深埋硬巖隧道圍巖參數概率反演方法

吳忠廣; 吳順川

doi:10.13374/j.issn2095-9389.2019.01.008

深埋硬巖隧道圍巖參數概率反演方法

doi: 10.13374/j.issn2095-9389.2019.01.008

吳忠廣^{1, 2, ,},
吳順川^{1, 3}

1.
北京科技大學土木與資源工程學院, 北京 100083
2.
交通運輸部科學研究院標準與計量研究中心, 北京 100029
3.
昆明理工大學國土資源學院, 昆明 650093

基金項目:

國家重點研發計劃專項資助項目 2017YFC08053003

國家自然科學基金資助項目 51174020

詳細信息

通訊作者:
吳忠廣, E-mail: kinliwu@163.com

中圖分類號: TU452
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- 文章訪問數: 981
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出版歷程
- 收稿日期: 2018-05-29
- 刊出日期: 2019-01-01

Probabilistic back analysis method for determining surrounding rock parameters of deep hard rock tunnel

WU Zhong-guang^{1, 2
, ,},
WU Shun-chuan^{1, 3}

1.
School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Research Center for Standards and Metrology, China Academy of Transportation Sciences, Beijing 100029, China
3.
Faculty of Land Resource Engineering, Kunming University of Science and Technology, Kunming 650093, China

More Information

Corresponding author: WU Zhong-guang, E-mail: kinliwu@163.com

摘要

摘要: 在貝葉斯理論框架下, 提出了一種基于多源數據融合的深埋硬巖隧道圍巖參數概率反演方法.首先, 分析硬巖隧道常用的啟裂-剝落界限本構模型中圍巖單軸抗壓強度、啟裂強度與抗壓強度比及抗拉強度三個參數不確定性來源, 確定其概率統計特征; 其次, 利用粒子群算法優化多輸出支持向量機, 建立反映反演參數與隧道監測數據間非線性映射關系的智能響應面; 最后, 結合貝葉斯分析方法構建概率反演模型, 運用馬爾科夫鏈蒙特卡洛模擬算法實現了圍巖參數的動態更新.將該方法應用到某深埋硬巖隧道中, 利用反演的圍巖參數計算隧道拱頂下沉點、周邊收斂點變化值及開挖損傷區深度, 與監測數據吻合較好.結果表明, 該方法可以實現圍巖多參數快速概率反演, 更新后的參數可用于硬巖隧道施工安全風險評估與結構可靠性設計.
- 硬巖隧道 /
- 概率反演 /
- 多源數據融合 /
- 貝葉斯理論 /
- 多輸出支持向量機
Abstract: A large number of tunnel projects are being constructed or will be constructed in the mountainous areas of western China. However, they are several safety challenges in the construction of deep hard rock tunnels because of the complex topographic and geological conditions, strong geological tectonic activities, large burial depth, and high in situ stress level. Uncertainty of tunnel wall parameters is one of main factors that contribute to tunnel construction risk. The traditional deterministic back analysis method cannot reflect the uncertainty characteristics of tunnel wall parameters; therefore, within the framework of Bayesian theory, a probabilistic back analysis method based on integrating multi-source monitoring information was proposed for determining the surrounding rock parameters of deep hard rock tunnel. First, the uncertainty sources of three parameters——uniaxial compressive strength (UCS), crack initiation stress to UCS ratio, and tensile strength for the widely used damage initiation and spalling limit approach——were analyzed, and their probabilistic statistical characteristics were determined. Second, a multi-output support vector machine (MSVM) was optimized by particle swarm optimization (PSO) algorithm, and an intelligent response surface model was established to reflect the nonlinear mapping relationship between back-analyzed parameters and field monitoring data. Last, by combination with the Bayesian (B) analysis method, the B-PSO-MSVM model was established, and surrounding rock parameters were dynamically updated by applying the Markov Chain Monte Carlo simulation algorithm. The method was applied to a deep hard rock tunnel, and parameters from probabilistic back analysis were utilized to calculate the point change of the tunnel vault settlement and peripheral displacement convergence as well as the depth of excavation damage zones, and the results agreed well with the actual monitoring data. It is shown that this method can be used to back analyze multi parameters of surrounding rock quickly and probabilistically, and parameters updated can be applied for risk assessment in construction safety and structural reliability design for the hard rock tunnel.
- hard rock tunnel /
- probabilistic back analysis /
- multi-source information fusion /
- Bayesian theory /
- multi-output support vector machine

HTML全文

圖 1 B-PSO-MSVM計算流程

Figure 1. Calculation flowchart of B-PSO-MSVM method

下載: 全尺寸圖片幻燈片

圖 2 算例模型

Figure 2. Example model

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圖 3 拱頂下沉點P₀

Figure 3. Vault settlement Point P₀

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圖 4 周邊收斂點P₁

Figure 4. Peripheral displacement convergence Point P₁

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圖 5 開挖損傷區深度

Figure 5. EDZ depths

下載: 全尺寸圖片幻燈片

圖 6 P₀數值模擬與多輸出支持向量機計算值比較圖

Figure 6. Comparison of P₀ obtained by numerical simulation and MSVM calculation

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圖 7 P₁數值模擬與多輸出支持向量機計算值比較圖

Figure 7. Comparison of P₁ obtained by numerical simulation and MSVM calculation

下載: 全尺寸圖片幻燈片

圖 8 EDZ數值模擬與多輸出支持向量機計算值比較圖

Figure 8. Comparison of EDZ obtained by numerical simulation and MSVM calculation

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圖 9 三個參數軌跡圖

Figure 9. Trace plot of three parameters

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圖 10 三個參數柱狀圖

Figure 10. Histograms of three parameters

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圖 11 UCS更新后參數值與初始值比較圖

Figure 11. Comparison of UCS between the updated value and original value

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圖 12 CI/UCS更新后參數值與初始值比較圖

Figure 12. Comparison of CI/UCS between the updated value and original value

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圖 13 T更新后參數值與初始值比較圖

Figure 13. Comparison of T between the updated value and original value

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表 1 DISL模型參數取值表

Table 1. DISL model input parameters

參數	峰值參數	殘余參數
a	0.25	0.75
s	(CI/UCS)^1/a	0.001
m	s(UCS/\|T\|)	6~12

下載: 導出CSV

表 2 巖體力學參數

Table 2. Rock mass mechanical parameters

參數	取值
地質強度指標, GSI	90
巖石單軸抗壓強度, UCS/MPa	logN (110, 20)
啟裂強度/抗壓強度, CI/UCS	N (0.403, 0.022)
巖石抗拉強度, T/MPa	N (-5.7, 1.4)
殘余m值, m_res	8
巖石彈性模量, E/GPa	19
泊松比, ν	0.12
注：N(ε₁，σ₁)指服從均值為ε₁，標準差為σ₁的正態分布；logN(ε₂，σ₂)指服從均值為ε₂，標準差為σ₂的對數正態分布.

下載: 導出CSV

表 3 訓練樣本集

Table 3. Set of training samples

試驗序號	樣本輸入			樣本輸出
試驗序號	UCS/ MPa	CI/ UCS	T/ MPa	P₀/ mm	P₁/ mm	EDZ/ m
1	50	0.3722	-6.54	13.5	13.5	3.808
2	54	0.414	-3.46	12	12	3.263
3	58	0.4558	-8.78	15	13.5	2.966
4	60	0.359	-5.42	9.5	12	2.963
5	62	0.3986	-2.2	9	12	2.898
6	66	0.4382	-7.66	8.95	12	2.066
7	70	0.3458	-4.3	10.5	13.5	2.948
8	74	0.3854	-9.62	8.5	12	2.052
9	78	0.425	-6.4	1.35	9.5	0.556
10	80	0.4646	-3.18	8.8	12	1.929
11	82	0.37	-8.5	9.5	12	2.027
12	86	0.4118	-5.14	8.5	12	1.842
13	90	0.4514	-2.06	7.45	10.5	1.791
14	94	0.3546	-7.38	12	14	2.252
15	98	0.3942	-4.02	8.8	12	1.825
16	100	0.436	-9.34	12	12	1.788
17	102	0.3414	-5.14	9.5	12	1.827
18	106	0.381	-2.9	10.5	12	1.787
19	110	0.4206	-8.22	12	10.5	1.378
20	114	0.4602	-5	8.55	10.5	1.236
21	118	0.3678	-1.78	22	11.5	1.423
22	120	0.4074	-7.1	8.75	10.5	1.774
23	122	0.447	-3.74	10.5	10.5	1.367
24	126	0.3502	-9.2	18	12	1.785
25	130	0.392	-5.98	6.3	10.5	1.375
26	134	0.4338	-2.62	2	10	0.718
27	138	0.337	-7.94	15	12	1.399
28	140	0.3766	-4.86	8.1	12	1.772
29	142	0.4162	-1.5	7.65	10.5	1.111
30	146	0.458	-6.82	9.6	10	0.868
31	150	0.3634	-3.6	12	10.5	1.235
32	154	0.403	-9.06	15	10.5	1.282
33	158	0.4426	-5.7	4.35	10	0.675
34	160	0.348	-2.34	8.55	10.5	0.866
35	162	0.3898	-7.8	8.95	10.5	1.123
36	166	0.4294	-4.58	2	9.5	0.674
37	170	0.469	-9.9	3.5	9	0.556

下載: 導出CSV

表 4 參數更新后計算結果表

Table 4. Results obtained by updated parameters

試驗序號	樣本輸出			備注
試驗序號	P₀/mm	P₁/mm	EDZ/m	備注
1	10	11	1.7	監測值^[39]
2	8.95	12	1.785	數值模擬
3	14.76	12.82	1.75	多輸出支持向量機計算

下載: 導出CSV

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