Abstract
A steel reinforced concrete arch of a bridge girder has been subjected to static and fatigue tests. The aim of this study is the application of guided waves in non-destructive diagnostics of civil engineering structures and early damage detection. Two piezoelectric transducers were mounted at a distance of 1 m to monitor area of the arch keystone. After every 500 000 cycles the signals of elastic waves have been measured and the girder visual examination was carried out. It turned out that both the load magnitude and the appearance of cracks have affected the signal changes. The obtained signal database has been used to train artificial neural networks and establish a diagnostic system. The results of the conducted tests have showed good sensitivity of anomaly detection and satisfying accuracy of load identification.
Highlights
The main problems of the bridge construction is a significant reduction in the durability of structures caused by corrosion of steel and concrete
The approach used in this work is based on a simple transformation which converts the measurement data set into the space of principal components (PCA)
It is worth mentioning that clear differences in signals were visible after each of the series of fatigue tests when compared to the same load levels
Summary
The main problems of the bridge construction is a significant reduction in the durability of structures caused by corrosion of steel and concrete. A guided wave excited in the structure, propagates through the structural elements and changes its character (direction, amplitude, etc.) in case of damage appearance and growth (i.e. cracks, delaminations, corrosion, impact damage, yielding zone) This technique has gained considerable popularity in the monitoring of aircraft structures. The approach used in this work is based on a simple transformation which converts the measurement data set into the space of principal components (PCA) It is a kind of lossy compression, but it usually works well in damage detection tasks [13]. An important issue is the correct design of the diagnostic system, sensitive to changes associated with the possible damage appearance and its growth In this case, the inference is usually supported by expert systems, probabilistic methods and artificial intelligence algorithms. The third chapter presents the idea of the developed diagnostic system, supplemented with exemplary results of its operation
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