15 August 2022, Volume 62 Issue 8

    

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Intelligent Construction
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  A review of intelligent dam construction techniques

  LI Qingbin, MA Rui, HU Yu, HUANGFU Zehua, SHEN Yiyuan, ZHOU Shaowu, MA Jingang, AN Zaizhan, GUO Guangwen

  Journal of Tsinghua University(Science and Technology). 2022, 62(8): 1252-1269. https://doi.org/10.16511/j.cnki.qhdxxb.2022.25.018

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  High dam construction is continuing to develop with new requirements for intelligent dam construction. New information technology capabilities are providing paths for improved intelligent dam construction. The key to achieving safe, quality, efficient, economic, green construction projects is to integrate these new information technology capabilities into intelligent construction methods. New systems enable intelligent construction of dams and the construction of intelligent dams. This article summarizes these two paths for intelligent construction, identifies three stages in the development of intelligent construction systems for dams, and analyzes the technical characteristics, goals, theory, methods, and management models with engineering examples for each stage of the intelligent construction process. The analysis shows the relationship between intelligent dam construction and intelligent dams, the three stages of intelligent dam construction, the changes in manager thinking for solving key problems in the intelligent era, and future developments in intelligent dam construction.
  
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  Review of smart production techniques for the entire self-compacting concrete production process

  LÜ Miao, AN Xuehui, LI Pengfei, ZHANG Jingbin, BAI Hao

  Journal of Tsinghua University(Science and Technology). 2022, 62(8): 1270-1280. https://doi.org/10.16511/j.cnki.qhdxxb.2022.25.037

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  Self-compacting concrete (SCC) has good fluidity that can fill the voids without vibrations, but the concrete performance is very sensitive to material property changes. Existing SCC production methods have material quality management problems with production discontinuities leading to inaccurate material information and poor test results. New image recognition and artificial intelligence methods are enabling intelligent systems for the entire production process, including material property tests, mix design, and mixing production, and rheological property tests. Smart material property tests improve material quality management. The mix proportions can be accurately determined by intelligent mix design methods that cope with material property changes. Real-time monitoring of the mixing can optimize mix proportions during mixing. Smart rheological property tests can monitor the rheological properties of the self-compacting mixtures during mixing. This paper reviews the research on smart production for the entire SCC production process and summarizes current problems and future development prospects.
  
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  3D printing of large filled construction projects

  LIU Tianyun

  Journal of Tsinghua University(Science and Technology). 2022, 62(8): 1281-1291. https://doi.org/10.16511/j.cnki.qhdxxb.2022.25.045

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  This paper presents a 3D printing system using intelligence robots for rapid, efficient filling of large construction projects. The 3D printing system includes a construction scheduling system and a 3D assembly line that uses intelligence robots. The 3D scheduling system cuts the 3D digital design model into slices to calculate the filling material information and then plans the transport roads on the site map model for each step in the construction process. The construction robots collect the fill materials when needed and transport the fill materials with intelligent paving and rolling of the fill materials. Once each construction layer is finished, the robots send construction state information to the scheduling system. The complete filling process is then printed step by step under the control of the 3D printing scheduling system.
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  Extremely hard rock mass excavation using rock breakdown methods to assist TBM in a deep, long diversion tunnel in the Qinling Mountains

  LIU Xiaoli, SUN Huan, DONG Qinxi, XIONG Yanlin, KUMAR Nawnit, SU Yan, ZHOU Jianjun

  Journal of Tsinghua University(Science and Technology). 2022, 62(8): 1292-1301. https://doi.org/10.16511/j.cnki.qhdxxb.2022.25.035

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  Extremely hard rock with tensile strengths of more than 300 MPa were encountered in a deep, long diversion tunnel in the Qinling Mountains which significantly slowed the TBM excavation. This paper presents a coupled rock breakdown method using thermal energy and mechanical methods for extremely hard rock mass excavation without changing the TBM system. Attempts to break the extremely hard rocks included using microwaves supplemented by numerical simulations of using plasma jets to break the rocks. The present study analyzed the applicability of five kinds of rock breakdown methods. The results show that the rocks are extremely hard due to small black, polar mineral particles and the high quartz content in the rocks. The rocks could be weakened by injecting black, polar substances to improve the effect of microwave irradiation on the rocks. In addition, high voltage plasma jets were found to induce electrical-mechanical effects and thermal melting. The hob could be designed as electrodes. Flame cutting was easily used on the excavation face, but needs a thermal shield and cooling time after the cutting. Super high pressure water jets were also useful, but needed pressures of 400 MPa to cut the high tensile strength rock with excavation distances of less than 1.0 m per day.
Intelligent Prediction and Feedback
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  Inversion analysis to determine the mechanical parameters of a high arch dam and its foundation based on an IAGA-BP algorithm

  ZHUANG Wenyu, ZHANG Rujiu, XU Jianjun, YIN Liang, WEI Haining, LIU Yaoru

  Journal of Tsinghua University(Science and Technology). 2022, 62(8): 1302-1313. https://doi.org/10.16511/j.cnki.qhdxxb.2022.25.034

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  Using an inversion analysis to determine the mechanical parameters of a dam and its foundation from monitoring data is of great significance to safety evaluation. An inversion analysis method was developed based on an adaptive genetic algorithm and a BP neural network. The analysis used the weighted absolute percentage error as the objective function to determine the mechanical parameters from multi-point monitoring data and nonlinear numerical simulations. Deformation data from 25 measurement points was used to determine 11 key mechanical parameters for the dam concrete, foundation rock mass and structural plane. The results show that the inversion values are in good agreement with measured data. The inversion accuracy is improved by using the material parameters as the input layer and the deformation as the output layer. The effects of the neural network topology, objective function and the number of training samples on the inversion results was analyzed.
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  Reliability analysis of structures using polynomial chaos expansions

  ZHANG Ming, WANG Enzhi, LIU Yaoru, QI Wenbiao, WANG Dehui

  Journal of Tsinghua University(Science and Technology). 2022, 62(8): 1314-1320. https://doi.org/10.16511/j.cnki.qhdxxb.2022.25.015

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  Practical engineering issues such as design optimization, design space exploration, sensitivity analyses, and reliability analyses need many simulations. If a single simulation is very time-consuming, engineers cannot perform the thousands or even millions of simulations needed for such analyses. The polynomial chaos expansion (PCE) method is an effective method that allows analyses of complex problems. This paper introduces the mathematical theory of the PCE method and presents a structural reliability analysis example. The performance response function for the structural reliability analysis is expressed as a PCE using Hermite polynomials. A general form of the Hermite polynomial, which is suitable for use in a computer program, is used to generalize the PCE analysis program and the adaptive selection of the polynomial order. Then, the accuracy and applicability of the surrogate model are verified using structural reliability analysis examples with explicit performance functions. The results show that the model has an excellent convergence rate with higher order PCE giving higher accuracy. The examples also show that the direct use of explicit performance functions is the easiest way to investigate PCE surrogate models.
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  GA-BP artificial neural networks for predicting the seismic response of arch dams

  YU Jingchi, JIN Aiyun, PAN Jianwen, WANG Jinting, ZHANG Chuhan

  Journal of Tsinghua University(Science and Technology). 2022, 62(8): 1321-1329. https://doi.org/10.16511/j.cnki.qhdxxb.2022.25.028

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  Arch dams may be subjected to strong earthquakes during their lifecycle and their seismic response has attracted extensive attention in dam engineering. Nonlinear finite element seismic response analyses of arch dams require large amounts of computational effort. This paper presents a back propagation (BP) genetic algorithm (GA) method for predict the seismic responses of arch dams which replaces some of the finite element analysis calculations and significantly reduces the computational cost compared with the finite element method. A BP neural network was trained and validated for the Dagangshan arch dam based on 390 nonlinear dynamic response cases calculated using the finite element method with the structural response as the BP neural network output and the seismic intensity parameter, IM, as the input. The results show that the GA-BP neural network can properly predict the dam seismic response and give reasonable seismic response curves using 30% of the 390 cases for training which shows that the GA-BP neural network can save 70% of the nonlinear finite element cost.
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  Time-frequency characteristic analyses of measured and artificial seismic waves using the XGBoost algorithm

  CAO Zilong, HUANG Duruo

  Journal of Tsinghua University(Science and Technology). 2022, 62(8): 1330-1340. https://doi.org/10.16511/j.cnki.qhdxxb.2022.25.036

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  The rapid development of artificial intelligence and machine learning techniques that can predict and generalize data sets are gradually being introduced into many engineering projects. The XGBoost artificial intelligence method was used here to analyze the time-frequency characteristics of measured and artificially simulated seismic waves to resolve the two problems of the lack of data and the lack of understanding seismic waves. The advantage of the XGBoost method is that high-performance artificial intelligence methods can analyze large amounts of data that would be difficult by traditional calculational methods. The method can then be used to analyze the time-frequency domain characteristics of seismic waves. The algorithm accurately discriminated 91% of the measured and artificial SIMQKE seismic waves. Further research showed that the difference between the artificial seismic waves and the measured waves was mainly reflected in the correlation of the time-frequency domain characteristics. This study reveals the time-frequency characteristics of seismic waves that will facilitate the development of artificial seismic wave simulation methods.
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  Relationship between reservoir bank deformation and reservoir-induced earthquakes during the impounding of high arch dams

  WANG Xingwang, LIU Yaoru, LÜ Shuai, YANG Qiang

  Journal of Tsinghua University(Science and Technology). 2022, 62(8): 1341-1350. https://doi.org/10.16511/j.cnki.qhdxxb.2022.25.016

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  Reservoir bank deformation and reservoir-induced earthquakes have been observed during the impounding of arch dams. Reservoir-induced earthquakes may be precursors of widespread slope deformation, which can endanger the long-term, efficient and safe operation of arch dams. This paper presents an analysis of deformation monitoring data and seismic monitoring data before and after impounding of the Xiluodu arch dam. The results compare the reservoir bank deformation and the seismic activity variations in the reservoir area before and after impoundment which were used to analyze the mechanisms relating the reservoir bank deformation and the reservoir-induced earthquakes. The results show that the two processes are correlated with initially fast responses including hysteresis transitioning to slower responses and then to constant values. The fast response is due to the reduction of the effective stress caused by the mechanical action of the water that leads to irreversible plastic deformation and fault slip. The hysteresis is due to the decreased shear strength of the rock mass and faults due to the physicochemical action of the reservoir water, which leads to slow deformation of the rock mass and the sliding of faults. The correlation between the two phenomena provides a basis for intelligent monitoring and safety warnings of high arch dams during impounding.
Project Management
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  Information management of water conservancy project partnering

  WU Zekun, ZHANG Yakun, ZHANG Xuteng, TANG Wenzhe, QIANG Maoshan

  Journal of Tsinghua University(Science and Technology). 2022, 62(8): 1351-1356. https://doi.org/10.16511/j.cnki.qhdxxb.2022.25.027

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  Water conservancy projects involve various participants and massive information databases, so there is a need to collaboratively manage construction processes using information technologies. Survey results for Ningxia water conservancy projects showed that the optimal integration and utilization of participant resources (especially the information) through partnering is the key to improving project performance. Partnering-based construction management information systems need to be developed using information technologies, such as the Internet, big data, and artificial intelligence, to improve participant collaborative management efficiency for project delivery. The information system should be intelligent, extensible, and secure and the system development should focus on 1) intelligent information collecting, handling, and analysis; 2) participant collaborative process support; 3) timely feedback; and 4) knowledge management. This study describes the partnering mechanism and outlines the information management approach for water conservancy project delivery. The results can guide the development of information management systems and provide empirical evidence for choosing suitable information technologies.
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  Renewable energy development and multi-energy complementation, taking Qinghai as an example

  JIN Yong, MA Jiming, ZHU Shouzhen, LI Nan

  Journal of Tsinghua University(Science and Technology). 2022, 62(8): 1357-1365. https://doi.org/10.16511/j.cnki.qhdxxb.2022.25.033

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  Actively developing renewable energy sources is one important way to achieve the carbon peak and neutrality targets in China. This paper summarizes the power installed capacities of various power generation methods in China in 2020 and compares the electricity green capabilities in China and the world. This paper then describes a method to analyze the peak shaving capacity of cascade reservoirs, presents a way to integrate multiple power sources, and uses simulations of cascade hydropower stations in the upper reaches of the Yellow River to predict the monthly peak shaving capability of hydropower and other energy sources for the power grid of Qinghai Province for the target year of 2030. The monthly load balance analysis shows that wind power and photovoltaic power generation will lead to more excess, wasted electricity generation in wet years, with full use of the generated power in normal and dry years. The hourly analysis of the multiple power sources during a typical day shows that wind power and photovoltaic power production increases the peak-valley difference of the electrical loads and lead to more excess electricity that is wasted, while in winters of dry years, the electrical production and peak shaving capacity are insufficient and lead to power outsourcing which results in the abandoning of wind and photovoltaic power sources. Thus, the Qinghai power grid needs to promote the construction of power storage systems and renewable power sources. Qinghai also needs to enlarge their baseload capacity and peak shaving capacity to reduce waste and power outsourcing. The simulation method to analyze power shaving capacities of cascade reservoirs and integrating multiple energy sources may be used in other power grids and the conclusions provide a reference for developing the Qinghai electrical system.
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  Life cycle carbon emissions of water reservoir and hydroelectric projects： A case study of the Quanmutang project

  HUANG Yuequn, LIU Yaoru, XU Wenbin, LI Junfeng

  Journal of Tsinghua University(Science and Technology). 2022, 62(8): 1366-1373. https://doi.org/10.16511/j.cnki.qhdxxb.2022.25.044

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  Water reservoirs and hydroelectric projects have many functions that produce great benefits that will play an important role in the "double carbon" strategy. However, there are few studies on the life cycle carbon emissions of hydropower and reservoir projects. This study uses the Quanmutang project in Hunan Province as an example to apply life cycle assessment theory and the carbon emissions grouping calculational method which is more suitable for reservoirs and hydroelectric projects. The carbon emission calculations show that the overall carbon emissions during the construction and operating phases of the Quanmutang project are controllable. This study then considers the carbon emission characteristics to present some strategies to reduce the carbon emissions, such as design optimization, developing green building materials, accelerating intellectualization and strengthening the emission reductions.