches are used to quantify the uncertainties in the different stages of the Performance-Based Earthquake Engineering (PBEE). For the seismic hazard, artificial accelerograms compatible with the response spectrum are generated by evolutionary Power Spectral Density (PSD) for seismic loading. For the structural analysis, distributions of the Engineering Demand Parameters (EDPs) are determined by using the Kernel Density Maximum Entropy Method (KDMEM), which provides the least biased probability density function from available data sets. For the combined damage analysis and loss estimation, GEU is used to evaluate the utility of the design alternatives based on the MAUT. The proposed framework adopts a Probability of Improvement (PI) function for the acquisition function of BOA. A hypothetical three-bay, five-story steel Moment Resisting Frame (MRF) building and three-bay, nine-story steel MRF building examples demonstrate the performances of the proposed framework.
Numerical models are very powerful tools used in simulation, damage detection, and evaluating physical structures. Accurate modeling of complex structures, however, remains challenging due to incomplete information (uncertainties) about the existing structure, which results in a difference between the responses of the numerical model and the measured responses of the actual "instrumented" structure. SMU is a process for improving the accuracy of a numerical model by reducing or even closing the gap between its prediction and the measured response of its physical counterpart through model parameter optimization. In this dissertation, the ABAQUS-Python Model Updating framework for overhead box beam highway sign structures is proposed. This is a Python-based framework that uses the ABAQUS software to create and analyze Finite Element (FE) models. The performance of the proposed framework is demonstrated by application to a single-post butterfly type overhead box beam sign structure on the California State Route 113, near the city of Davis, CA
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Experimental and Analytical Investigation of Reinforced Concrete Columns Subjected to Horizontal and Vertical Ground Motions
Get PDFThe effect of vertical excitation on shear strength of reinforced concrete (RC) columns has been investigated by various researchers. Field evidences, analytical studies and static or hybrid simulations suggested that excessive tension or tensile strain of the column may lead to shear degradation, and that vertical excitation can be one of the causes of shear failure. The published literature lacks dynamic experiments to investigate the effect of vertical excitation on the shear strength of RC columns due to limitations of testing facility. Considering that current seismic codes do not have a consensus on the effect of vertical acceleration on the shear demand and capacity, the presented dynamic tests and accompanying analytical investigation contribute to better understanding of the effect of vertical excitation on shear failure, one of the most critical brittle failure mechanisms.This dissertation provides the experimental and computational results, which confirm that the vertical acceleration can induce shear strength degradation of RC columns. Dynamic tests of two reduced geometrical scale specimens were conducted on the UC-Berkeley shaking table at Richmond Field Station. The two specimens had different transverse reinforcement ratio. As a result of an analytical investigation and preliminary fidelity tests, 1994 Northridge earthquake acceleration recorded at the Pacoima Dam was selected as an input motion among the 3,551 earthquake acceleration records in the PEER NGA database. The chosen ground motion was applied to the test specimens at various levels ranging from 5% to 125%. The specimens were subjected to combinations of the vertical component and the larger of the two horizontal components of the selected ground motion record. For the 125%-scale, not only combined vertical and horizontal motion was applied but also a single horizontal component was considered for direct evaluation of the effect of the vertical excitation.The experimental results imply that vertical acceleration has the potential to degrade the shear capacity of RC columns. The peak shear force in the 125%-scale run with only the horizontal component was larger than that in the 125%-scale runs with the horizontal and vertical components for each specimen, where the peak force was determined by the shear strength at these high-level tests. For these runs, considerable tensile forces were induced on the tested columns due to the vertical excitation. Tension in the columns resulted in degradation of the shear strength, which is mainly due to the degradation of the concrete contribution to the shear strength. Flexural damage at the top of the column took place before the flexural damage at the base since the bending moment at the top was larger. This was a result of the large mass moment of inertia and rigid body rotation of the mass blocks at the top of the column. In addition, comparison of the bending moment histories at the base and top of the two test specimens indicated that they were opposite in sign during the strong part of the excitation of all the intensity levels suggesting that the columns were in double-curvature. As a result of flexural yielding at the top and base of the column when bending in double curvature, the shear force reached the shear capacity which would not take place if yielding occurred only at the base. Consequently, shear cracks took place and extended over the entire column height as the intensity increased especially under the presence of significant axial tension.The analytical investigation also revealed that considerable axial tension forces can be induced in RC columns which resulted in degradation in the shear strength. Two types of computational models were utilized in the computational platform, OpenSees. Models A and B had a beam with hinges element and a nonlinear beam-column element, respectively. In addition, a new shear spring element was implemented in the same computational platform to employ code-based shear strength estimation. The element incorporates the shear strength estimations based on ACI or Caltrans SDC equations addressing the effect of column axial load and displacement ductility. Each of the models A and B was developed both without and with the newly-developed shear spring element. Upon improved modeling, results from the analysis of the tested specimens were examined in terms of shear strength variation. Accordingly, current code equations and the corresponding computational models were evaluated. The models without the shear springs did not capture the shear strength degradation accurately, whereas those including the ACI and Caltrans SDC shear springs captured the shear strength degradation due to the axial tension. Both of the ACI and Caltrans SDC springs provided results on the conservative side, where the ACI shear spring predictions were closer to the experimental results than those of the Caltrans SDC shear spring. Elimination of the concrete contribution to the shear strength under any tension was the main reason for the highly conservative predictions of the Caltrans SDC shear strength equation where the strength reduction caused by ductility was not as significant as that by the axial tension force
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Performance-Based Robust Nonlinear Seismic Analysis with Application to Reinforced Concrete Bridge Systems
Get PDFThe performance-based earthquake engineering (PBEE) approach, developed at the Pacific Earthquake Engineering Research (PEER) Center, aims to robustly decompose the performance assessment and design process into four logical stages that can be studied and resolved in a systematic and consistent manner. However, two key challenges are experienced in this approach, namely the accurate seismic structural analysis and the selection and modification of ground motions (GMs). This dissertation investigates these two challenges with application to reinforced concrete (RC) bridge systems.In nonlinear structural dynamics, the most accurate analytical simulation method is the nonlinear time history analysis (NTHA). It involves the use of different types of direct integration algorithms and nonlinear equation solvers where their stability performance and convergence behaviors are of great significance. Lyapunov stability theory, the most complete framework for stability analysis of dynamical systems, is introduced in this study. Based on this theory, a new nonlinear equation solver is developed and its convergence performance was theoretically formulated and verified by several examples. Stability is one of the most important properties of direct integration algorithms that must be considered for efficient and reliable NTHA simulations. Two Lyapunov-based approaches are proposed to perform stability analysis for nonlinear structural systems. The first approach transforms the stability analysis to a problem of existence, that can be solved via convex optimization. The second approach is specifically applicable to explicit algorithms for nonlinear single-degree of freedom and multi-degree of freedom systems considering strictly positive real lemma. In this approach, the stability analysis of the formulated nonlinear system is transformed to investigating the strictly positive realness of its corresponding transfer function matrix.Ground motion selection and modification (GMSM) procedures determine the necessary input excitations to the NTHA simulations of structures. Therefore, proper selection of the GMSM procedures is vital and an important prerequisite for the accurate and robust NTHA simulation and thus for the entire PBEE approach. Although many GMSM procedures are available, there is no consensus regarding a single accurate method and many studies focused on evaluating these procedures. In this dissertation, a framework for probabilistic evaluation of the GMSM procedures is developed in the context of a selected large earthquake scenario with bidirectional GM excitations.In urban societies, RC highway bridges, representing key components of the transportation infrastructure systems, play a significant role in transporting goods and people around natural terrains. Therefore, they are expected to sustain minor damage and maintain their functionality in the aftermath of major earthquakes, which commonly occur in California due to many active faults. Accurate seismic structural analysis of existing and newly designed RC highway bridges is fundamental to estimate their seismic demands. As such important lifeline structures, RC highway bridge systems are investigated as an application of the previously discussed theoretical developments proposed in this dissertation to address the two key challenges in the PEER PBEE approach
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Structural Health Monitoring Framework using Acceleration Data and Machine Learning Techniques
Get PDFStructural health monitoring (SHM) is becoming more and more important as the civil infrastructure systems in the US are aging. Since replacing all these structures are not feasible, SHM is necessary to monitor the structural integrity and assess the structural deterioration for safe and continuous operation of the structural systems. The monitoring of structures becomes more critical in the event of an earthquake as the lack of knowledge immediately following an earthquake about the structural condition may result in population displacement, business disruption, and an extended recovery time. In this study, a SHM framework is developed for rapid post-earthquake damage assessment. A damage feature called the cumulative absolute velocity (CAV) is used and machine learning techniques are employed. The applicability of the framework is tested using data from laboratory experiments, real structures, as well as numerical analysis.Although there are numerous damage features, only a handful of them can be applied to strong motion data. A wave-based feature that is simple in nature, scalable, easily computed with limited computational effort, and contains information from the whole signal is yet to be developed. Therefore, this study evaluates the application of the CAV as a local damage indicator. The response of a bridge column test specimen instrumented with 18 accelerometers and subjected to increasing six levels of shaking is investigated to understand the relationship between CAV and damage. This damage feature is subsequently evaluated for two instrumented buildings damaged during earthquakes in California: Van Nuys hotel and Imperial County Services (ICS) building. The results show that for the 7-story Van Nuys hotel, the CAV can correctly locate the damage that occurred during the 1994 Northridge earthquake. For the ICS building, the CAV is also able to correctly identify the location of damage. Thus, the presented CAV feature enables identifying the onset and location of damage, confirming the hypothesis that CAV is correlated with structural damage. To automate and expedite the process of damage assessment, a methodology is introduced using low dimensional, CAV-based feature within a machine learning (ML) algorithm. The appropriate features and ML algorithms are identified by analyzing a single degree of freedom (SDOF) OpenSEES model. A comparative analysis of four ML algorithms is performed using three CAV-based features to determine the ideal feature and ML tool. The analysis showed that when absolute and relative (with respect to linear response) CAV features are used together, the highest accuracy is obtained. Moreover, ordinal logistic regression (OLR) is shown to be a suitable ML approach. Subsequently, OLR is applied with the identified features to classify damage of two multi-degree of freedom (MDOF) systems representing five-story buildings. One of the systems has uniform story shear capacity (MDOF-US) and the other has non-uniform (MDOF-NS) story shear capacity. Results show that the proposed method assess severity and location of damage with high accuracy (greater than 90\%) when damage information is available through an experimental study or an analytical model. However, such models are typically not available for existing structures in most cases. Therefore, it may not be considered feasible to develop one just for the sole purpose of SHM due to cost, time, or resource limitations. Accordingly, an SHM framework called the human-machine collaboration (H-MC) is proposed for existing structures with limited data which attempts to use the advantages provided by the ML and the knowledge of a human expert. In this way, domain expertise and data science are efficiently combined to solve the SHM problem. Subsequently, the framework is applied to detect damage in selected fifteen California Strong Motion Instrumentation Program (CSMIP) instrumented buildings. The results show that the H-MC algorithm correctly labeled the undamaged and damaged cases. Moreover, it eliminated false positive detection, which is a concern in decision making based on conventional SHM. This framework opens up the opportunity to automate the process of damage detection and assess and notify the risk associated with each structure in near-real time after an earthquake.A probabilistic decision analysis method PBE-MAUT is also presented in this study. It integrates the powerful and efficient MAUT with the PBE methodology. It provides the decision maker with a robust quantitative tool to consider multiple criteria with different measuring units to facilitate the decision making process. The PBE-MAUT method is applied to compare different SHM systems considering cost and accuracy. Results show that the CAV-based method with all floors instrumented is the best option for a risk-neutral decision maker even though the option has the highest cost among all the options. The findings of this decision analysis, in turn, highlight the usefulness of instrumenting buildings and the associated effectiveness of the proposed SHM framework
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Experimental and Analytical Studies on Old Reinforced Concrete Buildings with Seismically Vulnerable Beam-Column Joints
Get PDFExisting reinforced concrete (RC) buildings designed prior to 1970s are vulnerable to shear failure in beam-column joints under earthquake loads because of insufficient transverse reinforcement in the joint region. To assess the seismic risk of old RC buildings, the accurate prediction of shear strength and deformability for these unreinforced beam-column joints is essential. Several joint shear strength models are available in the literature but they have been originally developed to predict the shear strength of reinforced beam-column joints. Due to the different shear force transfer mechanism between reinforced and unreinforced beam-column joints, the existing models have little success to assess the shear strength of unreinforced beam-column joints. The ASCE/SEI 41-06 provisions specify shear strengths and backbone curves for unreinforced beam-column joints but the predictions using these provisions are usually conservative compared with many experimental test data collected from the literature. This study is focusing on developing accurate shear strength models and backbone relationships for unreinforced exterior and corner beam-column joints.This study proposes two shear strength models, semi-empirical and analytical, for unreinforced exterior and corner beam-column joints to reflect the influence of two key parameters: (1) joint aspect ratio which is defined as the ratio of beam to column cross-section heights, and (2) beam reinforcement index which is related to the amount of beam longitudinal reinforcement in tension. These key parameters are determined from a parametric study using a large experimental data set of unreinforced exterior and corner beam-column joints from the published literature. The proposed models are validated by accurate predictions of the shear strength for the database specimens. Besides the accuracy of the proposed models, the semi-empirical model has the advantage of straightforward extension to other types of beam-column joints. An important advantage of the analytical model is that for the case of beam yielding followed by joint failure, the analytical model can predict the reduced shear strength without the need for the complexity of ductility consideration.The experimental phase of this study includes testing four full-scale unreinforced corner beam-column joint specimens. These four specimens are designed to investigate the effect of the joint aspect ratio and the beam longitudinal reinforcement ratio. The test results show that the joint shear strengths are reduced with increase of the joint aspect ratio and for each of the joint aspect ratio, the joint shear strengths are proportional to the beam longitudinal reinforcement ratio within the range provided in the test specimens. The shear strengths of the four specimens are accurately predicted by the two proposed models, while the ASCE/SEI 41-06 provisions for shear strength produce conservative estimates of the strengths for the test specimens.Based on the measured joint shear stress-rotation and visual observation of the tested corner beam-column joint specimens, a multi-linear backbone relationship is proposed in this study to reflect the following beam-column joint responses: (1) initial joint cracking, (2) either beam reinforcement yielding or significant opening of existing joint cracks, (3) either existing joint cracks further propagation or additional joint cracks opening at the peak load, and (4) residual joint shear stress and rotation after severe damage in the joint. Corresponding parameters in the backbone relationship are defined from the comparison with test results. The proposed backbone relationship is verified by the simulations for beam-column subassemblies of the tested four specimens and other four planar exterior beam-column joint specimens from the literature. To investigate the effect of beam-column joint flexibility on the lateral response in a structural system level, nonlinear static and dynamic simulations are performed. These simulations indicate that beam-column joint flexibility is essential for older-type RC buildings characterized by having unreinforced beam-column joints. As an extension of this study, progressive collapse analysis for older-type RC buildings will be pursued with the proposed beam-column joint backbone relationships
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Computational Strategies for Multi-Scale Modeling of Masonry Components and Structures
Get PDFIn the current state of structural engineering, physical experimentation still offers the most reliable means for the determination of parameters for accurate modeling of complex systems. However, engineers usually face time and financial constraints that prevent them from performing such experiments. On the other hand, engineers are in increased need of a reliable predicting computational power so that resources are optimized in the design of new structures and the retrofit of existing ones. Moreover, society expects us to be able to guarantee that structures will have adequate performance during their life. In the present work, a computational framework for the analysis of masonry structures is presented. The framework relies on the well-established theory of micromechanical homogenization. The dissertation consists of two distinct parts: I. Constitutive modeling on the component level and II. Applications at the structural level. First, homogenization is conducted for the elastic parameters. Those parameters are used in a linear elastic analysis of masonry at a macro-scale. Then, with proper engineering judgments, a representative state of strain is determined where the unit cell is subjected to a monotonically increasing state of strain. A stress versus strain relationship is obtained accounting for material regularization so that dimensional effects and proper softening behavior are considered. Effective material parameters are tested against experiments as well as against micro-modeling leading to acceptable results at an affordable computational cost. The framework is applied to the modeling of reinforced concrete frames with masonry infills subjected to in-plane and out-of-plane loading. A three-dimensional model is needed for explicitly accounting for the out-of-plane arching action, which makes the homogenization procedure the only feasible approach for such problem
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