표제지
국문요약
Abstract
목차
제1장 서론 17
1.1. 연구의 배경 17
1.2. 연구의 목적 및 방법 19
1.3. 논문의 구성 21
제2장 기존 연구 동향 22
2.1. 터널 유지관리 평가체계 및 계측현황 22
2.1.1. 국내외 터널 유지관리 평가체계 22
2.1.2. 국내외 터널 계측 현황 31
2.2. 빔-스프링 모델 34
2.2.1. 빔-스프링 모델 일반 사항 34
2.2.2. 빔-스프링 모델 연구 동향 42
2.3. 지반공학분야에서의 역해석 44
2.3.1. 역해석 기법 분류 45
제3장 직접법 역해석 적용을 위한 최적화 알고리즘의 개선 48
3.1. 최적화 알고리즘 개요 48
3.2. 차분진화 알고리즘과 타부서치 알고리즘 결합을 통한 최적화 알고리즘 개선 50
3.2.1. 차분진화 알고리즘 50
3.2.2. 타부 서치 알고리즘 57
3.2.3. 개선된 차분진화 알고리즘 60
3.3. 개선된 알고리즘의 성능 평가 63
3.3.1. 이론식을 통한 DE-TS 알고리즘 성능 평가 63
3.3.2. 수치해석을 통한 DE-TS 알고리즘 성능 평가 70
3.4. 소결 82
제4장 축소모형실험과 수치해석을 통한 안정성 평가 83
4.1. 빔-스프링 기반의 역해석 모델 구축 84
4.2. 축소모형실험 86
4.2.1. 추가 하중 재하 시험 89
4.2.2. 국부 열화 시험 94
4.3. 소결 102
제5장 터널 디지털 트윈 모델의 구조적 안정성 분석을 위한 빔-스프링 모델 구축 103
5.1. 빔-스프링 기반 모델 구축 개요 103
5.2. 모델링 및 수치해석 106
5.2.1. 2-링 빔-스프링 모델 비교검토 106
5.2.2. 터널 디지털 트윈 모델의 구조적 안정성 분석을 위한 빔-스프링 모델 장대화 112
5.2.3. 수치해석을 통한 구축된 디지털 트윈 안정성 해석 모델 검증 115
5.3. 소결 133
제6장 결론 135
6.1. 연구 결과 135
① 다변수 역해석을 위한 차분진화 알고리즘의 개선 135
② 변위 기반 적합도 함수의 적용성 검토 136
③ 터널 디지털 트윈 모델의 구조적 안정성 분석을 위한 빔-스프링 모델 적용성 검토 136
6.2. 연구의 기대효과 137
6.3. 연구의 한계점 및 향후 연구에 대한 제언 138
참고문헌 140
Table 2.1. Tunnel condition evaluation items 23
Table 2.2. Evaluation criteria for overall inspection of japan railway tunnel 26
Table 2.3. Evaluation criteria for external load change of Japan Road Tunnel 27
Table 2.4. Evaluation criteria for deterioration of Japan Road Tunnel 28
Table 2.5. Evaluation criteria for leakage of Japan Road Tunnel 29
Table 2.6. Concrete structure damage classification 30
Table 2.7. Real-time monitoring-based mid-term to long-term data items 32
Table 2.8. Formula for calculation of the coefficient of subgrade reaction 37
Table 3.1. Input parameters for analytical solution 66
Table 3.2. The range of target variables 67
Table 3.3. Back analysis result and error rate 69
Table 3.4. Properties of concrete wall 72
Table 3.5. Properties of soldier piles 73
Table 3.6. Properties of Tiebacks 73
Table 3.7. Properties of soil layers 73
Table 3.8. Range of target variables 76
Table 3.9. Results of case 1 77
Table 3.10. Results of case 2 79
Table 3.11. Results of case 3 80
Table 3.12. Results of case 4 81
Table 4.1. The Specification of scaled model test 88
Table 4.2. Optimization algorithm parameters 89
Table 4.3. Result of additional load test 91
Table 4.4. Result of back analysis 92
Table 4.5. Comparison of displacement between model test and back analysis 93
Table 4.6. Measured p-wave velocity and degradation rate 96
Table 4.7. Result of local degradation test 99
Table 4.8. Result of back analysis 100
Table 5.1. Material properties 107
Table 5.2. Segment lining properties 107
Table 5.3. Coefficient of subgrade reaction, rotational and Shear spring stiffness 107
Table 5.4. Digital twin process 113
Table 5.5. Simulation phase of numerical analysis 117
Table 5.6. Optimization algorithm parameters 124
Table 5.7. Results of DE-TS based back analysis 125
Table 5.8. Convergence at ring #15 125
Table 5.9. Member force of the #15 ring in the digital twin model 129
Table 5.10. Member force of digital twin model in state B 131
Fig. 1.1. Conceptual diagram of tunnel digital twin model 20
Fig. 2.1. Classification according to tunnel lining construction method 23
Fig. 2.2. The procedure for comprehensive evaluation of facilities 24
Fig. 2.3. Double ring beam-spring model with radial springs simulating ground, and joint springs simulating longitudinal and circumferential joints 35
Fig. 2.4. Beam-spring model for TBM segmental linings 36
Fig. 2.5. Schematic diagram of Muir Wood's formula 38
Fig. 2.6. Rotational spring calculation condition 39
Fig. 2.7. The procedure of forward analysis and back analysis 45
Fig. 3.1. The procedure of DE/rand/1 of 2D space 51
Fig. 3.2. The procedure of DE/best/2 of 2D space 52
Fig. 3.3. The procedure of DE/best/1 of 2D space 53
Fig. 3.4. The procedure of DE/best/2 of 2D space 53
Fig. 3.5. The procedure of DE/rand/2 of 2D space 54
Fig. 3.6. The procedure of DE/current-to-best/1 of 2D space 54
Fig. 3.7. The flowchart of differential evolution algorithm 56
Fig. 3.8. The flowchart of tabu search algorithm 59
Fig. 3.9. Conceptual diagram of algorithm process 61
Fig. 3.10. The flowchart of DE-TS 62
Fig. 3.11. Material behavior models 63
Fig. 3.12. A circular opening in an infinite medium 64
Fig. 3.13. Back analysis result and error rate 69
Fig. 3.14. Geometry of numerical analysis model 71
Fig. 3.15. Excavation step and analysis case 71
Fig. 3.16. Numerical analysis model 72
Fig. 3.17. Measurement points of numerical model 75
Fig. 3.18. Fitness value according to generation of case 1 78
Fig. 3.19. Variables range of case 1 78
Fig. 3.20. Fitness value according to generation of case 2 79
Fig. 3.21. Fitness value according to generation of case 3 80
Fig. 3.22. Fitness value according to generation of case 4 81
Fig. 4.1. The flowchart of Beam-spring model combined with back analysis algorithm 85
Fig. 4.2. Schematic diagram of scaled model test 87
Fig. 4.3. Scaled model test 88
Fig. 4.4. Displacement load graph of additional load test 90
Fig. 4.5. Result of additional load test(State B) 91
Fig. 4.6. Comparison of model test and back analysis results 93
Fig. 4.7. Schematic diagram of local degradation test 95
Fig. 4.8. Local degradation of scaled model 96
Fig. 4.9. Displacement-load graph of local degradation test 98
Fig. 4.10. Result of local degradation test 99
Fig. 4.11. Comparison of model test and back analysis results 101
Fig. 5.1. Local stiffness matrix of beam element 104
Fig. 5.2. System stiffness matrix of beam-spring model 105
Fig. 5.3. Analysis condition and pressure distribution 106
Fig. 5.4. Numerical model and schematic diagram 108
Fig. 5.5. Shear force diagram of the front ring in FEM and beam-spring model 109
Fig. 5.6. Bending moment diagram of the front ring in FEM and beam-spring model 110
Fig. 5.7. Shear force diagram in beam-spring model 111
Fig. 5.8. Bending moment diagram in beam-spring model 111
Fig. 5.9. Digital twin process of tunnel maintenance 114
Fig. 5.10. Analysis condition and pressure distribution for digital twin model 116
Fig. 5.11. Numerical analysis model assuming a physical model 117
Fig. 5.12. Conceptual diagram of digital twin model 118
Fig. 5.13. Convergence at measured point 1 120
Fig. 5.14. Convergence at measured point 2 121
Fig. 5.15. Convergence at measured point 3 122
Fig. 5.16. Additional load imposed on tunnel physical model 123
Fig. 5.17. Target variables range of back analysis 124
Fig. 5.18. Convergence at measured point 1 126
Fig. 5.19. Convergence at measured point 2 127
Fig. 5.20. Convergence at measured point 3 128
Fig. 5.21. Shear force diagram of digital twin models 130
Fig. 5.22. Bending moment diagram of digital twin models 130
Fig. 5.23. Shear force diagram of digital twin models in state B 132
Fig. 5.24. Bending moment diagram of digital twin models in state B 132