| Bridges,as a member of large-scale engineering structure,play an important role in the transportation industry.According to “Standards for Technical Condition Evaluation of Highway Bridges” and “Specification for Inspection and Evaluation of Load-bearing Capacity of Highway Bridges”,the geometric shape of bridges can reflect the deformation of the structures.At present,the commonly used curve mode detection tools have the limitations of discrete measurement points and complicated operations.Therefore,for large-scale structures such as bridges and roads,it is of great significance to develop a continuous,fast,high-precision,time-saving and labor-saving curve mode detection system.In view of this demand,a preliminary curve mode detection system for bridges are proposed by our research group based on FOG(Fiber Optic Gyro).The curve mode detection system can realize fast,continuous and efficient curve mode detection.However,there are still have internal and external environment errors in practical applications,which would cause distortion of the measured results.To suppress the internal and external error interference,in this paper,the error suppression and compensation for the curve mode results are carried out from the perspective of data processing.The sources and characteristics of various errors are analyzed.The influence of errors on alignment is found.A variety of correction methods and error depression methods are employed,which has important guiding significance and application value for improving the curve mode detection accuracy of bridges.The main contents are following:(1)By analyzing the generation mechanisms of inherent characteristic errors of FOG,calculation errors,and operating errors,the causes and effects on curve results are summarized.By designing the experiment,the errors cause by the carrier velocity and engine vibration have been analyzed.A joint modeling simulation of the vehicle suspension system and the pavement system was carried out,and then the impact of wheelbase size on the measurement was discussed.Finally,the data is processed by using control point restrain reference,curve mode modulate correcting,and low-pass filtering,which preliminarily eliminates the effects of internal errors and improves the curve mode accuracy.(2)By studying the impact of impact noise and vibration in the engineering environment caused by obstacles on road surface,the data characteristics are analyzed.The discrete wavelet transform is employed to identify the location of noise signal.It is proposed to use a combined filtering error suppression method based on EMD and ICA to process the noise signal in the interval.The feasibility of this method was verified by designing bridge model experiments.The experimental results show that under the premise of accurately identifying the impact noise generated by uneven road surface,the composite filtering method could effectively filter out the effects.The accuracy of the actual curve mode can be improved.This method provides postprocessing solution for the FOG curve mode detection system.(3)The optical fiber gyro curve mode detection system after adding the composite filter method is applied to the practical engineering measurement to achieve continuous detection.It can obtain the location of maximum deflection and local impact noise.Through long-term monitoring of deflection changes,periodic assessment of bridge health and safety status can be achieved.The bridge data processed by the compound filtering method shows that with this method FOG curve mode detection system can effectively suppress error data and ensuring high accuracy of results.And highprecision curve correction can be realized.The obvious advantages of the optical fiber gyro curve mode detection system after adding the error suppression compound filtering method in practical engineering measurement are determined.Through data mining on the curve elevation data of multiple bridges,it is possible to establish a conversion relationship with the international roughness index without using other road surface roughness measurement tools.The road surface roughness status of multiple bridges could be obtained indirectly.It can provide relevant support for the collection of road surface roughness,and provide an auxiliary reference for curve mode detection. |
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