Sungmoon Jung

Systematic update of a deep excavation model using field performance data

Abstract Numerical simulation of construction staging of deep urban excavations is commonly used to estimate induced ground deformations. A novel method to extract the constitutive model of the soil behavior directly from field measurements of excavation response is introduced. This methodology develops a neural network based constitutive model of the soil, given the lateral wall deformation and surface settlement profile measurements. The resulting soil model, used in a numerical analysis, provides correct ground deformations and can be used in the forward prediction of future excavations or later excavation stages. The soil model can continuously evolve using additional field information.

Extracting inelastic metal behaviour through inverse analysis: a shift in focus from material models to material behaviour

This article implements a new data-driven inverse analysis method, SelfSim (self-learning simulations), for extracting material behaviour of metals. SelfSim uses load-displacement measurements from structural tests whereby the material experiences non-uniform stresses and strains to extract the material constitutive behaviour in the form of a stress-strain database unconstrained by a pre-defined material model. The method is verified using simulated and physical experiments on metal structures. SelfSim successfully extracts the anisotropic response of aluminium from multiple tests. The method simplifies the laborious and lengthy process of developing a conventional material model whenever a new material constitutive behaviour is to be characterized.

Hurricane Loss Analysis Based on the Population-Weighted Index

This paper discusses different measures for quantifying regional hurricane loss. The main measures used in the past are normalized percentage loss and dollar value loss. In this research, we show that these measures are useful but may not properly reflect the size of the population influenced by hurricanes. A new loss measure is proposed that reflects the hurricane impact on people occupying the structure. For demonstrating the differences among these metrics, regional loss analysis was conducted for Florida. The regional analysis was composed of three modules: the hazard module stochastically modeled the wind occurrence in the region; the vulnerability module utilized vulnerability functions developed in this research to calculate the loss; and the financial module quantified the hurricane loss. In the financial module, we calculated three loss metrics for certain region. The first metric is the average annual loss (AAL) which represents the expected loss per year in percentage. The second is the average annual dollar loss (AADL) which represents the expected dollar amount loss per year. The third is the average annual population-weighted loss (AAPL) — a new measure proposed in this research. Compared to the AAL, the AAPL reflects the number of people influenced by the hurricane. The advantages of the AAPL are illustrated using three different analysis examples: 1) conventional regional loss analysis, 2) mitigation potential analysis, and 3) forecasted future loss analysis due to the change in population.

Subspace-Based Reliability Method (SBRM) for Sequential Improvement of Probability Estimation

Reliability is a critical design criterion in a product development process and it is generally used as a product quality measurement. In a virtual product development environment, reliability can be estimated by conducting a large number of simulations such as Monte Carlo simulations. However, this approach is often impractical to be implemented in an industrial application due to high computational cost. To alleviate the prohibitive cost, point estimation methods, such as FirstOrder Reliability Method (FORM) and Second-Order Reliability Method (SORM), are often used. Such methods approximate a limit state boundary with the fixed order of Taylor series expansion (i.e., linear for FORM). The accuracy of the reliability estimation from the methods depends on the fitness of the simplified limit state boundary to the actual one. Using the point estimation method, a designer is often unable to determine the error or confidence level associated with the analysis result, especially for a complex and unfamiliar problem. In order to address the issues, the Subspace-Based Reliability Method (SBRM) is proposed in this paper. In SBRM, reliability is estimated by considering the failure boundary, as well as the Most Probable Point (MPP). The Moving Least Square (MLS) method is used to avoid the pre-fixed order of failure boundary approximation. A subspace centering MPP is defined with a desired level of accuracy in the assessment result. Within the subspace, the actual integration of failure probability is performed with the approximated boundary. For the efficiency of the integration with a multi-dimensional problem, the Dimension Reduction (DR) method is utilized. By sequentially adding more simulations for the approximation, a convergence history of reliability assessments is obtained and used to check the credibility of the result. The applicability of the proposed method is demonstrated with analytical examples.

Along-Wind Response of High-Rise Buildings Subjected to Hurricane Boundary Layer Winds

AbstractRecent studies show that characteristics of hurricane surface winds are different from those of nonhurricane surface winds. The characteristics relevant to analyzing high-rise buildings inc...

Skin Performance in the Rollover Crashworthiness Analysis of Cutaway Bus

Rollovers are recognized as the most dangerous type of road accident. Among all accidents, they are the most challenging to analyze due to the complex nature of rollover accidents. The aim of this paper is to analyze the influence of the structural components, more specifically skin parts, on the safety of occupants during rollover crashes. Full scale experiments and components testing are needed to evaluate the safety of the passengers. As part of this study, full rollover test according to ECE R66 test procedure was conducted. Additionally, series of Finite Element (FE) analyses using LS-Dyna were performed to evaluate capabilities of skin parts to absorb the crash energy and its influence on the crush mechanism. Along with structural assessments, Anthropometric Test Devices (ATD) were used to evaluate the severity of the rollover crashes. Injury parameters such as Head Injury Criteria (HIC), chest acceleration, pelvic acceleration, and neck forces were measured for different rollover scenarios. The results showed that for the bus with high deformation of skin-cage structure the injury outcomes were lower than the injuries from the stiffer passenger compartment. Preliminary Results of this study are in conflict with the of UN ECE R66 safety assessment results, which indicate that stiffer buses are safer. Hence, further work needs to be carried out to find the links between an intrusion of skin and cage, and severity of injuries.

Effect of Piezoelectric Material in Mitigation of Aerodynamic Forces

In this study, piezoelectric materials were used to generate perturbations on the surface. This perturbation was used to combine upward wall motion and surface curvature. For this purpose, a Macro Fiber Composite (MFC) material was mounted on the surface of a cylindrical specimen for generating perturbation in the wind tunnel. Four different perturbation frequencies (1 Hz, 2 Hz, 3 Hz and 4 Hz) as well as the baseline specimen were tested in a low-speed wind tunnel (Re = 2.8 × 104). The MFC materials were mounted in a specimen to apply a combination of upward wall motion and surface curvature on the specimen in order to their effects on the leeward flow filed. The results showed that in the leeward flow field, for all actuation frequencies flow is bounded to a narrower width. In addition, Confinement of the flow in high actuation frequencies result in more turbulence in the leeward locations. In this case, the actuation frequencies resulted in up to 27% increase in turbulence intensity in the leeward.

Multiobjective Optimization Approach for Robust Bridge Damage Identification against Sensor Noise

One of the important goals of structural health monitoring is to identify structural damage using measured responses. However, such damage identification is sensitive to noises in the response measurements. Even a small change in the measurement may result in a significantly biased damage assessment. The goal of this paper is to expand the multiobjective optimization approach developed for robust damage identification in order to facilitate its applications to more realistic bridge damage identification problems. Specifically, a benchmark problem on highway bridges, developed under the auspices of International Association for Bridge Maintenance and Safety (IABMAS), is investigated. Various issues regarding sensor noises, multiple measurements, and loading scenarios are addressed to improve the robustness of bridge damage identification. A major finding from this study is that the stochastic process of Pareto optimal solutions obtained in a single run not only captures the actual damage locations successfully but also provides useful information such as damage-detected ratio on the potential candidates for damage to be inspected on site. Moreover, it is shown through the success, failure, and partial detection rates that the robustness of the proposed approach can be improved by using appropriate excitation scenarios and multiple sets of measurement data.

Measuring the accessibility of critical facilities in the presence of hurricane-related roadway closures and an approach for predicting future roadway disruptions

Roadway closures magnify the adverse effects of disasters on people since any type of such disruption increases the emergency response travel time (ERTT), which is of central importance for the safety and survival of the affected people. Especially in the State of Florida, high winds due to hurricanes, such as the Hurricane Hermine, lead to notable roadway disruptions and closures that compel special attention. As such, in this paper, the accessibility of emergency response facilities, such as police stations, fire stations and hospitals in the City of Tallahassee, the capital of Florida, was extensively studied using real-life data on roadway closures during Hurricane Hermine. A new metric, namely Accessibility Decrease Index, was proposed, which measures the change in ERTT before and in the aftermath of a hurricane such as Hermine. Results clearly show those regions with reduced emergency response facility accessibility and roadways under a disruption risk in the 1-week window after Hermine hit Tallahassee. City officials can pinpoint these critical locations for future improvements and identify those critical roadways, which are under a risk of disruption due to the impact of the hurricane. This information can be utilized to improve emergency response plans by improving the roadway infrastructure and providing alternative routes to public.

Regularized discriminant analysis for multi-sensor decision fusion and damage detection with Lamb waves

In this study we propose a regularized linear discriminant analysis approach for damage detection which does not require an intermediate feature extraction step and therefore more efficient in handling data with high-dimensionality. A robust discriminant model is obtained by shrinking of the covariance matrix to a diagonal matrix and thresholding redundant predictors without hurting the predictive power of the model. The shrinking and threshold parameters of the discriminant function (decision boundary) are estimated to minimize the classification error. Furthermore, it is shown how the damage classification achieved by the proposed method can be extended to multiple sensors by following a Bayesian decision-fusion formulation. The detection probability of each sensor is used as a prior condition to estimate the posterior detection probability of the entire network and the posterior detection probability is used as a quantitative basis to make the final decision about the damage.