Structural Damage Detection Using Static And Dynamic Test Data

Structural performance deterioration will inevitablly occur because of variousfactors, such as environmental hazards, excessive forces etc. This structuraldeterioration can result in some serious accidents, which should be paid much attention,especially for those mechanical equipments or structures, which play an important rolein the national economic lifeline. Therefore, various efforts trying to estimate damagestatus of structures and to ensure structural safety through various approaches have beendone in structural engineering research. Those approaches are the so-called DamageDetection and SHM: Structural Health Monitoring. In the most general terms，structural damage can be defined as changes introduced into a system that adverselyaffects its current or future performance. Structural damage detection is to detect, locate,and assess its severity. According to the detection result，the structure reliability and thedamage are evaluated.Traditional structure reliability is ensured by design. But structural design can notguarantee reliability in the whole life period of a structure，as a number of structuresmay be dangerous owing to many factors such as that they might be inadequatelyconstructed and are unable to meet current standards, or they are aging and deterioratingdue to environmental hazards such as wind, earthquakes and excessively higher serviceloads during many years service. This damage could imperil the structure safety. How toeffectively and reliably detect the damage existence，distribution and extent is a hotresearch topic to which many engineers pay much passion.In order to study the damage diagnosis method for structures or mechanicalequipments, firstly, in this thesis, we have numerically simulated and done experimentalworks about the deformation and strain of a damaged cantilever beam under the staticand dynamic loadings. In the part of experiment, in this study, we uses apolymer/carbon nanotube nanocomposite strain sensor, invented by our group for nearly8years, to collect static strain signals of the intact and damaged beams. Moreover, weemploy PZT piezoelectric sensors to collect dynamic strain signals of the intact anddamaged beams. By using these static or dynamic sensor signals, we perform thestructural damage identification. In the part of numerical simulation, the static anddynamic responses of the intact and damaged cantilever beams under static anddynamic loadings are obtained by using a finite element software to collect the data for analysis.The data processing techniques up to now are mainly built up based on FastFournier Transform (FFT) as the core of traditional signal processing methods.Basically, they are suitable to the steady signal symptom extraction and analysis,however, relatively improper to the symptom extraction of non-steady signals which areinduced from sudden load change. Therefore, the new technology of signalprocessing—wavelet analysis is adopted in this thesis to overcome the insufficiency oftradition signal processing method.This thesis mainly has the following several contents:①In view of that the traditional strain gauge has the low sensitivity, theemployment of this sensor needs the signal bridge road or amplifier, and the cost is high,in this thesis, we use a polymer/carbon nanotube nanocomposite strain sensor, whichhas the outstanding sensitivity, and it is expected to be more sensitive to some tinydamages, resulting in effective damage monitoring or detection.②In view of the misdiagnosis caused by experimental noises and human errors,we have performed numerical simulations and have done experimental work about thedeformation and strain of a damaged cantilever beam under the static and dynamicloadings. By combing the numerical and experimental data, it is more effective to carryout the damage detection.③In view of the tradition signal processing method which is not suitable fornon-steady signal symptom extraction, wavelet analysis is adopted in signal symptomextraction for structural damage detection. The results of diagnosis example indicate thevalidity of this method.