Mechanical analysis for the full face of tunnel structure is crucial to maintain stability, which is a challenge in classical analytical solutions and data analysis. Along this line, this study aims to develop a spatial deduction model to obtain the full-faced mechanical behaviors through integrating mechanical properties into pure data-driven model. The spatial tunnel structure is divided into many parts and reconstructed in a form of matrix. Then, the external load applied on structure in the field was considered to study the mechanical behaviors of tunnel. Based on the limited observed monitoring data in matrix and mechanical analysis results, a double-driven model was developed to obtain the full-faced information, in which the data-driven model was the dominant one and the mechanical constraint was the secondary one. To verify the presented spatial deduction model, cross-test was conducted through assuming partial monitoring data are unknown and regarding them as testing points. The well agreement between deduction results with actual monitoring results means the proposed model is reasonable. Therefore, it was employed to deduct both the current and historical performance of tunnel full face, which is crucial to prevent structural disasters.
SchröderKEckeWKautzM, et al.An approach to continuous on-site monitoring of contact forces in current collectors by a fiber optic sensing system. Opt Lasers Eng2013; 51: 172–179.
2.
AnnamdasVGMBhallaSSohCK. Applications of structural health monitoring technology in Asia. Struct Health Monit2017; 16: 324–346.
3.
SalazarFMoránRToledoMÁ, et al.Data-based models for the prediction of dam behaviour: a review and some methodological considerations. Arch Comput Methods Eng2017; 24: 1–21.
4.
Chuan LiCZhaoY-gHao LiuH, et al.Combined interrogation using an encapsulated fbg sensor and a distributed brillouin tight buffered fiber sensor in a tunnel. Struct Health Monit2010; 9: 341–346.
5.
Ariznavarreta-FernándezFGonzález-PalacioCMenéndez-DíazA, et al.Measurement system with angular encoders for continuous monitoring of tunnel convergence. Tunnelling Underground Space Techn2016; 56: 176–185.
6.
LiCHouSLiuY, et al.Analysis on the crown convergence deformation of surrounding rock for double-shield TBM tunnel based on advance borehole monitoring and inversion analysis. Tunnelling Underground Space Techn2020; 103: 103513.
7.
SimeoniLZaneiL. A method for estimating the accuracy of tunnel convergence measurements using tape distometers. Int J Rock Mech Mining Sci2009; 46: 796–802.
8.
YangJ-PChenW-ZLiM, et al.Structural health monitoring and analysis of an underwater TBM tunnel. Tunnelling Underground Space Techn2018; 82: 235–247.
9.
TanXChenWWangL, et al.Settlement behaviors investigation for underwater tunnel considering the impacts of fractured medium and water pressure. J Mar Georesources Geotechnology2020; 38: 1–10.
10.
WangL-yChenW-zTanX-y, et al.Evaluation of mountain slope stability considering the impact of geological interfaces using discrete fractures model. J Mountain Sci2019; 16: 2184–2202.
11.
WangLChenWTanX, et al.The impact of various crack geometrical parameters on stress field over tip under different mixed loading conditions and inclination angles. Theor Appl Fracture Mech2019; 102: 239–254.
12.
LiXYanZWangZ, et al.A progressive model to simulate the full mechanical behavior of concrete segmental lining longitudinal joints. Eng Structures2015; 93: 97–113.
13.
CaoRHCaoPLinH, et al.Mechanical behavior of an opening in a jointed rock-like specimen under uniaxial loading: experimental studies and particle mechanics approach. Arch Civil Mech Eng2018; 18: 198–214.
14.
YeJ.SunL.DuB., et al.Co-prediction of multiple transportation demands based on deep spatio-temporal neural network. In: Proceedings, The 25th ACM SIGKDD international conference on knowledge discovery and data mining, Anchorage, AK, 4-8 August 2019, pp. 305–313.
15.
LaiGChangWCYangY, et al.Modeling long-and short-term temporal patterns with deep neural networks. In Proceedings, The 41st international ACM SIGIR conference on research and development in information retrieval, Ann Arbor, MI, 8-12 July 2018, pp. 95–104.
16.
YinHCuiBZhouX, et al.Joint modeling of user check-in behaviors for real-time point-of-interest recommendation. J ACM Trans Inf Syst2016; 35: 1631–1640.
17.
SalimiARostamiJMoormannC. Application of rock mass classification systems for performance estimation of rock TBMs using regression tree and artificial intelligence algorithms. Tunnelling Underground Space Techn2019; 92: 103046.
18.
WangPLiuYQinZ, et al.A novel hybrid forecasting model for PM10 and SO2 daily concentrations. Sci Total Environ2015; 505: 1202–1212.
19.
PoloczekJTreiberNAKramerO. KNN regression as geo-imputation method for spatio-temporal wind data. In Proceedings, international joint conference SOCO’14-CISIS’14-ICEUTE’14, Bilbao, Spain, 25-27 June 2014, 185–193
20.
AydilekIBArslanA. A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm. Inf Sci2013; 233: 25–35.
21.
GaoGYuYYangM, et al.Multi-scale patch based representation feature learning for low-resolution face recognition. Appl Soft Comput2020; 90: 106183.
22.
WangYWNiYQWangX. Real-time defect detection of high-speed train wheels by using Bayesian forecasting and dynamic model. Mech Syst Signal Process2020; 139: 106654.
23.
QinMDuZZhangF, et al.A matrix completion-based multiview learning method for imputing missing values in buoy monitoring data. Inf Sci2019; 487: 18–30.
24.
ZhuYNiY-QJinH, et al.A temperature-driven MPCA method for structural anomaly detection. Eng Structures2019; 190: 447–458.
25.
XuSZhangCZhangJ. Bayesian deep matrix factorization network for multiple images denoising. Neural Networks2020; 123: 420–428.
26.
GloaguenJ-RCanALagrangeM, et al.Road traffic sound level estimation from realistic urban sound mixtures by Non-negative Matrix Factorization. Appl Acoust2019; 143: 229–238.
27.
LiangJXiaoDWangM, et al.Low-complexity privacy preserving scheme based on compressed sensing and non-negative matrix factorization for image data. Opt Lasers Eng2020; 129: 106056.
28.
TanXChenWWuG, et al.A structural health monitoring system for data analysis of segment joint opening in an underwater shield tunnel. Struct Health Monit2019; 19: 1032–1050.
29.
TengYYaoYQiS, et al.A novel framework for the NMF methods with experiments to unmixing signals and feature representation. J Comput Appl Math2019; 362: 205–218.
30.
Ludeña-ChoezJChoquehuanca-ZevallosJJMayhua-LópezE. Sensor nodes fault detection for agricultural wireless sensor networks based on NMF. Comput Electro Agric2019; 161: 214–224.
