تشخیص نابجای رفتار سازه با استفاده از تجزیه و تحلیل موجک

Structural health monitoring (SHM) can be defined as the continuous monitoring of a bridge's state properties, such as static and dynamic response, in order to diagnose the onset of anomalous structural behaviour. This involves measuring and evaluating the state properties and relating these to defined performance parameters. The process of measuring state properties, either continuously or periodically, produces large quantities of data and by careful analysis of these data, sudden and gradual changes in the bridge's behaviour can be identified and characterised. The ability of wavelet transforms to detect abrupt changes, gradual change beginnings and ends of events make them well suited for the analysis of bridge health monitoring data. This paper presents the application of wavelet analysis to identify events and changes in structural state in a bridge during and after its construction.

A healthytrans portation system helps to sustain industryand commerce of anycountry byensuri ng public safety and societal well-being. Bridges constitute significant and critical discrete components of such a transportation system and they are among the most expensive investment asset of anycoun try’s civil infrastructure. They also have a long service life compared with most commercial products and are rarelyreplac eable once erected. Manycoun tries have recognised the importance of maintaining the health of their bridge stocks and to this end, numerous bridge authorities have introduced bridge management systems. Most current bridge management systems are based on visual inspections in which visual inspection data are assigned condition states, which are then interpreted to assess the condition of the bridge and project its future behaviour. While inspection-based bridge management systems provide a useful platform for developing bridge repair and maintenance programmes and associated budgets, theyalso present some drawbacks. These include high manpower demands, inaccessibilityof some critical areas of the bridge during inspections and lack of information on actual in-service loading environment. As a result, some problems related to the structural performance of a bridge maygo unnoticed until theybecome serious or expensive to repair. Shortcomings of inspection-based bridge management systems, developments in signal processing tools, and availabilityof affordable instrumentation have motivated the development of instrumented monitoring systems. Numerous research efforts on instrumented bridge monitoring systems have been reported in technical papers, e.g. [1–6] to mention just a few. Most of these have focussed on developing methodologies for vibration-based structural identification and damage detection. A detailed review of some structural identification and damage detection techniques is given in [7, 8]. In this paper, attention is paid to the analysis of long-term continuous monitoring of static performance data.

Strain data from a long-term SHM system has been expressed as a multi-scale model in which each wavelet scale or set of scales is associated with variables influencing strain change. This representation allows the variables to be monitored individually. The paper demonstrates the use of wavelet analysis to detect the onset of anomalous behaviour by monitoring random events occurring on a bridge. However, the proposed procedure does not give information about the effect of these anomalous events on structural behaviour, that is, ‘did the structure experience some damage after the event?’ In a health monitoring system, such information is useful to minimise false alarms. False alarms could arise for example if a heavytruck crosses the bridge during recording, or if there is a sudden change in the weather, such as rainfall. Further investigations are ongoing on these subjects.