Abstract

Maintenance continues for structure’s life cycle, which usually costs a lot. Inspection and/or monitoring are widely implemented to investigate the conditions of the structures. Existing databases are sometimes referred to for understanding the performance of structure with inspection/monitoring data. Since structural performance is related to various irregularly time-shifted factors, it is complicated to analyze a database efficiently. To improve the situation, the key data selection (KDS) method was proposed to extract key data from a database in this paper. The KDS method is first introduced in the context of conventional maintenance procedure. Afterwards, the detailed principle, implementation procedure, and possible applications of the KDS method are explained. Verification is performed with the data from the natural vibration of a bridge girder produced by vehicles (normal conditions) and from the lateral displacement of a bridge girder during a typhoon (accidental conditions). The results showed that the method can lessen the amount of data without changing their tendency and keep the error within an acceptable range. As a result, the KDS method is simple, reliable, and capable of reducing data under normal and accidental conditions.

摘要

维护在结构的生命周期中持续进行，这通常会花费很多。广泛实施检查和/或监测以调查结构的状况。有时会参考现有数据库来了解具有检查/监控数据的结构的性能。由于结构性能与各种不规则的时移因素有关，因此有效地分析数据库很复杂。为了改善这种情况，本文提出了关键数据选择（KDS）方法从数据库中提取关键数据。KDS 方法首先是在常规维护程序的背景下引入的。随后，对KDS方法的详细原理、实现过程和可能的应用进行了说明。使用来自车辆产生的桥梁的自然振动（正常情况）和台风期间桥梁的横向位移（意外情况）的数据进行验证。结果表明，该方法可以在不改变数据趋势的情况下减少数据量，并将误差保持在可接受的范围内。因此，KDS 方法简单、可靠，并且能够在正常和意外情况下减少数据。