Dominant Factor in the Collapse of WTC-1

The behavior of the World Trade Center Tower 1 on September 11, 2001 is studied in light of experimental data on the effect of elevated temperature on mechanical properties of structural steel. It is concluded that the damage inflicted by aircraft impact on the insulation of the core framing was the dominant factor in the collapse of the structure.     

Response to Fire Exposure of the Pentagon Structural Elements

An overview of fire damage sustained by the Pentagon structural elements in the September 11, 2001, terrorist attack is provided. The fire intensity in some compartments of the affected areas inside the Pentagon was approximated to be between those of the two standard fire exposures ASTM E119 and E1529, based on the observed fire damage and estimated fuel load. Thermal analyses of the structural columns and beams were performed using the standard fire exposures to demonstrate the increased vulnerability of these structural elements once the concrete cover was lost.     

Bayesian Analysis of the Phase II IASC–ASCE Structural Health Monitoring Experimental Benchmark Data

A two-step probabilistic structural health monitoring approach is used to analyze the Phase II experimental benchmark studies sponsored by the IASC–ASCE Task Group on Structural Health Monitoring. This study involves damage detection and assessment of the test structure using experimental data generated by hammer impact and ambient vibrations. The two-step approach involves modal identification followed by damage assessment using the pre- and postdamage modal parameters based on the Bayesian updating methodology. An Expectation–Maximization algorithm is proposed to find the most probable values of the parameters. It is shown that the brace damage can be successfully detected and assessed from either the hammer or ambient vibration data. The connection damage is much more difficult to reliably detect and assess because the identified modal parameters are less sensitive to connection damage, allowing the modeling errors to have more influence on the results.     

Toughness of the Pentagon Structure

On September 11, 2001, the reinforced concrete structure of the Pentagon Building was able to resist, without collapse, the impact of a large commercial airliner despite the total loss of 26 columns and severe damage to 15 columns at the ground level. The ensuing fire and related fire-fighting activities led to the collapse of a portion of the building approximately one-half hour after the impact. In this paper, the reasons for the demonstrated toughness of the reinforced concrete structure are examined and attributed to use of spiral columns, effective splicing of reinforcing bars, strong girders, and short span lengths.     

Structural Damage Diagnosis and Assessment under Seismic Excitations

This study proposes a method of detecting, locating, and quantifying structural damage by directly using structural vibration measurements in the time domain. In this method, the coupling effect of the damage at different locations in the structure on the structural vibratory responses is eliminated by projecting these measured quantities onto some specific subspaces. As a result, the structural system, generally modeled with multiple degree of freedom, is decomposed into several independent single-degree-of-freedom (SDOF) systems, every one of which is only associated with the damage at one certain location or region. A monitor is designed as an observer to detect the structural damage related to each SDOF system. A decision-making scheme is developed to correlate the monitor’s output to the occurrence of the damage. The severity of the damage is estimated with a traditional system identification method in an iterative way. The analysis of the effects of measurement noise is also included. Numerical examples are presented to demonstrate the effectiveness of the proposed method.     

Structural Damage Detection Using Empirical Mode Decomposition: Experimental Investigation

This paper presents an experimental investigation on the applicability of the empirical mode decomposition (EMD) for identifying structural damage caused by a sudden change of structural stiffness. A three-story shear building model was constructed and installed on a shaking table with two springs horizontally connected to the first floor of the building to provide additional structural stiffness. Structural damage was simulated by suddenly releasing two pretensioned springs either simultaneously or successively. Various damage severities were produced using springs of different stiffness. A series of free vibration, random vibration, and earthquake simulation tests were performed on the building with sudden stiffness changes. Dynamic responses including floor accelerations and displacements, column strains, and spring releasing time instants were measured. The EMD was then applied to measured time histories to identify damage time instant and damage location for various test cases. The comparison of identified results with measured ones showed that damage time instants could be accurately detected in terms of damage spikes extracted directly from the measurement data by EMD. The damage location could be determined by the spatial distribution of the spikes along the building. The influence of damage severity, sampling frequency, and measured quantities on the performance of EMD for damage detection was also discussed.     

Two-Stage Structural Health Monitoring Approach for Phase I Benchmark Studies

This paper presents a two-stage structural health monitoring methodology and applies it to the Phase I benchmark study sponsored by the IASC-ASCE Task Group on Structural Health Monitoring. In the first stage, modal parameters are identified using measured structural response from the undamaged system and then from the (possibly) damaged system. In the second stage, these data are used to update a parametrized structural model of the system using Bayesian system identification. The approach allows one to obtain not only estimates of the stiffness parameters but also the probability that damage in any substructure exceeds any specified threshold expressed in terms of a fractional stiffness loss. It successfully identifies the location and severity of damage in all cases of the benchmark problem.     

Hilbert-Huang Based Approach for Structural Damage Detection

When measured data contain damage events of the structure, it is important to extract the information of damage as much as possible from the data. In this paper, two methods are proposed for such a purpose. The first method, based on the empirical mode decomposition (EMD), is intended to extract damage spikes due to a sudden change of structural stiffness from the measured data thereby detecting the damage time instants and damage locations. The second method, based on EMD and Hilbert transform is capable of (1) detecting the damage time instants, and (2) determining the natural frequencies and damping ratios of the structure before and after damage. The two proposed methods are applied to a benchmark problem established by the ASCE Task Group on Structural Health Monitoring. Simulation results demonstrate that the proposed methods provide new and useful tools for the damage detection and evaluation of structures.     

Bayesian Network Enhanced with Structural Reliability Methods: Application

The enhanced Bayesian network (eBN) methodology described in the companion paper facilitates the assessment of reliability and risk of engineering systems when information about the system evolves in time. We present the application of the eBN: (1) to the assessment of the life-cycle reliability of a structural system; (2) to the optimization of a decision on performing measurements in that structural system; and (3) to the risk assessment of an infrastructure system subject to natural hazards and deterioration of constituent structures. In all applications, observations of system performances or the hazards are made at various points in time and the eBN efficiently includes these observations in the analysis to provide an updated probabilistic model of the system at all times.     

Multivariate Statistical Approach to Structural Damage Detection

The issue of structural damage detection is addressed through an innovative multivariate statistical approach in this paper. By invoking principal component analysis, the vibration responses acquired from the structure being monitored are represented by the multivariate data of the sample principal component coefficients (PCCs). A damage indicator is then defined based on a multivariate exponentially weighted moving average control chart analysis formulation, involving special procedures to allow for the effects of the estimated parameters and to determine the upper control limits in the control chart analysis for structural damage detection applications. Also, a data shuffling procedure is proposed to remove the autocorrelation probably present in the obtained sample PCCs. This multivariate statistical structural damage detection scheme can be applied to either the time domain responses or the frequency domain responses. The efficacy and advantages of the scheme are demonstrated by the numerical examples of a five-story shear frame and a shear wall as well as the experimental example of the I-40 Bridge benchmark.