Study on Semi-Parametric Statistical Model of Safety Monitoring of Cracks in Concrete Dams

For dams and rock foundations of ages, the actual mechanical parameters sometimes differed from the design and the experimental values. Therefore, it is necessary to carry out the inversion analysis on main physical and mechanical parameters of dams and rock foundations. However, only the integrated deformation modulus can be inversed by utilizing the conventional inversion method, and it does not meet the actual situation. Therefore, a new method is developed in this paper to inverse the actual initial zoning deformation modulus and to determine the inversion objective function for the actual zoning deformation modulus, based on the dam displacement measured data and finite element calculation results. Furthermore, based on the chaos genetic optimization algorithm, the inversion method for zoning deformation modulus of dam, dam foundation and, reservoir basin is proposed. Combined with the project case, the feasibility and validity of the proposed method are verified.

Section: Case Studymentioning

confidence: 99%

“…But for dams of ages, the actual mechanical parameters differed from the design and experiment. It is often difficult to reflect the overall mechanical behaviors of dams by utilizing the values of mechanical parameters determined by design or experiment [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Zoning Modulus Inversion Method for Concrete Dams Based on Chaos Genetic Optimization Algorithm

Zhong-ru

Huang

et al. 2015

“…Wu derived a dam's time effect displacement expression on the basis of concrete creep theory. They used the periodic function to stimulate temperature and other periodic loads and then used nonlinear least square method for parameter estimation [4,5]. They proposed the establishment of the dam crack opening degree statistic model and analysis method, the time series analysis method of horizontal displacement of dam crest, and the multiple-arch dam displacement deterministic model's theory and method.…”

Section: Introductionmentioning

confidence: 99%

Structural Safety Monitoring of High Arch Dam Using Improved ABC-BP Model

Zhu

Zhao

et al. 2016

Self Cite

The establishment of a structural safety monitoring model of a dam is necessary for the evaluation of the dam’s deformation status. The structural safety monitoring method based on the monitoring data is widely used in traditional research. On the basis of the analysis of the high arch dam’s deformation principles, this study proposes a structural safety monitoring method derived from the dam deformation monitoring data. The method first analyzes and establishes the spatial and temporal distribution of high arch dam’s safety monitoring, overcoming the standard artificial bee colony (ABC) algorithm’s shortcoming of easily falling into the local optimum by adopting the adaptive proportion and average Euclidean distance afterwards. The improved ABC algorithm is used to optimize the backpropagation (BP) neural network’s initial weight and threshold. The application example proves that ABC-BP model’s improvement method is important for the establishment of a high arch deformation safety monitoring model and can effectively improve the model’s fitting and forecasting ability. This method provides a reference for the establishment of a structural safety monitoring model of dam and provides guidance for the establishment of a forecasting model in other fields.

“…At present, the work of analyzing monitoring data and diagnosing dam status mainly relies on the monitoring model of single point. Since the Italian scholar Tonini [4] described the main factors that affected dam deformation as hydraulic press, temperature, and time, in 1956, further studies aimed at the classical monitoring model of single point have been conducted and have been widely used in practical engineering, forming the statistical model [5,6], the deterministic model [7], and the hybrid model [8]. Meanwhile, with the development of machine learning algorithms [9,10], the modeling approaches of single point have developed into the nonlinear direction.…”

Section: Introductionmentioning

confidence: 99%

Method of Fusion Diagnosis for Dam Service Status Based on Joint Distribution Function of Multiple Points

Jiang

2016

The traditional methods of diagnosing dam service status are always suitable for single measuring point. These methods also reflect the local status of dams without merging multisource data effectively, which is not suitable for diagnosing overall service. This study proposes a new method involving multiple points to diagnose dam service status based on joint distribution function. The function, including monitoring data of multiple points, can be established with t-copula function. Therefore, the possibility, which is an important fusing value in different measuring combinations, can be calculated, and the corresponding diagnosing criterion is established with typical small probability theory. Engineering case study indicates that the fusion diagnosis method can be conducted in real time and the abnormal point can be detected, thereby providing a new early warning method for engineering safety.