## 2 **Introduction**  
A large concrete aqueduct with a total length of 1083.6 m is a key structure of a water supply project in eastern China. It has 21 spans with a 51.6 m equal span length. The structural feature of the aqueduct is shown in Figure 1, and its main load-bearing components are comprised of arch trusses (concrete design strength C50), bent frames (C40) and moment frame supports (C40). The aqueduct was built in 2006, and after 17 years of operation without any safety monitoring measures, the management now plans to install an SHM on it.  
The dynamic elastic modulus (\(E_{\_}{d}\)) of concrete is of great interest in hydraulic structures. In the Chinese hydraulic concrete design code, the durability of concrete is evaluated by the reduction of \(E_{\_}{d}\), which can also provide appropriate dynamic parameters for the seismic analysis [1, 2]. For high-, normal- and low-strength concrete, the ratios of the dynamic-to-static elastic modulus (\(E_{\_}{d}/E_{\_}{c}\)) are around 1.2, 1.3 and 1.4, respectively [1]. According to the dynamic equilibrium equation, the natural vibration frequencies of a concrete structure are directly related to its concrete \(E_{\_}{d}\), which can be obtained from examining the dynamic responses of the structure on site.  
Aqueducts are structurally similar, with bridges. Vibration-based (dynamic) tests have been considered as an effective approach to evaluate the structural health of bridges [3]. Many previous studies focused on analyzing modal parameters (frequencies and shapes) through numerical models combined with field test results to identify and locate structural damages in bridges [4; 5; 6; 7; 8]. Analyses of modal parameters were performed to assess significant negative effects on bridges due to structural rehabilitation [9; 10], and some researchers conducted ambient and vehicle-induced vibration tests for a real bridge subject to various deliberately designed damage conditions [11]. Besides full-scale applications, vibration tests were also used to evaluate local damages in decks and slab-girder connectors of bridges [12; 13; 14] and assist in optimizing the boundary constraints of the structural FE models [15]. In recent years, more researchers have employed machine learning, especially artificial neural networks, in the damage detection of bridges by exploiting in situ experimental and numerical analytical data [16; 17; 18; 19], and particularly due to its maturity in both theory and performance as well as flexibility in network architecture, BP (backpropagation) neural network has been widely used [20; 21; 22; 23]. Efforts have also been made in applying convolutional neural networks (CNNs) to visually reveal structural defects in bridge girders [24; 25].  
The purpose of this study was to determine the general concrete \(E_{d}\) status for the main load-bearing components (arch trusses, bent frames and moment frame supports) of the aqueduct, thus to set an evaluation baseline before the SHM implementation. So with the long-term concrete \(E_{d}\) data accumulated from the subsequent monitoring, it is possible to track the development of the health status of this old aqueduct. This task is completed by the application of in situ dynamic tests and the BP neural network trained with analytical data from a full-scale numerical model (see Figure 2), as discussed below.  
Figure 1: Main structural feature of the arch-truss aqueduct.