[ 1 ] Nadimi S, Shahriar K, Sharifzadeh M, Moarefvand P. Triaxial creep tests and back analysis of time-dependent behavior of siah bisheh cavern by 3-dimensional distinct element method. Tunnel Undergr Space Technol 2011;26(1):155‒62. 链接1

[ 2 ] Debernardi D, Barla G. New viscoplastic model for design analysis of tunnels in squeezing conditions. Rock Mech Rock Eng 2009;42(2):259‒88. 链接1

[ 3 ] Jimenez R, Recio D. A linear classifier for probabilistic prediction of squeezing conditions in Himalayan tunnels. Eng Geol 2011;121(3‒4):101‒9.

[ 4 ] Guan Z, Jiang Y, Tanabashi Y. Rheological parameter estimation for the prediction of long-term deformations in conventional tunnelling. Tunnel Undergr Space Technol 2009;24(3):250‒9. 链接1

[ 5 ] Li J, Hao H, Chen Z. Damage identification and optimal sensor placement for structures under unknown traffic-induced vibrations. J Aerosp Eng 2017;30(2): B4015001. 链接1

[ 6 ] He J, Guan X, Liu Y. Structural response reconstruction based on empirical mode decomposition in time domain. Mech Syst Signal Process 2012;28:348‒66. 链接1

[ 7 ] Lu W, Teng J, Li C, Cui Y. Reconstruction to sensor measurements based on a correlation model of monitoring data. Appl Sci 2017;7(3):243. 链接1

[ 8 ] Zhao X, Jia J, Zheng YM. Strain monitoring data restoring of large-span steel skybridge based on BP neural network. J Archit Civil Eng 2009;26(1):101‒6. Chinese.

[ 9 ] Zhigang F, Katsunori S, Qi W. Sensor fault detection and data recovery based on LS-SVM predictor. Chin J Sci Instrum 2007;28(2):193‒7. Chinese.

[10] Bao Y, Li H, Sun X, Yu Y, Ou J. Compressive sampling-based data loss recovery for wireless sensor networks used in civil structure health monitoring. Struct Health Monit 2013;12(1):78‒95. 链接1

[11] Huang Y, Wu D, Liu Z, Li J. Lost strain data reconstruction based on least squares support vector machine. Meas Control Tech 2010;29:8‒12. Chinese.

[12] Huang YW, Wu DG, Li J. Structural healthy monitoring data recovery based on extreme learning machine. Comput Eng 2011;16:241‒3. 链接1

[13] Zhang Z, Luo Y. Restoring method for missing data of spatial structural stress monitoring based on correlation. Mech Syst Signal Process 2017;91:266‒77. 链接1

[14] Chen C, Jiao S, Zhang S, Liu W, Feng L, Wang Y. TripImputor: real-time imputing taxi trip purpose leveraging multi-sourced urban data. IEEE Trans Intell Transp Syst 2018;19(10):3292‒304. 链接1

[15] Tan X, Sun X, Chen W, Du B, Ye J, Sun L. Investigation on the data augmentation using machine learning algorithms in structural health monitoring information. Struct Health Monit 2021;20(4):2054‒68. 链接1

[16] Fan G, Li J, Hao H. Lost data recovery for structure health monitoring based on convolutional neural networks. Struct Control Health Monit 2019;26(10): e2433. 链接1

[17] Jiang H, Wan C, Yang K, Ding Y, Xue S. Continuous missing data imputation with incomplete dataset by generative adversarial networks-based unsupervised learning for long-term bridge health monitoring. Struct Health Monit 2021;21(3):1093‒109. 链接1

[18] Kammer DC. Estimation of structural response using remote sensor locations. J Guid Control Dyn 1997;20(3):501‒8. 链接1

[19] Wan Z, Li S, Huang Q, Wang T. Structural response reconstruction based on the modal superposition method in the presence of closely spaced modes. Mech Syst Signal Process 2014;42(1‒2):14‒30.

[20] Iliopoulos A, Shirzadeh R, Weijtjens W, Guillaume P, Van Hemelrijck D, Devriendt C. A modal decomposition and expansion approach for prediction of dynamic responses on a monopile offshore wind turbine using a limited number of vibration sensors. Mech Syst Signal Process 2016;68‒69:84‒104.

[21] Mace B, Halkyard C. Time domain estimation of response and intensity in beams using wave decomposition and reconstruction. J Sound Vib 2000;230(3):561‒89. 链接1

[22] Li J, Law SS, Ding Y. Damage detection of a substructure based on response reconstruction in frequency domain. Key Eng Mater 2013;569‒570:823‒30.

[23] Li J, Law SS. Substructural response reconstruction in wavelet domain. J Appl Mech 2011;78(4):041010. 链接1

[24] Li J, Hao H. Substructure damage identification based on wavelet-domain response reconstruction. Struct Health Monit 2014;13(4):389‒405. 链接1

[25] Lai T, Yi TH, Li HN. Wavelet-galerkin method for reconstruction of structural dynamic responses. Adv Struct Eng 2017;20(8):1174‒84. 链接1

[26] Zhang X, Wu Z. Dual-type structural response reconstruction based on moving-window Kalman filter with unknown measurement noise. J Aerosp Eng 2019;32(4):04019029. 链接1

[27] Zhang C, Xu Y. Structural damage identification via response reconstruction under unknown excitation. Struct Control Health Monit 2017;24(8):e1953. 链接1

[28] Peng Z, Dong K, Yin H. A modal-based Kalman filter approach and OSP method for structural response reconstruction. Shock Vib 2019;5475696:1‒14. 链接1

[29] Bao Y, Beck JL, Li H. Compressive sampling for accelerometer signals in structure health monitoring. Struct Health Monit 2011;10(3):235‒46. 链接1

[30] Bao Y, Yu Y, Li H, Mao X, Jiao W, Zou Z, et al. Compressive sensing-based lost data recovery of fast-moving wireless sensing for structure health monitoring. Struct Control Health Monit 2015;22(3):433‒48. 链接1

[31] Wan HP, Ni YQ. Bayesian multi-task learning methodology for reconstruction of structure health monitoring data. Struct Health Monit 2019;18 (4):1282‒309. 链接1

[32] Kerschen G, Poncelet F, Golinval JC. Physical interpretation of independent component analysis in structural dynamics. Mech Syst Signal Process 2007;21(4):1561‒75. 链接1

[33] Hasanov A, Baysal O. Identification of an unknown spatial load distribution in a vibrating cantilevered beam from final overdetermination. J Inverse Ill-Posed Probl 2015;23(1):85‒102. 链接1

[34] Li Y, Sun L. Structural deformation reconstruction by the Penrose-Moore pseudo-inverse and singular value decomposition-estimated equivalent force. Struct Health Monit 2021;20(5):2412‒29. 链接1

[35] Shang Z, Sun L, Xia Y, Zhang W. Vibration-based damage detection for bridges by deep convolutional denoising autoencoder. Struct Health Monit 2020;20(4):1880‒903. 链接1

[36] Tan X, Wang Y, Du B, Ye J, Chen W, Sun L, et al. Analysis for full face mechanical behaviors through spatial deduction model with real-time monitoring data. Struct Health Monit 2021;21(4):1805‒18. 链接1

[37] Bani-Hani KA. Vibration control of wind-induced response of tall buildings with an active tuned mass damper using neural networks. Struct Control Health Monit 2007;14(1):83‒108. 链接1

[38] Ni Y, Li M. Wind pressure data reconstruction using neural network techniques: a comparison between BPNN and GRNN. Measurement 2016;88:468‒76. 链接1

[39] Oh BK, Glisic B, Kim Y, Park HS. Convolutional neural network-based data recovery method for structure health monitoring. Struct Health Monit 2020;19(6):1821. 链接1

[40] Zhang Y, Miyamori Y, Mikami S, Saito T. Vibration-based structural state identification by a 1-dimensional convolutional neural network. Comput Aided Civ Infrastruct Eng 2019;34(9):822‒39. 链接1

[41] Jiang K, Han Q, Du X, Ni P. Structural dynamic response reconstruction and virtual sensing using a sequence to sequence modeling with attention mechanism. Autom Constr 2021;131:103895. 链接1

[42] Bao Y, Tang Z, Li H, Zhang Y. Computer vision and deep learning‒based data anomaly detection method for structure health monitoring. Struct Health Monit 2019;18(2):401‒21. 链接1

[43] Tang Z, Chen Z, Bao Y, Li H. Convolutional neural network-based data anomaly detection method using multiple information for structure health monitoring. Struct Control Health Monit 2019;26(1):e2296. 链接1

[44] Chen C, Zhang D, Castro PS, Li N, Sun L, Li S, et al. iBOAT: isolation-based online anomalous trajectory detection. IEEE Trans Intell Transp Syst 2013;14(2):806‒18. 链接1

[45] Yang WX, Tes PW. Development of an advanced noise reduction method for vibration analysis based on singular value decomposition. NDT E Int 2003;36(6):419‒32. 链接1

[46] Žvokelj M, Zupan S, Prebil I. Non-linear multivariate and multiscale monitoring and signal denoising strategy using kernel principal component analysis combined with ensemble empirical mode decomposition method. Mech Syst Signal Process 2011;25(7):2631‒53. 链接1

[47] Calabrese L, Campanella G, Proverbio E. Noise removal by cluster analysis after long time ae corrosion monitoring of steel reinforcement in concrete. Constr Build Mater 2012;34:362‒71. 链接1

[48] Jiang X, Mahadevan S, Adeli H. Bayesian wavelet packet denoising for structural system identification. Struct Control Health Monit 2007;14(2):333‒56. 链接1

[49] Katicha SW, Flintsch G, Bryce J, Ferne B. Wavelet denoising of TSD deflection slope measurements for improved pavement structural evaluation. Comput Aided Civ Infrastruct Eng 2014;29(6):399‒415. 链接1

[50] Du B, Sun X, Ye J, Cheng K, Wang J, Sun L. GAN-based anomaly detection for multivariate time series using polluted training set. IEEE Trans Knowl Data Eng 2021:1‒13. 链接1

[51] Neerukatti RK, Hensberry K, Kovvali N, Chattopadhyay A. A novel probabilistic approach for damage localization and prognosis including temperature compensation. J Intell Mater Syst Struct 2016;27(5):592‒607. 链接1

[52] Pavlopoulou S, Worden K, Soutis C. Novelty detection and dimension reduction via guided ultrasonic waves: damage monitoring of scarf repairs in composite laminates. J Intell Mater Syst Struct 2016;27(4):549‒66. 链接1

[53] Yuan L, Fan W, Yang X, Ge S, Xia C, Foong SY, et al. Piezoelectric PAN/BaTiO3 nanofiber membranes sensor for structure health monitoring of real-time damage detection in composite. Compos Commun 2021;25:100680. 链接1

[54] Castaldo P, Jalayer F, Palazzo B. Probabilistic assessment of groundwater leakage in diaphragm wall joints for deep excavations. Tunn Undergr Space Technol 2018;71:531‒43. 链接1

[55] Liu G, Mao Z, Todd M, Huang Z. Damage assessment with state-space embedding strategy and singular value decomposition under stochastic excitation. Struct Health Monit 2014;13(2):131‒42. 链接1

[56] Nichols J, Todd M, Wait J. Using state space predictive modeling with chaotic interrogation in detecting joint preload loss in a frame structure experiment. Smart Mater Struct 2003;12(4):580‒601. 链接1

[57] Sohn H, Allen DW, Worden K, Farrar CR. Statistical damage classification using sequential probability ratio tests. Struct Health Monit 2003;2(1):57‒74. 链接1

[58] Lynch JP, Sundararajan A, Law KH, Kiremidjian AS, Carryer E. Embedding damage detection algorithms in a wireless sensing unit for operational power efficiency. Smart Mater Struct 2004;13(4):800‒10. 链接1

[59] Nair KK, Kiremidjian AS, Law KH. Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure. J Sound Vib 2006;291(1‒2):349‒68.

[60] Wu Z, Xu B, Yokoyama K. Decentralized parametric damage detection based on neural networks. Comput Aided Civ Infrastruct Eng 2002;17(3):175‒84. 链接1

[61] Yan L, Elgamal A, Cottrell GW. Substructure vibration NARX neural network approach for statistical damage inference. J Eng Mech 2013;139(6):737‒47. 链接1

[62] Gul M, Necati CF. Statistical pattern recognition for structure health monitoring using time series modeling: theory and experimental verifications. Mech Syst Signal Process 2009;23(7):2192‒204. 链接1

[63] Skarlatos D, Karakasis K, Trochidis A. Railway wheel fault diagnosis using a fuzzy-logic method. Appl Acoust 2004;65(10):951‒66. 链接1

[64] Sohn H, Kim SD, Harries K. Reference-free damage classification based on cluster analysis. Comput Aided Civ Infrastruct Eng 2008;23(5):324‒38. 链接1

[65] Kesavan KN, Kiremidjian AS. A wavelet-based damage diagnosis algorithm using principal component analysis. Struct Control Health Monit 2012;19(8):672‒85. 链接1

[66] Liu XZ, Ni YQ. Wheel tread defect detection for high-speed trains using FBGbased online monitoring techniques. Smart Struct Syst 2018;21(5):687‒94.

[67] Abdeljaber O, Avci O, Kiranyaz MS, Boashash B, Sodano H, Inman DJ. 1-d CNNs for structural damage detection: verification on a structure health monitoring benchmark data. Neurocomputing 2018;275:1308‒17. 链接1

[68] Mousavi Z, Varahram S, Ettefagh MM, Sadeghi MH, Razavi SN. Deep neural networks-based damage detection using vibration signals of finite element model and real intact state: an evaluation via a lab-scale offshore jacket structure. Struct Health Monit 2021;20(1):379‒405. 链接1

[69] Zhao J, Du B, Sun L, Zhuang F, Lv W, Xiong H. Multiple relational attention network for multi-task learning. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining; 2019 Aug 4‒8; Anchorage, AK, USA. New York City: Association for Computing Machinery; 2019. p. 1123‒31. 链接1

[70] Chen C, Liu Q, Wang X, Liao C, Zhang D. semi-Traj2Graph: identifying finegrained driving style with GPS trajectory data via multi-task learning. IEEE Trans Big Data 2021;8(6):1‒15.

[71] Tran D, Bourdev L, Fergus R, Torresani L, Paluri M. Learning spatiotemporal features with 3D convolutional networks. In: Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV); 2015 Dec 11‍‒‍18; Santiago, Chile. Danvers: Institute of Electrical and Electronics Engineers (IEEE); 2015. p. 4489‒97. 链接1

[72] LeCun Y, Kavukcuoglu K, Farabet C. Convolutional networks and applications in vision. In: Proceedings of 2010 IEEEInternational Symposium on Circuits and Systems ISCAS); 2010 May 30‒Jun 2; Paris, France. Paris: Institute of Electrical and Electronics Engineers (IEEE); 2010. p. 253‒6. 链接1

[73] Li Y, Yu R, Shahabi C, Liu Y. Diffusion convolutional recurrent neural network: data-driven traffic forecasting. In: Proceedings of 6th International Conference on Learning Representations; 2018 Apr 30‒May 3; Vancouver Convention Center, Vancouver, BC, Canada. OpenReview.net; 2017. p. 1‒16.

[74] Yu B, Yin H, Zhu Z. Spatio‒temporal graph convolutional networks: a deep learning framework for traffic forecasting. In: JérômeL, editor. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI-18); 2018 Jul 13‒19; Stockholm, Sweden. Palo Alto: AAAI Press; 2017. p. 3634‒40. 链接1

[75] Chen Y, Ye YQ, Sun BN, Lou W, Yu J. Application of model prediction technology to bridge health monitoring. J Zhejiang Univ Eng Sci 2008;42(1):157‒63.

[76] Yang N, Bai X. Forecasting structural strains from long-term monitoring data of a traditional Tibetan building. Struct Control Health Monit 2019;26(1):e2300. 链接1

[77] Solhjell IK. Bayesian forecasting and dynamic models applied to strain data from the Göta river bridge [dissertation]. Blindern: University of Oslo; 2009.

[78] Wang Y, Ni Y. Bayesian dynamic forecasting of structural strain response using structure healthmonitoring data. Struct Control HealthMonit 2020;27(8):e2575. 链接1

[79] Ching J, Chen YC. Transitional Markov chain Monte Carlo method for Bayesian model updating, model class selection, and model averaging. J of Eng Mech 2007;133(7):816‒32. 链接1

[80] Cheung SH, Beck JL. Calculation of posterior probabilities for Bayesian model class assessment and averaging from posterior samples based on dynamic system data. Comput Aided Civ Infrastruct Eng 2010;25(5):304‒21. 链接1

[81] Fei X, Lu CC, Liu K. A Bayesian dynamic linear model approach for real-time short-term freeway travel time prediction. Transp Res Part C Emerg Technol 2011;19(6):1306‒18. 链接1

[82] Fan X, Liu Y. Dynamic extreme stress prediction of bridges based on nonlinear mixed Gaussian particle filtering algorithm and structure health monitoring data. Adv Mech Eng 2016;8(6):1‒10. 链接1

[83] Dervilis N, Shi H, Worden K, Cross E. Exploring environmental and operational variations in SHM data using heteroscedastic Gaussian processes. Dyna Civil Struct 2016;2:145‒53. 链接1

[84] Worden K, Cross E. On switching response surface models, with applications to the structure health monitoring of bridges. Mech Syst Signal Process 2018;98:139‒56. 链接1

[85] Caywood MS, Roberts DM, Colombe JB, Greenwald HS, Weiland MZ. Gaussian process regression for predictive but interpretable machine learning models: an example of predicting mental workload across tasks. Front Hum Neurosci 2017;10:647. 链接1

[86] Su G, Yu B, Xiao Y, Yan L. Gaussian process machine-learning method for structural reliability analysis. Adv Struct Eng 2014;17(9):1257‒70. 链接1

[87] Prakash G, Sadhu A, Narasimhan S, Brehe JM. Initial service life data towards structure health monitoring of a concrete arch dam. Struct Control Health Monit 2018;25(1):e2036. 链接1

[88] Wang X, Yang K, Shen C. Study on MGPA-BP of gravity dam deformation prediction. Math Probl Eng 2017;2586107:1‒18. 链接1

[89] Kang F, Liu J, Li J, Li S. Concrete dam deformation prediction model for health monitoring based on extreme learning machine. Struct Control Health Monit 2017;24(10):e1997. 链接1

[90] Deng NW, Qiu FQ, Xu H. Application of BP model to data analysis of earth-rock dams. Eng J Wuhan Univ 2001;34(4):17‒20. Chinese.

[91] Kao CY, Loh CH. Monitoring of long-term static deformation data of Fei-Tsui arch dam using artificial neural network-based approaches. Struct Control Health Monit 2013;20(3):282‒303. 链接1

[92] Lam HF, Yuen KV, Beck JL. Structure health monitoring via measured ritz vectors utilizing artificial neural networks. Comput Aided Civ Infrastruct Eng 2006;21(4):232‒41. 链接1

[93] Du B, Li W, Tan X, Ye J, Chen W, Sun L. Development of load-temporal model to predict the further mechanical behaviors of tunnel structure under various boundary conditions. Tunn Undergr Space Technol 2021;116:104077. 链接1

[94] Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput 1997;9(8):1735‒80. 链接1

[95] Chen C, Zhang D, Wang Y, Huang HY. Enabling smart urban services with GPS trajectory Data. Heidelberg: Springer; 2021. 链接1

[96] Troisi R, Castaldo P. Technical and organizational challenges in the risk management of road infrastructures. J Risk Res 2022;25(6):791‒806. 链接1