Mô hình dự đoán biến dạng đập cải tiến dựa trên máy học cực trị với việc xem xét các đặc tính vật lý và đặc tính hồi tiếp của chuỗi biến dạng

Journal of Civil Structural Health Monitoring - Tập 12 - Trang 1173-1190 - 2022
Zhijian Cai1, Jia Yu1, Wenlong Chen1, Jiajun Wang1, Xiaoling Wang1, Hui Guo1
1State Key Laboratory of Hydraulic Engineering Simulation and Safety, School of Civil Engineering, Tianjin University, Tianjin, People’s Republic of China

Tóm tắt

Mô hình hóa chính xác và dự đoán biến dạng của đập góp phần vào phân tích an toàn của đập. Nghiên cứu về mô hình hóa biến dạng đập dựa trên dữ liệu đang nhận được sự chú ý ngày càng tăng. Tuy nhiên, hầu hết các mô hình đã thiết lập không thể xem xét toàn diện các đặc điểm vật lý (như sự thay đổi xu hướng không thể đảo ngược, sự thay đổi theo chu kỳ và sự thay đổi ngẫu nhiên) và các đặc tính hồi tiếp của chuỗi biến dạng. Do đó, một mô hình dự đoán biến dạng đập dựa trên máy học cực trị được cải tiến đã được đề xuất. Các tham số của máy học cực trị đã được tối ưu hóa bằng cách sử dụng thuật toán đàn sứa cải tiến. Để xác định các đặc điểm vật lý, phương pháp phân rã theo mùa và theo xu hướng sử dụng Loess đã được sử dụng để phân tách chuỗi biến dạng thành các thành phần xu hướng, chu kỳ và phần dư. Để xác định các đặc tính hồi tiếp, lý thuyết tái cấu trúc không gian pha đã được áp dụng để giải quyết vấn đề lựa chọn độ trễ thời gian của chuỗi biến dạng với các đặc điểm hỗn loạn. Mô hình được đề xuất đã được sử dụng để dự đoán biến dạng của một đập bê tông ở Trung Quốc. Hơn nữa, mô hình đã vượt trội hơn các phương pháp thay thế khác, do đó cung cấp một giải pháp mới cho việc dự đoán biến dạng đập.

Từ khóa

#biến dạng đập #máy học cực trị #tối ưu hóa #đặc tính vật lý #đặc tính hồi tiếp #phân rã xu hướng

Tài liệu tham khảo

Wu ZR, Su HZ, Guo HQ (2008) Risk assessment method of major unsafe hydroelectric project. Sci China Ser E Technol Sci 51:1345–1352 Zhang JY, Li Y, Xuan GX et al (2009) Overtopping breaching of cohesive homogeneous earth dam with different cohesive strength. Sci China Ser E Technol Sci 52:3024–3029 Gu CS, Su HZ, Wang SW (2016) Advances in calculation models and monitoring methods for long-term deformation behavior of concrete dams. J Hydroelectr Eng 35:1–14 Li Y, Bao T, Shu X et al (2021) A hybrid model integrating principal component analysis, fuzzy C-means, and gaussian process regression for dam deformation prediction. Arab J Sci Eng 46:4293–4306 Shao CF, Gu CS, Yang M et al (2018) A novel model of dam displacement based on panel data. Struct Control Heal Monit 25:1–13 Li DY, Zhou YC, Gan XQ (2011) Research on multiple points deterministic displacement monitoring model of concrete arch dam. J Hydraul Eng 42:981–986 Ribeiro LS, Wilhelm VE, Faria ÉF et al (2019) A comparative analysis of long-term concrete deformation models of a buttress dam. Eng Struct 193:301–307 Wei BW, Yuan DY, Li HK, Xu ZK (2019) Combination forecast model for concrete dam displacement considering residual correction. Struct Heal Monit 18:232–244 Wei BW, Liu B, Yuan DY et al (2021) Spatiotemporal hybrid model for concrete arch dam deformation monitoring considering chaotic effect of residual series. Eng Struct 228:111488 Liu X, Kang F, Ma C, Li H (2021) Concrete arch dam behavior prediction using kernel-extreme learning machines considering thermal effect. J Civ Struct Heal Monit 11:283–299 Mata J, De CAT, Da CJS (2014) Constructing statistical models for arch dam deformation. Struct Control Heal Monit 21:423–437 Su HZ, Li X, Yang BB, Wen ZP (2018) Wavelet support vector machine-based prediction model of dam deformation. Mech Syst Signal Process 110:412–427 Zhang Z, Gu CS, Bao TF et al (2010) Application analysis of empirical mode decomposition and phase space reconstruction in dam time-varying characteristic. Sci China Technol Sci 53:1711–1716 Li YT, Bao TF, Gong J et al (2020) The prediction of dam displacement time series using STL, extra-trees, and stacked LSTM neural network. IEEE Access 8:94440–94452 Li MC, Shen Y, Ren QB, Li H (2019) A new distributed time series evolution prediction model for dam deformation based on constituent elements. Adv Eng Informatics 39:41–52 Cao EH, Bao TF, Gu CS et al (2020) A novel hybrid decomposition-ensemble prediction model for dam deformation. Appl Sci 10:5700 Cleveland RB, Cleveland WS, McRae JE, Terpenning I (1990) STL: a seasonal-trend decomposition procedure based on loess. J Off Stat 6:3–73 Luo XG, Yuan XH, Zhu S et al (2019) A hybrid support vector regression framework for streamflow forecast. J Hydrol 568:184–193 Xiong T, Li CG, Bao YK (2018) Seasonal forecasting of agricultural commodity price using a hybrid STL and ELM method: Evidence from the vegetable market in China. Neurocomputing 275:2831–2844 Su HZ, Wen ZP, Chen ZX, Tian SG (2016) Dam safety prediction model considering chaotic characteristics in prototype monitoring data series. Struct Heal Monit 15:639–649 Wei BW, Yuan DY, Xu ZK, Li LH (2018) Modified hybrid forecast model considering chaotic residual errors for dam deformation. Struct Control Heal Monit 25:e2188 Tian ZS, Zhang XN, Zhu QL et al (2010) Study of Bp neural network model to dam deformation monitoring. In: 2010 Sixth International Conference on Natural Computation, vol 4. IEEE, Yantai, Shandong, China, pp 1856–1859 Kao CY, Loh CH (2013) Integral resonant control scheme for cancelling human-induced vibrations in light-weight pedestrian structures. Struct Control Heal Monit 20:282–303 Mata J (2011) Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models. Eng Struct 33:903–910 Kang F, Liu J, Li JJ, Li SJ (2017) Concrete dam deformation prediction model for health monitoring based on extreme learning machine. Struct Control Heal Monit 24:e1997 Cheng JT, Xiong Y (2017) Application of extreme learning machine combination model for dam displacement prediction. Procedia Comput Sci 107:373–378 Kang F, Liu X, Li J (2020) Temperature effect modeling in structural health monitoring of concrete dams using kernel extreme learning machines. Struct Heal Monit 19:987–1002 Ren Q, Li M, Kong R et al (2021) A hybrid approach for interval prediction of concrete dam displacements under uncertain conditions. Eng Comput. https://doi.org/10.1007/s00366-021-01515-3 Shu X, Bao T, Li Y et al (2021) VAE-TALSTM: a temporal attention and variational autoencoder-based long short-term memory framework for dam displacement prediction. Eng Comput. https://doi.org/10.1007/s00366-021-01362-2 Xi W, Yang J, Song J, Qu X (2020) Deep learning model of concrete dam deformation prediction based on CNN. IOP Conf Ser Earth Environ Sci. https://doi.org/10.1088/1755-1315/580/1/012042 Xu Y, Zhang MQ, Ye LL et al (2018) A novel prediction intervals method integrating an error & self-feedback extreme learning machine with particle swarm optimization for energy consumption robust prediction. Energy 164:137–146 Karami H, Karimi S, Bonakdari H, Shamshirband S (2018) Predicting discharge coefficient of triangular labyrinth weir using extreme learning machine, artificial neural network and genetic programming. Neural Comput Appl 29:983–989 Meruane V (2016) Online sequential extreme learning machine for vibration-based damage assessment using transmissibility data. J Comput Civ Eng 30:04015042 Jeddi S, Sharifian S (2020) A hybrid wavelet decomposer and GMDH-ELM ensemble model for Network function virtualization workload forecasting in cloud computing. Appl Soft Comput J 88:105940 Liu H, Mi X, Li Y (2018) An experimental investigation of three new hybrid wind speed forecasting models using multi-decomposing strategy and ELM algorithm. Renew Energy 123:694–705 Li H, Xu Q, He Y, Deng J (2018) Prediction of landslide displacement with an ensemble-based extreme learning machine and copula models. Landslides 15:2047–2059 Zhang B, Tan R, Lin CJ (2021) Forecasting of e-commerce transaction volume using a hybrid of extreme learning machine and improved moth-flame optimization algorithm. Appl Intell 51:952–965 Matias T, Souza F, Araújo R, Antunes CH (2014) Learning of a single-hidden layer feedforward neural network using an optimized extreme learning machine. Neurocomputing 129:428–436 Eshtay M, Faris H, Obeid N (2018) Improving extreme learning machine by competitive swarm optimization and its application for medical diagnosis problems. Expert Syst Appl 104:134–152 Zhang Y, Chen X, Liao R et al (2021) Research on displacement prediction of step-type landslide under the influence of various environmental factors based on intelligent WCA-ELM in the Three Gorges Reservoir area. Nat Hazards 107:1709–1729 Samanta IS, Rout PK, Mishra S (2020) Power quality events recognition using S-transform and wild goat optimization-based extreme learning machine. Arab J Sci Eng 45:1855–1870 Yang Y, Tao Z, hang, Qian C, et al (2021) A hybrid robust system considering outliers for electric load series forecasting. Appl Intell. https://doi.org/10.1007/s10489-021-02473-5 Mirjalili S, Gandomi AH, Mirjalili SZ et al (2017) Salp Swarm Algorithm: a bio-inspired optimizer for engineering design problems. Adv Eng Softw 114:163–191 Faris H, Mirjalili S, Aljarah I et al (2020) Salp swarm algorithm: theory, literature review, and application in extreme learning machines. Springer, Cham Cover TM, Thomas JA (2007) Elements of information theory. Wiley-Blackwell, New Jersey Cellucci CJ, Albano AM, Rapp PE (2005) Statistical validation of mutual information calculations: comparison of alternative numerical algorithms. Phys Rev E 71:066208 Cao LY (1997) Practical method for determining the minimum embedding dimension of a scalar time series. Phys D Nonlinear Phenom 110:43–50 Alan W, Jack BS, Swinney HL, Vastano JA (1985) Determining Lyapunov exponents from a time series. Phys D Nonlinear Phenom 16(3):285–317 Guo ZH, Zhao WG, Lu HY, Wang JZ (2012) Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model. Renew Energy 37:241–249 Sun GQ, Chen T, Wei ZN et al (2016) A carbon price forecasting model based on variational mode decomposition and spiking neural networks. Energies 9:54 Bender M, Simonovic SP (1994) Decision-support system for long-range stream flow forecasting. J Comput Civ Eng 8:20–34 Shi YH, Eberhart Russell (1998) A Modified Particle Swarm Optimizer. In: 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence. IEEE, Anchorage, AK, USA, pp 69–73 Wang GG, Guo LH, Gandomi AH et al (2014) Chaotic Krill Herd algorithm. Inf Sci (NY) 274:17–34 Bin HG, Zhu QY, Siew CK (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501 Bui K-TT, Tien Bui D, Zou J et al (2018) A novel hybrid artificial intelligent approach based on neural fuzzy inference model and particle swarm optimization for horizontal displacement modeling of hydropower dam. Neural Comput Appl 29:1495–1506 Deng SH, Wang XL, Zhu YS et al (2019) Hybrid grey wolf optimization algorithm-based support vector machine for groutability prediction of fractured rock mass. J Comput Civ Eng 33:04018065 Hu J, Ma F (2021) Comparison of hierarchical clustering based deformation prediction models for high arch dams during the initial operation period. J Civ Struct Heal Monit 11:897–914