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Engineering with Computers
Published in: Engineering with Computers 4/2016

01-10-2016 | Original Article

Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling

Authors: Mahdi Hasanipanah, Majid Noorian-Bidgoli, Danial Jahed Armaghani, Hossein Khamesi

Published in: Engineering with Computers | Issue 4/2016

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Abstract

The potential surface settlement, especially in urban areas, is one of the most hazardous factors in subway and other infrastructure tunnel excavations. Therefore, accurate prediction of maximum surface settlement (MSS) is essential to minimize the possible risk of damage. This paper presents a new hybrid model of artificial neural network (ANN) optimized by particle swarm optimization (PSO) for prediction of MSS. Here, this combination is abbreviated using PSO-ANN. To indicate the performance capacity of the PSO-ANN model in predicting MSS, a pre-developed ANN model was also developed. To construct the mentioned models, horizontal to vertical stress ratio, cohesion and Young’s modulus were set as input parameters, whereas MSS was considered as system output. A database consisting of 143 data sets, obtained from the line No. 2 of Karaj subway, in Iran, was used to develop the predictive models. The performance of the predictive models was evaluated by comparing performance prediction parameters, including root mean square error (RMSE), variance account for (VAF) and coefficient correlation (R 2). The results indicate that the proposed PSO-ANN model is able to predict MSS with a higher degree of accuracy in comparison with the ANN results. In addition, the results of sensitivity analysis show that the horizontal to vertical stress ratio has slightly higher effect of MSS compared to other model inputs.
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Literature
1.
Ocak I, Seker SE (2013) Calculation of surface settlements caused by EPBM tunneling using artificial neural network, SVM, and Gaussian processes. Environ Earth Sci 69(5):1673–1683. doi:10.​1007/​s12665-012-2002-7 CrossRef
2.
3.
Mohammadi SD, Naseri F, Alipoor S (2014) Development of artificial neural networks and multiple regression models for the NATM tunnelling-induced settlement in Niayesh subway tunnel. Bull Eng Geol Environ, Tehran. doi:10.​1007/​s10064-014-0660-2
4.
5.
Farias MM, Junior AM, Assis AP (2004) Displacement control in tunnels excavated by the NATM: 3-D numerical simulations. Tunn Undergr Space Technol 19:283–293CrossRef
6.
Karakus M, Ozsan A, Basarir H (2007) Finite element analysis for the twin metro tunnel constructed in Ankara Clay-Turkey. Bull Eng Geol Environ 66:71–79CrossRef
7.
Schmidt B (1969) A method of estimating surface settlement above tunnels constructed in soft ground. Can Geotech J 20:11–22
8.
Attewell PB, Farmer IW (1974) Ground disturbance caused by shield tunneling in a stiff. Can Geotech J 11:380–395CrossRef
9.
10.
Atkinson JH, Potts DM (1977) Subsidence above shallow tunnels in soft ground. ASCE Geotechnical Eng Div, pp 59–64
11.
Hamza M, Ata A, Roussin A (1999) Ground movements due to construction of cut-and-cover structures and slurry shield tunnel of the Cairo Metro. Tunn Undergr Sp Tech 14(3):281–289CrossRef
12.
Chi SY, Chern JC, Lin CC (2001) Optimized Back-Analysis for Tunneling-Induced Ground Movement Using Equivalent Ground Loss Model. Tunn Undergr Sp Tech 16:159–165CrossRef
13.
Chou WI, Bobet A (2002) Predictions of ground deformations in shallow tunnels in clay. Tunn Undergr Sp Tech 17:3–19CrossRef
14.
15.
16.
Yagiz S, Gokceoglu C, Sezer E, Iplikci S (2009) Application of two non-linear prediction tools to the estimation of tunnel boring machine performance. Eng Appl Artif Intel 22(4):808–814CrossRef
17.
18.
Amiri M, Bakhshandeh Amnieh H, Hasanipanah M, Mohammad Khanli L (2016) A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure. Eng Comput. doi:10.​1007/​s00366-016-0442-5
19.
20.
Jahed Armaghani D, Hajihassani M, Mohamad ET, Marto A, Noorani SA (2014) Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization. Arab J Geosci 7:5383–5396CrossRef
21.
Jahed Armaghani D, Hasanipanah M, Mohamad ET (2015) A combination of the ICA-ANN model to predict air overpressure resulting from blasting. Eng Comput. doi:10.​1007/​s00366-015-0408-z
22.
Simpson PK (1990) Artificial neural system: foundation, paradigms, applications and implementations. Pergamon, New York
23.
Haykin S (1999) Neural networks, 2nd edn. Prentice-Hall, Englewood CliffsMATH
24.
Hasanipanah M, Jahed Armaghani D, Khamesi H, Bakhshandeh Amnieh H, Ghoraba S (2015) Several non-linear models in estimating air-overpressure resulting from mine blasting. Eng Comput. doi:10.​1007/​s00366-015-0425-y
25.
Monjezi M, Bahrami A, Yazdian Varjani A (2010) Simultaneous prediction of fragmentation and flyrock in blasting operation using artificial neural networks. Int J Rock Mech Min Sci 47:476–480CrossRef
26.
Monjezi M, Hasanipanah M, Khandelwal M (2013) Evaluation and prediction of blast-induced ground vibration at Shur River Dam, Iran, by artificial neural network. Neural Comput Appl 22:1637–1643CrossRef
27.
Fausett LV (1994) Fundamentals of neural networks: architecture, algorithms and applications. Prentice-Hall, Englewood CliffsMATH
28.
Khandelwal M, Singh TN (2009) Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci 46:1214–1222CrossRef
29.
Wang XG, Tang Z, Tamura H, Ishii M, Sun WD (2004) An improved backpropagation algorithm to avoid the local minima problem. Neurocomputing 56:455–460CrossRef
30.
Adhikari R, Agrawal RK (2011) Effectiveness of PSO based neural network for seasonal time series forecasting. Indian International Conference on Artificial Intelligence (IICAI). Tumkur, India, pp 232–244
31.
Kennedy J, Eberhart RC (1995) Particle swarm optimization. Proc. IEEE International Conference on Neural Networks (Perth, Australia), IEEE Service Center, Piscataway, p 1942–1948
32.
Jahed Armaghani D, Raja SNSB, Faizi K, Rashid ASA (2015) Developing a hybrid PSO–ANN model for estimating the ultimate bearing capacity of rock-socketed piles. Neural Comput Appl. doi:10.​1007/​s00521-015-2072-z
33.
Khamesi H, Torabi S, Mirzaei-Nasirabad H, Ghadiri Z (2015) Improving the Performance of Intelligent Back Analysis for Tunneling Using Optimized Fuzzy Systems: case Study of the Karaj Subway Line 2 in Iran. J Comput Civ Eng. doi:10.​16.​1061/​(ASCE)CP.​1943-5487.​0000421
34.
Poli R, Kennedy J, Blackwell T (2007) Particle swarm optimization. Swarm Intell 1(1):33–57CrossRef
35.
Shi Y, Eberhart R (1998) Parameter selection in particle swarm optimization. Proceedings of the seventh annual conference on evolutionary. Springer, New York, pp 591–600CrossRef
36.
Das MT, Dulger LC (2009) Signature verification (SV) toolbox: application of PSO-NN. Eng Appl Artif Intell 22(4):688–694CrossRef
37.
Swingler K (1996) Applying neural networks: a practical guide. Academic Press, New York
38.
Looney CG (1996) Advances in feed-forward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans Knowl Data Eng 8(2):211–226CrossRef
39.
Nelson M, Illingworth WT (1990) A Practical Guide to Neural Nets. Addison–Wesley, Reading MAMATH
40.
Hush DR (1989) Classification with neural networks: a performance analysis. Proceedings of the IEEE International Conference on Systems Engineering. Dayton, OH, USA, pp 277–280
41.
Kanellopoulas I, Wilkinson GG (1997) Strategies and best practice for neural network image classification. Int J Remote Sens 18:711–725CrossRef
42.
Maulenkamp F, Grima MA (1999) Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness. Int J Rock Mech Min Sci 36:29–39CrossRef
43.
Ornek M, Laman M, Demir A, Yildiz A (2012) Prediction of bearing capacity of circular footings on soft clay stabilized with granular soil. Soil Found 52:69–80CrossRef
44.
Ceryan N, Okkan U, Kesimal A (2013) Prediction of unconfined compressive strength of carbonate rocks using artificial neural networks. Environ Earth Sci 68:807–819CrossRef
45.
Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the First IEEE International Conference on Neural Networks, San Diego, pp 11–14
46.
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal Approximators. Neural Networks 2:359–366CrossRef
47.
Baheer I (2000) Selection of methodology for modeling hysteresis behavior of soils using neural networks. J Comput Aid Civil Infrastruct Eng 5:445–463CrossRef
48.
Sonmez H, Gokceoglu C, Nefeslioglu HA, Kayabasi A (2006) Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min Sci 43:224–235CrossRef
49.
Sonmez H, Gokceoglu C (2008) Discussion on the paper by H. Gullu and E. Ercelebi, “A neural network approach for attenuation relationships: an application using strong ground motion data from Turkey. Eng Geol 97:91–93CrossRef
50.
Ripley BD (1993) Statistical aspects of neural networks. In: Barndoff- Neilsen OE, Jensen JL, Kendall WS, editors. Networks and chaos-statistical and probabilistic aspects. London: Chapman & Hall, pp. 40–123
51.
Paola JD (1994) Neural network classification of multispectral imagery. MSc thesis, The University of Arizona, USA
52.
Wang C (1994) A theory of generalization in learning machines with neural application. PhD thesis, The University of Pennsylvania, USA
53.
Masters T (1994) Practical neural network recipes in C++. Academic Press, Boston MAMATH
54.
Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10:215–236CrossRef
55.
Hajihassani M, Jahed Armaghani D, Sohaei H, Mohamad ET, Marto A (2014) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust 80:57–67CrossRef
56.
Momeni E, Jahed Armaghani D, Hajihassani M, Amin MFM (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement 60:50–63CrossRef
57.
Zorlu K, Gokceoglu C, Ocakoglu F, Nefeslioglu HA, Acikalin S (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96(3):141–158CrossRef
58.
Gordan B, Jahed Armaghani D, Hajihassani M, Monjezi M (2015) Prediction of seismic slope stability through combination of particle swarm optimization and neural network. Eng Comput. doi:10.​1007/​s00366-015-0400-7
59.
Tonnizam Mohamad E, Jahed Armaghani D, Momeni E, Alavi Nezhad Khalil Abad SV (2014) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull Eng Geol Environ. doi:10.​1007/​s10064-014-0638-0
60.
Yang Y, Zang O (1997) A hierarchical analysis for rock engineering using artificial neural networks. Rock Mech Rock Eng 30:207–222CrossRef
Metadata
Title
Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling
Authors
Mahdi Hasanipanah
Majid Noorian-Bidgoli
Danial Jahed Armaghani
Hossein Khamesi
Publication date
01-10-2016
Publisher
Springer London
Published in
Engineering with Computers / Issue 4/2016
Print ISSN: 0177-0667
Electronic ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-016-0447-0

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