nach oben

Environmental Earth Sciences

Erschienen in:

01.03.2017 | Original Article

Seismic liquefaction potential assessed by neural networks

verfasst von: Xinhua Xue, Enlong Liu

Erschienen in: Environmental Earth Sciences | Ausgabe 5/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This study presents two optimization techniques: genetic algorithm (GA) and particle swarm optimization (PSO), to improve the efficiency of backpropagation (BP) neural network model for predicting liquefaction susceptibility of soil. A detailed parametric study is designed and performed to find the optimal parameters of GA and PSO, respectively. The database used in this study includes 166 CPT-based field observations from more than eight major earthquakes between 1964 and 1983. Six factors including cone resistance, total vertical stress, effective vertical stress, depth of penetration, normalized peak horizontal acceleration at ground surface and earthquake magnitude are selected as the evaluating indices. The predictions from the PSO–BP model were compared with those from two models: BP and GA–BP. The study concluded that the proposed PSO–BP model improves the classification accuracy and is a feasible method in predicting soil liquefaction.

Vorheriger Artikel Bacterial activity in hydrogenetic ferromanganese crust from the Indian Ocean: a combined geochemical, experimental and pyrosequencing study

Nächster Artikel Study of diel hydrochemical variation in a volcanic watershed using principal component analysis: Tatun Volcano Group, North Taiwan

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Alemdag S, Gurocak Z, Cevik A, Cabalar AF, Gokceoglu C (2016) Modeling deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming. Eng Geol 203:70–82CrossRef

Banerjee TP, Das S (2012) Multi-sensor data fusion using support vector machine for motor fault detection. Inf Sci 217:96–107CrossRef

Belkhatir M, Schanz T, Arab A, Della N, Kadri A (2014) Insight into the effects of gradation on the pore pressure generation of sand–silt mixtures. Geotech Test J 37(5):922–931CrossRef

Cabalar AF, Cevik A (2009) Genetic programming-based attenuation relationship: an application of recent earthquakes in turkey. Comput Geosci 35(9):1884–1896CrossRef

Cabalar AF, Cevik A (2011) Triaxial behavior of sand–mica mixtures using genetic programming. Expert Syst Appl 38(8):10358–10367CrossRef

Cabalar AF, Cevik A, Guzelbey IH (2010) Constitutive modeling of Leighton Buzzard sands using genetic programming. Neural Comput Appl 19(5):657–665CrossRef

Cevik A, Cabalar AF (2009) Modelling damping ratio and shear modulus of sand-mica mixtures using genetic programming. Expert Syst Appl 36(4):7749–7757CrossRef

Chern SG, Lee CY (2009) CPT-based simplified liquefaction assessment by using fuzzy-neural network. J Mar Sci Technol 17(4):326–331

Chern SG, Lee CY, Wang CC (2008) CPT-based liquefaction assessment by using fuzzy-neural network. J Mar Sci Technol 16(2):139–148

Ding SF, Su CY, Yu JZ (2011) An optimizing BP neural network algorithm based on genetic algorithm. Artif Intell Rev 36(2):153–162CrossRef

Erzin Y, Ecemis N (2015) The use of neural networks for CPT-based liquefaction screening. Bull Eng Geol Environ 74(1):103–116CrossRef

Farrokhzad F, Choobbasti AJ, Barari A (2012) Liquefaction microzonation of Babol city using artificial neural network. J King Saud Univ Sci 24(1):89–100CrossRef

Ghosh S, Das S, Kundu D, Suresh K, Panigrahi BK, Cui ZH (2012) An inertia-adaptive particle swarm system with particle mobility factor for improved global optimization. Neural Comput Appl 21(2):237–250CrossRef

Goh ATC (1994) Seismic liquefaction potential assessed by neural networks. J Geotech Eng 120(9):1467–1480CrossRef

Goh ATC (1996) Neural network modeling of CPT seismic liquefaction data. J Geotech Eng 122(1):70–73CrossRef

Goh ATC (2002) Probabilistic neural network for evaluating seismic liquefaction potential. Can Geotech J 39:219–232CrossRef

Guettaya I, El Ouni MR (2014) In situ-based assessment of soil liquefaction potential-case study of an earth dam in Tunisia. Front Struct Civ Eng 8(4):456–461CrossRef

Jha SK, Suzuki K (2009) Reliability analysis of soil liquefaction based on standard penetration test. Comput Geotech 36(4):589–596CrossRef

Juang CH, Chen CJ, Jiang T, Andrus RD (2000) Risk-based liquefaction potential evaluation using standard penetration tests. Can Geotech J 37(6):1195–1208CrossRef

Juang CH, Yuan HM, Lee DH, Lin PS (2003) Simplified cone penetration test-based method for evaluating liquefaction resistance of soils. J Geotech Geoenviron Eng 129(1):66–80CrossRef

Karegowda AG, Manjunath AS, Jayaram MA (2011) Application of genetic algorithm optimized neural network connection weights for medical diagnosis of Pima Indians diabetes. Int J Soft Comput 2(2):15–22CrossRef

Karthikeyan J, Kim D, Aiyer BG, Samui P (2013) SPT-based liquefaction potential assessment by relevance vector machine approach. Eur J Environ Civ Eng 17(4):248–262CrossRef

Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the 1995 IEEE international conference on neural networks, vol 4, Perth, Australia. IEEE Service Center, Piscataway, pp 1942–1948

Kumar S, Naresh R (2007) Efficient real coded genetic algorithm to solve the non-convex hydrothermal scheduling problem. Int J Electr Power Energy Syst 29(10):738–747CrossRef

Kurup PU, Dudani NK (2002) Neural networks for profiling stress history of clays from PCPT data. J Geotech Geoenviron 128(4):569–579CrossRef

Lee CY, Chern SG (2013) Application of a support vector machine for liquefaction assessment. J Mar Sci Technol 21(3):318–324

Marcuson WF III (1978) Definition of terms related to liquefaction. J Geotech Eng Div ASCE 104(9):1197–1200

Mughieda O, Bani HK, Safieh B (2009) Liquefaction assessment by artificial neural networks based on CPT. Int J Geotech Eng 2:289–302CrossRef

Nasir M, Das S, Maity D, Sengupta S, Halder U, Suganthan PN (2012) A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization. Inf Sci 209:16–36CrossRef

Padmini D, Ilamparuthi K, Sudheer KP (2008) Ultimate bearing capacity prediction of shallow foundations on cohesionless soils using neurofuzzy models. Comput Geotech 35:33–46CrossRef

Pal M (2006) Support vector machines-based modeling of seismic liquefaction potential. Int J Numer Anal Methods 30(10):983–996CrossRef

Pour MN, Asakereh A (2015) A comparison between two field methods of evaluation of liquefaction potential in the Bandar Abbas city. Am J Civ Eng 3(2–2):1–5

Ramakrishnan D, Singh TN, Purwar N, Badre KS, Gulati A, Gupta S (2008) Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data, Gujarat, India. Comput Geosci 12:491–501CrossRef

Ren Z, San Z (2007) Improvement of real-valued genetic algorithm and performance study. Acta Electron Sin 35(2):269–274

Ren C, An N, Wang JZ, Li L, Hu B, Shang D (2014) Optimal parameters selection for BP neural network based on particle swarm optimization: a case study of wind speed forecasting. Knowl Based Syst 56:226–239CrossRef

Sakthivel VP, Bhuvaneswari R, Subramanian S (2010) An improved particle swarm optimization for induction motor parameter determination. Int J Comput Appl 1(2):62–67

Samui P (2007) Seismic liquefaction potential assessment by using relevance vector machine. Earthq Eng Eng Vib 6(4):331–336CrossRef

Samui P (2013) Liquefaction prediction using support vector machine model based on cone penetration data. Front Struct Civ Eng 7(1):72–82CrossRef

Samui P, Sitharam TG (2011) Machine learning modelling for predicting soil liquefaction susceptibility. Nat Hazards Earth Syst Sci 11:1–9CrossRef

Tang Y, Zang YQ, Huang G, Hu X (2005) Granular SVM-RFE gene selection algorithm for reliable prostate cancer classification on microarray expression data. In: Proceedings of the 5th IEEE symposium on bioinformatics and bioengineering (BIBE’05)

Whitley D, Starkweather T, Bogart C (1990) Genetic algorithms and neural networks: optimizing connection and connectivity. Parallel Comput 14(3):347–361CrossRef

Xue XH, Yang XG (2014) Seismic liquefaction potential assessed by fuzzy comprehensive evaluation method. Nat Hazards 71:2101–2112CrossRef

Yamagami Y, Jiang JC (1997) A search for the critical slip surface in three-dimensional slope stability analysis. Soils Found 37(3):1–16CrossRef

Young-Su K, Byung-Tak K (2006) Use of artificial neural networks in the prediction of liquefaction resistance of sands. J Geotech Geoenviron Eng 132(11):1502–1504CrossRef

Zhang G, Robertson PK, Brachman RWI (2004) Estimating liquefaction-induced lateral displacements using the standard penetration test or cone penetration test. J Geotech Geoenviron Eng 130(8):861–871CrossRef

Zhao WG (2012) BP neural network based on PSO algorithm for temperature characteristics of gas nanosensor. J Comput 7(9):2318–2323

Titel: Seismic liquefaction potential assessed by neural networks
verfasst von: Xinhua Xue
Enlong Liu
Publikationsdatum: 01.03.2017
Verlag: Springer Berlin Heidelberg
Erschienen in: Environmental Earth Sciences / Ausgabe 5/2017
Print ISSN: 1866-6280
Elektronische ISSN: 1866-6299
DOI: https://doi.org/10.1007/s12665-017-6523-y

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 5/2017

Response of the soil hydrothermal process to difference underlying conditions in the Beiluhe permafrost region

Alpine wetland ecosystem carbon sink and its controls at the Qinghai Lake

Bacterial activity in hydrogenetic ferromanganese crust from the Indian Ocean: a combined geochemical, experimental and pyrosequencing study

Accumulation behavior of toxic elements in the soil and plant from Xinzhuangzi reclaimed mining areas, China

Cross-formational flow of water into coalbed methane reservoirs: controls on relative permeability curve shape and production profile

GIS-based FRASTIC model for pollution vulnerability assessment of fractured-rock aquifer systems