Integrated approach to diagnose structural behaviour of dam

Abstract

The objectives of this study are to: (1) develop an approach for extracting representative information from dam behaviour data and (2) diagnose deformation and crack opening behaviour by integrating techniques in transfer entropy (TE), chaos theory and wavelet transform (WT). First, TE is employed to map the information flow between measuring points to select the representative measuring point. The phase space reconstruction of observation data on dam behaviour is implemented, where lag time τ and embedding dimension m are identified by the statistical C–C method. The non-linear structural dynamical behaviour of the dam is then quantified in the form of the largest Lyapunov exponent representing system chaoticity. In addition, a moving window formula is employed to scan along the observation data to ensure an on-line continuous diagnosis. WT may be employed to assist in detecting the time where significant changes occur. A simple 4 degrees of freedom spring-mass-damper (K-M-C) system is employed to illustrate the performance of the proposed approach. The approach is then applied to an actual dam using monitoring data. From the deformation and crack opening behaviour, it is concluded that the studied dam is performing with no significant changes in behaviour over time.

HIGHLIGHTS

• Transfer entropy is applied to map the information flow between measuring points.

• The representative information on structural behaviour of an actual dam is identified.

• TE and chaos theory are combined to diagnose the dam behaviour.

• A moving window is adopted to facilitate online statistical diagnosis.

Keywords:

• Dam structural behaviour
• transfer entropy
• chaos theory
• online statistical diagnosis
• wavelet transform

Data availability statement

The data that support the findings of this study are available from the corresponding author, B Dai, upon reasonable request.

Disclosure statement

The authors declare that they have no conflict of interest.

Acknowledgement

We thank Dr. Lin Cheng from Xi’an University of Technology for technical help.

Additional information

Funding

The writers are grateful for the financial support from National Key R&D Program of China (Grant Number: 2022YFC3005405), National Natural Science Foundation of China (Grant Number: 51979176), Scientific Research Fund of Nanjing Hydraulic Research Institute (Grant Numbers: Y723003, Y721006, Y721007), Advanced Technology Promotion Project of the Ministry of Water Resources (Grant Numbers: St722001ZT1, St722002), Reservoir Dam Safety and Management Innovation Team (Grant Number: Y722003).