在恒载作用下破坏时的再分配的方法论英文文献和中文翻译

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Damage Identification Based on Dead Abstract: A new method for damage identification in large, massive civil structures is presented, which is based on the idea that dead load is redistributed when damage occurs in the structure. The method uses static strain measurements due to dead load only as input to the identification procedure. An analytical model of a fixed-fixed beam is developed in which the damage is represented by a section of reduced flexural rigidity. The damage state is determined by the location, length, and severity of the stiffness reduction. A forward analysis of the beam response is first presented to illustrate how the dead load is redistributed for different damage scenarios. The inverse problem is defined by a constrained optimization problem and is solved using a genetic algorithm. The proposed method correctly identified damage in the beam for a wide range of locations and damage severities. The identification procedure, in general, has a greater degree of success with increasing damage severity. Results show that damage is difficult to identify when it is close to the inflection point of theundamaged beam, where the dead load strain is zero. The effect of measurement noise on the ability to identify damage is investigated in the companion paper.51282

CE Database subject headings: Dead loads; Beams; Algorithms; Monitoring; Optimization; Strain; Damage.

Introduction:

Structural health monitoring may be defined as the application of advanced technologies to the automated detection and assessment of deterioration or damage in a structural system. The concept,which has received considerable attention in recent years, involves the use of advanced sensors, microprocessors, communication systems, and algorithms to automatically sense, locate,assess, diagnose, and report on the condition of a structural system. Applied to a civil structure, health monitoring may be used to detect slow gradual changes in a structure due to sustained load or environmental factors, and/or to detect rapid changes due to rare, high intensity events, such as an earthquake, hurricane, or blast. Health monitoring can be utilized on an as-needed basis, or as part of a permanent, long-term health monitoring program.

Methods for damage detection can generally be classified as either dynamic- or static-based techniques. The more popular dynamic techniques are based on the premise that when a structure is damaged, the associated change in the structure will result in a change in the natural frequencies, mode shapes, damping ratios, modal strain energies, or other dynamic characteristics of the system. By measuring one or more of these properties of the damaged structure (and in some cases the undamaged structure) the location and extent of damage could be identified. The dynamic procedures require some type of dynamic excitation; the most popular are ambient vibration, impulse response, and forced harmonic vibration. Accelerations are usually measured in the dynamic-based techniques.

A tremendous amount of work has been conducted on damage detection based on dynamic responses. Doebling et al. (1996) provides a comprehensive literature review of the work in this area. The review is updated for literature published between 1996 and 2001, in Sohn et al. (2003).

While some have found success, dynamic-based techniques are faced with a number of practical challenges when applied to large, massive, civil structures. First, it is very difficult to excite a large structure to a level that v, ill elicit changes in the signal outputs resulting from localized damage. It is often impractical and too expensive to attempt to excite the structure at all in a controlled way, and therefore, many dynamic techniques rely on ambient vibrations. Second, in an ambient vibration survey the excitation is provided by a host of uncontrolled sources, such as wind, microtrenmrs, traffic, and extraneous vibration sources. These same sources are present in a forced vibration test, but are usually not measured or accounted for in the analysis and have the effect of "contaminating" the signal outputs relative to the "known" excitation. Third, field tests have shown that the variability of the test data from replicate tests conducted on different days, due to such things as temperature and extraneous excitation sources, can be of the same order of magnitude as the changes due to the damage imposed; thus the damage could not be successfully identified (FmTar and Doebling 1997).