The tunnel collapse is one of the most frequent and harmful geological hazards during the construction of highway rock tunnels. As for reducing the occurrence probability of tunnel collapse, a new dynamic risk assessment methodology for the tunnel collapse was established, which combines the Cloud model (CM), the Membership function, and the Bayesian network (BN). During the preparation phase, tunnel collapse risk factors are identified and an index system is constructed. Then, the proposed novel assessment method is used to evaluate the probability of tunnel collapse risk for on-site construction. The probability of tunnel collapse risk in the dynamic process of construction can provide real-time guidance for tunnel construction. Moreover, a typical case study of the Yutangxi tunnel is performed, which belongs to the Pu-Yan Highway Project (Fujian, China). The results show that the dynamic evaluation model is well validated and applied. The risk value of tunnel collapse in a construction cycle is predicted successfully, and on-site construction is guided to reduce the occurrence of tunnel collapse. Besides, it also proves the feasibility of the dynamic evaluation method and its application potential.
Citation: Bo Wu, Weixing Qiu, Wei Huang, Guowang Meng, Jingsong Huang, Shixiang Xu. Dynamic risk evaluation method for collapse disasters of drill-and-blast tunnels: a case study[J]. Mathematical Biosciences and Engineering, 2022, 19(1): 309-330. doi: 10.3934/mbe.2022016
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The tunnel collapse is one of the most frequent and harmful geological hazards during the construction of highway rock tunnels. As for reducing the occurrence probability of tunnel collapse, a new dynamic risk assessment methodology for the tunnel collapse was established, which combines the Cloud model (CM), the Membership function, and the Bayesian network (BN). During the preparation phase, tunnel collapse risk factors are identified and an index system is constructed. Then, the proposed novel assessment method is used to evaluate the probability of tunnel collapse risk for on-site construction. The probability of tunnel collapse risk in the dynamic process of construction can provide real-time guidance for tunnel construction. Moreover, a typical case study of the Yutangxi tunnel is performed, which belongs to the Pu-Yan Highway Project (Fujian, China). The results show that the dynamic evaluation model is well validated and applied. The risk value of tunnel collapse in a construction cycle is predicted successfully, and on-site construction is guided to reduce the occurrence of tunnel collapse. Besides, it also proves the feasibility of the dynamic evaluation method and its application potential.
Throughout the paper, we work over an algebraically closed field
Σk=Σk(C,L)⊆Pr |
of
Assume that
σk+1:Ck×C⟶Ck+1 |
be the morphism sending
Ek+1,L:=σk+1,∗p∗L, |
which is a locally free sheaf of rank
Bk(L):=P(Ek+1,L) |
equipped with the natural projection
H0(Bk(L),OBk(L)(1))=H0(Ck+1,Ek+1,)=H0(C,L), |
and therefore, the complete linear system
βk:Bk(L)⟶Pr=P(H0(C,L)). |
The
It is clear that there are natural inclusions
C=Σ0⊆Σ1⊆⋯⊆Σk−1⊆Σk⊆Pr. |
The preimage of
Theorem 1.1. Let
To prove the theorem, we utilize several line bundles defined on symmetric products of the curve. Let us recall the definitions here and refer the reader to [2] for further details. Let
Ck+1=C×⋯×C⏟k+1times |
be the
Ak+1,L:=Tk+1(L)(−2δk+1) |
be a line bundle on
The main ingredient in the proof of Theorem 1.1 is to study the positivity of the line bundle
Proposition 1.2. Let
In particular, if
In this section, we prove Theorem 1.1. We begin with showing Proposition 1.2.
Proof of Proposition 1.2. We proceed by induction on
Assume that
rz,k+1,L:H0(Ck+1,Ak+1,L)⟶H0(z,Ak+1,L|z) |
is surjective. We can choose a point
rz,k+1,L:H0(Ck+1,Ak+1,L)⟶H0(z,Ak+1,L|z) |
where all rows and columns are short exact sequences. By tensoring with
rz,k+1,L:H0(Ck+1,Ak+1,L)⟶H0(z,Ak+1,L|z) |
in which we use the fact that
Since
Lemma 2.1. Let
Proof. Note that
B′/A′⊗A′A′/m′q=B′/(m′qB′+A′)=B′/(m′p+A′)=0. |
By Nakayama lemma, we obtain
We keep using the notations used in the introduction. Recall that
αk,1:Bk−1(L)×C⟶Bk(L). |
To see it in details, we refer to [1,p.432,line –5]. We define the relative secant variety
Proposition 2.2. ([2,Proposition 3.15,Theorem 5.2,and Proposition 5.13]) Recall the situation described in the diagram
αk,1:Bk−1(L)×C⟶Bk(L). |
Let
1.
2.
3.
As a direct consequence of the above proposition, we have an identification
H0(Ck+1,Ak+1,L)=H0(Σk,IΣk−1|Σk(k+1)). |
We are now ready to give the proof of Theorem 1.1.
Proof of Theorem 1.1. Let
b:˜Σk:=BlΣk−1Σk⟶Σk |
be the blowup of
b:˜Σk:=BlΣk−1Σk⟶Σk |
We shall show that
Write
γ:˜Σk⟶P(V). |
On the other hand, one has an identification
ψ:Ck+1⟶P(V). |
Also note that
ψ:Ck+1⟶P(V). |
Take an arbitrary closed point
α−1(x)⊆π−1k(x″)∩β−1k(x′). |
However, the restriction of the morphism
[1] | G.-H. Zhang, W. Chen, Y.-Y. Jiao, H. Wang, C.-T. Wang, A failure probability evaluation method for collapse of drill-and-blast tunnels based on multistate fuzzy Bayesian network, Eng. Geol., 276 (2020), 105752. doi: 10.1016/j.enggeo.2020.105752. |
[2] |
X. Wu, H. Liu, L. Zhang, M. Skibniewski, Q. Deng, J. Teng, A dynamic Bayesian network based approach to safety decision support in tunnel construction, Reliab. Eng. Syst. Saf., 134 (2015), 157-168. doi: 10.1016/j.ress.2014.10.021. doi: 10.1016/j.ress.2014.10.021
![]() |
[3] | W. Liu, E. J. Chen, E. Yao, Y. Wang, Y. Chen, Reliability analysis of face stability for tunnel excavation in a dependent system, Reliab. Eng. Syst. Saf., 206 (2021), 107306. doi: 10.1016/j.ress.2020.107306. |
[4] |
H. Nezarat, F. Sereshki, M. Ataei, Ranking of geological risks in mechanized tunneling by using Fuzzy Analytical Hierarchy Process (FAHP), Tunn. Undergr. Space Technol., 50 (2015), 358-364. doi: 10.1016/j.tust.2015.07.019. doi: 10.1016/j.tust.2015.07.019
![]() |
[5] | J. Wu, Y. Bai, W. Fang, R. Zhou, G. Reniers, Khakzad. N, An Integrated Quantitative Risk Assessment Method for Urban Underground Utility Tunnels, Reliab. Eng. Syst. Saf., 213 (2021), doi: 107792.10.1016/j.ress.2021.107792. |
[6] | R. Zhou, A risk assessment model of a sewer pipeline in an underground utility tunnel based on a Bayesian network, Tunn. Undergr. Space Technol., 10 (2020). doi: 10.1016/j.tust.2020.103473. |
[7] | C. Chen, L. Zhang, R. L. K. Tiong, A novel learning cloud Bayesian network for risk measurement, Applied Soft Computing, 87 (2020), 105947. doi: 10.1016/j.asoc.2019.105947. |
[8] | M. M. G. Elbarkouky, A. R. Fayek, N. B. Siraj, N. Sadeghi, Fuzzy Arithmetic Risk Analysis Approach to Determine Construction Project Contingency, J Constr Eng Manage, 142 (2016), 04016070. doi: 10.1061/(ASCE)CO.1943-7862.0001191. |
[9] |
X. Li, X. Li, Y. Su, A hybrid approach combining uniform design and support vector machine to probabilistic tunnel stability assessment, Struct. Saf., 61 (2016), 22-42. doi: 10.1016/j.strusafe.2016.03.001. doi: 10.1016/j.strusafe.2016.03.001
![]() |
[10] | M. Z. Naghadehi, M. Thewes, A. A. Lavasan, Face stability analysis of mechanized shield tunneling: An objective systems approach to the problem, Eng. Geol., 262 (2019), 105307. doi: 10.1016/j.enggeo.2019.105307. |
[11] |
L. Zhang, X. Wu, J. S. Miroslaw, J. Zhong, Y. Lu, Bayesian-network-based safety risk analysis in construction projects, Reliab. Eng. Syst. Saf., 131 (2014), 29-39. doi: 10.1016/j.ress.2014.06.006. doi: 10.1016/j.ress.2014.06.006
![]() |
[12] |
M. Holický, J. Marková, M. Sýkora, Forensic assessment of a bridge downfall using Bayesian networks, Eng. Fail. Anal., 30 (2013), 1-9. doi: 10.1016/j.engfailanal.2012.12.014. doi: 10.1016/j.engfailanal.2012.12.014
![]() |
[13] |
S. J. Lee, M. C. Kim, P. H. Seong, An analytical approach to quantitative effect estimation of operation advisory system based on human cognitive process using the Bayesian belief network, Reliab. Eng. Syst. Saf., 93 (2008), 567-577. doi: 10.1016/j.ress.2007.02.004. doi: 10.1016/j.ress.2007.02.004
![]() |
[14] | X. Yu, J. Bo, Y. Tang, Study on fundamental conception of risk and major geotechnical project risk, Journal of natural disasters, (2019), 110-118. (in Chinese) |
[15] | D. Li, C. Liu, W. Gan, A new cognitive model: Cloud model. Int. J. Intell. Syst., 24 (2009), 357-375. doi: 10.1002/int.20340. |
[16] |
N. Li, X. Feng, R. Jimenez, Predicting rock burst hazard with incomplete data using Bayesian networks, Tunn. Undergr. Space Technol., 61 (2017), 61-70. doi: 10.1016/j.tust.2016.09.010. doi: 10.1016/j.tust.2016.09.010
![]() |
[17] | T. L. Saaty, How to handle dependence with the analytic hierarchy process. Math. Model., 9 (1987), 369-376. doi: 10.1016/0270-0255(87)90494-5. |
[18] | K.-C. Hyun, Risk analysis using fault-tree analysis (FTA) and analytic hierarchy process (AHP) applicable to shield TBM tunnels, Tunn. Undergr. Space Technol., 9 (2015). |
[19] | Y. Zhang, B. Li, J. Cui, Method of target threat assessment based on cloudy Bayesian network, Comput. Sci, 40 (2013), 127-131. (in Chinese) |
[20] | T. Aven, The risk concept—historical and recent development trends. Reliab. Eng. Syst. Saf., 99 (2012), 33-44. doi: 10.1016/j.ress.2011.11.006. |
[21] | S. D. Eskesen, P. Tengborg, J. Kampmann, T. H. Veicherts, Guidelines for tunnelling risk management: International Tunnelling Association, Working Group No. 2, Tunn. Undergr. Space Technol., 19 (2004), 217-237. doi: 10.1016/j.tust.2004.01.001. |
[22] | Q. Qian, P. Lin, Safety risk management of underground engineering in China: Progress, challenges and strategies, J. Rock Mech. Geotech. Eng., 8 (2016), 423-442. doi: 10.1016/j.jrmge.2016.04.001. |
[23] |
S. Wang, L. Li, S. Shi, S. Cheng, H. Hu, T. Wen, Dynamic Risk Assessment Method of Collapse in Mountain Tunnels and Application, Geotech. Geol. Eng, 38 (2020), 2913-2926. doi: 10.1007/s10706-020-01196-7. doi: 10.1007/s10706-020-01196-7
![]() |
[24] | F. Zhou, Research on Fuzzy Hierarchical Evaluation of Mountain Tunnel Landslide Risk. Master's Thesis, Central South University, (2008) China, Changsha. (in Chinese) |
[25] | F. Li, Risk prediction and control of tunnel collapse. Master's Thesis, Central South University, (2011) China, Changsha. (in Chinese) |
[26] | W. Chen, G. Zhang, H. Wang, G. Zhong, C. Wang, Evaluation of possibility of tunnel collapse by drilling and blasting method based on T-S fuzzy fault tree, Rock Soil Mech., (2019) 319-328. (in Chinese) |
[27] | J. Sun, Study on collapse risk and stability evaluation in mining construction of mountain tunnel. Master's Thesis, Beijing Jiaotong University, (2019) China, Beijing. (in Chinese) |
[28] | B. Wang, S. Li, Q. Zhang, L. Li, Q. Zhang, F. Xu, Risk Assessment of a Tunnel Collapse in a Mountain Tunnel Based on the Attribute Synthetic Evaluation System, Geo-China 2016, Shandong, China, American Society of Civil Engineers, (2016) 198-209. doi: 10.1061/9780784480038.025. |
[29] | Q. Guo, S. Amin, Q. Hao, O. Hass, Resilience assessment of safety system at subway construction sites applying analytic network process and extension cloud models, Reliab. Eng. Syst. Saf., 201 (2020), doi: 106956.10.1016/j.ress.2020.106956. |