Method for separating bridge deflection temperature effect from long-term deflection

Abstract

The invention discloses a method for separating a bridge deflection temperature effect from a long-term deflection. The method comprises the following steps of separating deterministic components and random disturbance components of a time sequence within N hours by utilization of a local tangent space alignment method; performing multi-scale data fusion processing on the deterministic components in a section in units of hour, so as to obtain first fusion data; separating a time sequence of the first fusion data by utilization of an IHSSM (Improved Harrison and Stevens Seasonal Model), so as to separate out a daily temperature effect and a first trend term; performing the multi-scale data fusion processing on the separated first trend term in units of day, so as to obtain second fusion data; dividing the second fusion data into a plurality of segments, composing data, in the same position, in the segments into a plurality of data groups, building an IHSSM for each data group, so as to separate out a second season term and a second trend term, and respectively composing to form a year temperature effect and the long-term deflection. The method disclosed by the invention has the advantages that all response components of a deflection monitoring signal can be accurately acquired, and a basis for diagnosing bridge structure diseases and estimating bridge working states and the like is provided.

Description

technical field [0001] The invention relates to the field of bridge monitoring research, in particular to a method for separating temperature effects of bridge deflection and long-term deflection. Background technique [0002] Deflection is a crucial parameter in bridge safety evaluation, the effect of load and environment, and the variation of structural materials can be expressed through the change of deflection. Accurately obtaining each response component of the deflection monitoring signal is the premise and basis for correctly diagnosing bridge structural disease, accurately evaluating its working state, and analyzing the mechanism of disease occurrence. [0003] In the actual monitoring data of bridge deflection, the seasonal characteristics, trend characteristics and irregular changes contained in it cannot be directly observed, so the influence of the temperature field on the deflection cannot be directly studied from it, and it can only be decomposed by artificial ...

AI Technical Summary

Problems solved by technology

This method relies heavily on human experience, resulting in inaccurate data

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