Statistical Model for Dam-Settlement Prediction and Structural-Health Assessment

Deformation forecasting of a hydropower dam by hybridizing a long short‐term memory deep learning network with the coronavirus optimization algorithm

“…As mentioned above, in this work, we employed the measured data at the PV6 point (see Figure 1) for nearly 22 years, from January 23, 1998, to September 5, 2019, with 260 measured cycles, retrieved on a monthly basis. Since machine learning models are based on time‐series measured data only, without considering the physical characteristics of the dam materials in the modeling (Salazar et al., 2016), therefore, to forecast the dam deformation, it is necessary to determine its influencing factors.…”

“…When the monitoring time‐series data is long enough, statistical methods, that is, hydrostatic–seasonal–time (Sigtryggsdóttir et al., 2018), have more advantages than deterministic methods in terms of more straightforward function form and calculating speed (Shao et al., 2017; Stojanovic et al., 2013; Wei et al., 2020). However, in some cases, it is impossible to obtain enough time‐series data, so the reliability of the methods is not guaranteed.…”

“…Recently, machine learning, that is, neural networks (Kao & Loh, 2013; Mata, 2011; Ranković et al., 2012; Rodríguez et al., 2019; Salazar et al., 2017), artificial immune algorithm (Xi et al., 2011), support vector machine (SVM; Ranković et al., 2014; Salazar et al., 2015; Su et al., 2017; Tabari & Sanayei, 2018; Wei et al., 2020; Zheng et al., 2013), wavelet SVM (Su et al., 2018), RF and boosted regression tree (Salazar et al., 2016), GP (Kang & Li, 2020), multivariate adaptive regression splines (Salazar et al., 2015), adaptive neural fuzzy inference system (Bui et al., 2018; Ranković et al., 2012; Taormina & Chau, 2015), multi‐block‐based diagnosis method (Qin et al., 2017), and signal‐residual amendment (Wei et al., 2020), have successfully forecasted dam displacement with great accuracy results.…”

In situ measurement of rockfill dam settlement using fiber optic gyroscope monitoring system

“…Rockfill dams mainly consist of rock particles and impervious materials that are usually made of concrete face slab, central clay core, and asphaltic concrete core. Over the past few decades, rockfill dams are frequently used in water conservancy and hydropower projects because of the advantages of simple construction technology, cost‐effectiveness, effective utilization of local materials, excellent adaptability to topographic and geological conditions, and resistance to earthquake loading 1–6 . The height of rockfill dams is continually increasing and even reaches 300 m, such as Shuibuya dam (233 m), Houziyan dam (223.5 m), Jiangpinghe dam (219 m), Lianghekou dam (295 m), and Shuangjiangkou dam (314 m) in China, Bakun dam (203.5 m) in Malaysia, La Yesca dam (205 m) in Mexico, and Campos Novos dam (202 m) in Brazil 7–9 .…”

“…Over the past few decades, rockfill dams are frequently used in water conservancy and hydropower projects because of the advantages of simple construction technology, cost-effectiveness, effective utilization of local materials, excellent adaptability to topographic and geological conditions, and resistance to earthquake loading. [1][2][3][4][5][6] The height of rockfill dams is continually increasing and even reaches 300 m, such as Shuibuya dam (233 m), Houziyan dam (223.5 m), Jiangpinghe dam (219 m), Lianghekou dam (295 m), and Shuangjiangkou dam (314 m) in China, Bakun dam (203.5 m) in Malaysia, La Yesca dam (205 m) in Mexico, and Campos Novos dam (202 m) in Brazil. [7][8][9] The safety of the dam during construction and operation is significantly concerned because dam collapse is a great threat to the life and property of downstream people.…”