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Characteristic of regional groundwater flow system: case modeling study in Longzici karst spring area, North China

Qiao Xiaojuan, Cheng Yu, Wang Dawei

Acta Geophysica

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2024

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Vol. 72, no. 1

323--339

EN

Karst spring water dynamic characteristics and its response to atmospheric precipitation are of great significance for water resources utilization under the background of climate change. This paper selects Longzici spring area, North China, as the study area. Based on a long series of spring water flow and precipitation data, the dynamic characteristics of spring flow were analyzed and the numerical simulation of the groundwater flow model was established. The results show that the groundwater kept the sustained decline over the past decades which is in a negative equilibrium state, with a storage variable of - 2.26 million m3/year. The sensitivity of spring flow to precipitation under different precipitation scenarios shows that the water level changes in the recharge and drainage areas are similar about (3-5 cm) and slightly larger than that in the runoff area(1.5 cm) when minimum rainfall (287.24 mm) happens. When the precipitation is at its maximum (867.66 mm), the water level change in the runoff area can reach 95 cm which is much larger than those in the recharge and discharge areas. The results indicate that Longzici karst spring has a relatively good regulation water resource capacity and the runoff area is more sensitive which plays an important role in response to climate change.

2

Mechanical properties of CFST short columns with different void ratios

Song Junxi, Han Zhuowei, Wang Dawei, Lu Xiaorui

Archives of Civil Engineering

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2023

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Vol. 69, nr 3

587--596

EN

In order to study the mechanical behavior of concrete-filled steel tube(CFST) short column with different void ratios under a certain eccentricity. A fiber model of concrete-filled steel tube section with different void heights was established. Compared with existing model test data, the axial force and flexural moment strength models of concrete-filled steel tube columns with different void ratios were established. The results show that, in the case of different void ratios, the cross-section strength envelope shows an overall contraction tendency with the increase of void ratio, and each line is basically parallel. A model for calculating the coefficient of axial load degradation was established. The Han’s flexural moment strength model of the flexural component was revised, and the strength model of concrete-filled steel tube column under eccentric compression considering void ratio was established, which provides a theoretical basis and method for the safety assessment during the operation of concrete-filled steel tube arch bridges.

3

A study on the deflection and crack layout in a hollow slab bridge

Wang Songtao, Wang Dawei

Archives of Civil Engineering

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2023

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Vol. 69, nr 3

541--555

EN

This paper conducts research based on the hollow slab members in the reconstruction and expansion project of expressways, two types of numerical finite element models with and without considering bond-slip relationship of reinforcement and concrete are established, and verified by tests. The distribution characteristics of crack spacing in reinforced concrete beams are studied. The results show that the bond-slip characteristics of reinforced concrete have little effect on the load-deflection characteristics of 8m hollow slab beam. Due to the influence of the bond-slip relationship of reinforced concrete, the load-deflection curve is partially serrated, while without considering the bond-slip relationship of reinforced concrete, the load-deflection curve is smooth. In the numerical model without considering the bond-slip characteristics, almost all damage occurs in the longitudinal direction, and the distribution characteristics of cracks can’t be accurately determined. Regardless of whether the bond-slip is considered or not, the macroscopic characteristics of the stress distribution is: smaller near the support and larger at the mid-span. As secondary flexural cracks expand, models with and without consideration of bond-slip characteristics can’t calculate crack spacing based on the stress distribution characteristics of the reinforcement.

4

Machine learning for predicting discharge fuctuation of a karst spring in North China

Cheng Shu, Qiao Xiaojuan, Shi Yaolin, Wang Dawei

Acta Geophysica

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2021

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Vol. 69, no. 1

257--270

EN

The quantitative analyses of karst spring discharge typically rely on physical-based models, which are inherently uncertain. To improve the understanding of the mechanism of spring discharge fuctuation and the relationship between precipitation and spring discharge, three machine learning methods were developed to reduce the predictive errors of physical-based groundwater models, simulate the discharge of Longzici spring’s karst area, and predict changes in the spring on the basis of long time series precipitation monitoring and spring water fow data from 1987 to 2018. The three machine learning methods included two artifcial neural networks (ANNs), namely multilayer perceptron (MLP) and long short-term memory–recurrent neural network (LSTM–RNN), and support vector regression (SVR). A normalization method was introduced for data preprocessing to make the three methods robust and computationally efcient. To compare and evaluate the capability of the three machine learning methods, the mean squared error (MSE), mean absolute error (MAE), and root-mean-square error (RMSE) were selected as the performance metrics for these methods. Simulations showed that MLP reduced MSE, MAE, and RMSE to 0.0010, 0.0254, and 0.0318, respectively. Meanwhile, LSTM–RNN reduced MSE to 0.0010, MAE to 0.0272, and RMSE to 0.0329. Moreover, the decrease in MSE, MAE, and RMSE was 0.0397, 0.1694, and 0.1991, respectively, for SVR. Results indicated that MLP performed slightly better than LSTM–RNN, and MLP and LSTM–RNN performed considerably better than SVR. Furthermore, ANNs were demonstrated to be prior machine learning methods for simulating and predicting karst spring discharge.