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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 21/2021

28.05.2021 | Original Article

Soil–conduit interaction: an artificial intelligence application for reinforced concrete and corrugated steel conduits

Erschienen in: Neural Computing and Applications | Ausgabe 21/2021

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Abstract

Marston’s load theory is commonly used for understanding the soil–conduit interaction. However, there are no practical methods available which can estimate the Marston’s soil prism (MSP) width ratio. Moreover, the advent of soft computing methods has made many traditional approaches antiquated. The main purpose of this work is to compare and evaluate the predictive abilities of several machine learning-based models in predicting the MSP width ratio for the reinforced concrete (RC) and corrugated steel (CS) conduits. By utilizing the finite element modelling, a large-scale dataset was generated for the width of the soil prism for both types of conduit material, when buried under sandy soils of varying stiffness. After preparing the required dataset, feature validity technique based on correlation-based feature selection was employed to find the most influential parameters affecting the MSP width. Thereafter, five regression-based data driven models namely artificial neural networks (ANN), least-square support vector regression, extreme learning machine, Gaussian process regression, and multiple linear regression were developed to forecast the MSP width ratio. The results showed that the ANN outperforms the other predictive models for both the conduit types. In addition, due to the excellent overall performance of the ANN, it was translated into functional relationship for predicting the MSP width ratio for RC and CS conduits.
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Literatur
1.
Watkins RK, Anderson LR (1999) Structural mechanics of buried pipes. CRC Press, Boca RatonCrossRef
2.
Khan MUA, Shukla SK (2020) Load-settlement response and bearing capacity of a surface footing located over a conduit buried within a soil slope. Int J Geomech 20(10):04020173CrossRef
3.
Marston MG (1930) The theory of external loads on closed conduits in the light of the latest experiments. Proc Highw Res Board 9:138–170
4.
White LH, Layer PJ (1960) The corrugated metal conduit as a compression ring. Proc Highw Res Board 39:389–397
5.
Burghignoli A (1981) Soil interaction in buried structures. Proc Int Conf Soil Mech Found Eng 1519:69–74
6.
Spangler MG (1962) Culverts and conduits. McGraw Hill, New York
7.
Whitman RV, Luscher U (1962) Basic experiment into soil–structure interaction. J Soil Mech Found Div 88(6):135–168CrossRef
8.
Finn WD (1963) Boundary value problems of soil mechanics. J Soil Mech Found Div 89(5):39–72CrossRef
9.
Chelapati CV (1964) Arching in soil due to the deflection of a rigid horizontal strip. In: Proceedings of the symposium on soil-structure interaction, pp 356–377
10.
Nielson FD (1966) Soil-structure-arching analysis of buried flexible structures. Ph.D. Dissertation, University of Arizona
11.
Bjerrum LC, Frimann LJ, Has M, Duncan JM (1972) Earth pressures on flexible structures—a state of the art report. In: Proceedings, fifth european conference on soil mechanics and foundation engineering, pp 169–196
12.
Selig ET (1990) Soil properties for plastic pipe installations. ASTM Spec Tech Publ, pp 141–158
13.
14.
Marston A, Anderson AO (1913) The theory of loads on pipe in ditches and tests of cement and clay drain tile and sewer pipe. Iowa State Coll Agric Mech Arts 31:1–185
15.
16.
Moser AP, Folkman SL (2001) Buried pipe design. McGraw-Hill, New York
17.
Greenwood ME, Lang DC (1990) Vertical deflection of buried flexible pipes. ASTM Spec Tech Publ, pp 185–214
18.
Moore ID (2001) Buried pipes and culverts. In: Geotechnical and geoenvironmental engineering handbook, pp 541–567
19.
Kang J, Parker F, Kang YJ, Yoo CH (2008) Effects of frictional forces acting on sidewalls of buried box culverts. Int J Numer Anal Methods Geomech 32(3):289–306MATHCrossRef
20.
Kim MK, Cho SH, Yun IJ, Won JH (2012) Three-dimensional responses of buried corrugated pipes and ANN-based method for predicting pipe deflections. Int J Numer Anal Methods Geomech 36(1):1–16CrossRef
21.
Vu-Bac N, Lahmer T, Zhuang X, Nguyen-Thoi T, Rabzuk T (2016) A software framework for probabilistic sensitivity analysis for computationally expensive models. Adv Eng Softw 100:19–31CrossRef
22.
Acharyya R, Dey A (2019) Assessment of bearing capacity for strip footing located near sloping surface considering ANN model. Neural Comput Appl 31:8087–8100CrossRef
23.
Kardani N, Zhou A, Nazem M, Shen SL (2021) Improved prediction of slope stability using a hybrid stacking ensemble method based on finite element analysis and field data. J Rock Mech Geotech Eng 13(1):188-201CrossRef
24.
Zhang K, Lyu HM, Shen SL, Zhou A, Yin ZY (2020) Evolutionary hybrid neural network approach to predict shield tunneling-induced ground settlements. Tunn Undergr Sp Technol 106:103594CrossRef
25.
Moayedi H, Hayati S (2018) Modelling and optimization of ultimate bearing capacity of strip footing near a slope by soft computing methods. Appl Soft Comput J 66:208–219CrossRef
26.
Moayedi H, Moatamediyan A, Nguyen H, Bui XN, Bui DT, Rashid ASA (2020) Prediction of ultimate bearing capacity through various novel evolutionary and neural network models. Eng Comput 36:671–687CrossRef
27.
Yuan C, Moayedi H (2020) Evaluation and comparison of the advanced metaheuristic and conventional machine learning methods for the prediction of landslide occurrence. Eng Comput 36:1801–1811CrossRef
28.
29.
Tarawneh B, Nusairat J, Hakam Y (2018) Load testing and settlement of shallow foundation on desert sands. Proc Inst Civ Eng Geotech Eng 171(1):52–63CrossRef
30.
Shao Z, Jahed Armaghani D, Bejarbaneh BY, Muazu MA, Mohamad ET (2019) Estimating the friction angle of black shale core specimens with hybrid-ANN approaches. Meas J Int Meas Confed 145:744–755CrossRef
31.
32.
Acharyya R, Dey A, Kumar B (2020) Finite element and ANN-based prediction of bearing capacity of square footing resting on the crest of c-φ soil slope. Int J Geotech Eng 14(2):176–187CrossRef
33.
Gao W, Raftari M, Rashid ASA, Muazu MA, Jusoh WAW (2020) A predictive model based on an optimized ANN combined with ICA for predicting the stability of slopes. Eng Comput 36(1):325–344CrossRef
34.
Tafreshi SNM, Mehrjardi GT (2008) The use of neural network to predict the behavior of small plastic pipes embedded in reinforced sand and surface settlement under repeated load. Eng Appl Artif Intell 21(6):883–894CrossRef
35.
Shokouhi SKS, Dolatshah A, Ghobakhloo E (2013) Seismic strain analysis of buried pipelines in a fault zone using hybrid FEM-ANN approach. Earthq Struct 5(4):417–438CrossRef
36.
Sargand SM, Masada T (2003) Soil arching over deeply buried thermoplastic pipe. Transp Res Rec 1849(1):109–118CrossRef
37.
Qin X, Ni P, Zhou M (2017) Improved analytical solution of vertical pressure on top of induced trench rigid culverts. Geosynth Int 24(6):615–624CrossRef
38.
Fan CC, Luo JH (2008) Numerical study on the optimum layout of soil-nailed slopes. Comput Geotech 35(4):585–599CrossRef
39.
Ghazavi M, Eghbali AH (2008) A simple limit equilibrium approach for calculation of ultimate bearing capacity of shallow foundations on two-layered granular soils. Geotech Geol Eng 26:535–542CrossRef
40.
Brinkgreve RB, Kumarswamy S, Swolfs WM, Foria F (2018) Plaxis 2D technical manual. Rotterdam, London
41.
Elshimi TM, Moore ID (2013) Modeling the effects of backfilling and soil compaction beside shallow buried pipes. J Pipeline Syst Eng Pract 4(4):04013004CrossRef
42.
Wadi A, Pettersson L, Karoumi R (2015) Flexible culverts in sloping terrain: numerical simulation of soil loading effects. Eng Struct 101:111–124CrossRef
43.
Sharma V, Kumar A (2018) Behavior of ring footing resting on reinforced sand subjected to eccentric-inclined loading. J Rock Mech Geotech Eng 10:347–357CrossRef
44.
Mcgrath TJ (1998) Calculating loads on buried culverts based on pipe hoop stiffness. Transp Res Rec 1656(1):73–79CrossRef
45.
Gao W, Alsarraf J, Moayedi H, Shahsavar A, Nguyen H (2019) Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms. Appl Soft Comput J 84:105748CrossRef
46.
Cai J, Luo J, Wang S, Yang S (2018) Feature selection in machine learning: a new perspective. Neurocomputing 300:70–79CrossRef
47.
Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182MATH
48.
Shahin MA, Maier HR, Jaksa MB (2002) Predicting settlement of shallow foundations using neural networks. J Geotech Geoenvironmental Eng 128:785–793CrossRef
49.
Samui P, Sitharam TG (2008) Least-square support vector machine applied to settlement of shallow foundations on cohesionless soils. Int J Numer Anal Methods Geomech 32:2033–2043MATHCrossRef
50.
Pal M, Deswal S (2010) Modelling pile capacity using Gaussian process regression. Comput Geotech 37:942–947CrossRef
51.
Samui P, Kim D (2013) Least square support vector machine and multivariate adaptive regression spline for modeling lateral load capacity of piles. Neural Comput Appl 23:1123–1127CrossRef
52.
Liu Z, Shao J, Xu W, Wu Q (2015) Indirect estimation of unconfined compressive strength of carbonate rocks using extreme learning machine. Acta Geotech 10:651–663CrossRef
53.
Suthar M (2020) Applying several machine learning approaches for prediction of unconfined compressive strength of stabilized pond ashes. Neural Comput Appl 32:9019–9028CrossRef
54.
55.
Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, OxfordMATH
56.
Witten IH, Frank E, Hall MA, Pal CJ (2016) Data mining: practical machine learning tools and techniques. Morgan Kaufmann, Cambridge
57.
Rumelhart DE (1986) Parallel distributed processing: explorations in the microstructure of cognition. In: Learning internal representations by error propagation. MIT press, Cambridge, pp 318–362
58.
Goh ATC (1995) Back-propagation neural networks for modeling complex systems. Artif Intell Eng 9(3):143–151CrossRef
59.
Ashrafian A, Shokri F, Taheri Amiri MJ, Yaseen ZM, Rezaie-Balf M (2020) Compressive strength of foamed cellular lightweight concrete simulation: new development of hybrid artificial intelligence model. Constr Build Mater 230:117048CrossRef
60.
Zurada JM (1992) Introduction to artificial neural systems. West Publishing Company, St. Paul
61.
Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan College Publishing Company, Englewood CliffsMATH
62.
Cherkassky V, Mulier FM (2007) Learning from data: concepts, theory, and methods. John Wiley & Sons, New YorkMATHCrossRef
63.
Xue X, Yang X (2016) Seismic liquefaction potential assessed by support vector machines approaches. Bull Eng Geol Environ 75(1):153–162CrossRef
64.
Zhao H, Ru Z, Yin S (2015) A practical indirect back analysis approach for geomechanical parameters identification. Mar Georesour Geotechnol 33(3):212–221CrossRef
65.
Zhuang DY, Ma K, Tang CA, Liang ZZ, Wang KK, Wang ZW (2019) Mechanical parameter inversion in tunnel engineering using support vector regression optimized by multi-strategy artificial fish swarm algorithm. Tunn Undergr Sp Technol 83:425–436CrossRef
66.
67.
Cristianini N, Shawe-Taylor JS-TJ (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, CambridgeMATHCrossRef
68.
Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2(3):1–27CrossRef
69.
70.
Suykens JAK, Vandewalle J (1999) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300CrossRef
71.
Kisi O (2015) Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree. J Hydrol 528:312–320CrossRef
72.
Samui P (2008) Slope stability analysis: a support vector machine approach. Environ Geol 56(2):255–267CrossRef
73.
Mercer J (1909) Functions of positive and negative type and their connection with the theory of integral equations. Philos Trans R Soc 209:441–458MATH
74.
Bin HG, Chen L, Siew CK (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879–892CrossRef
75.
Ding S, Zhao H, Zhang Y, Xu X, Nie R (2015) Extreme learning machine: algorithm, theory and applications. Artif Intell Rev 44(1):103–115CrossRef
76.
Rasmussen CEWC (2006) Gaussian processes for machine learning. MIT press, CambridgeMATH
77.
Sobhani J, Najimi M, Pourkhorshidi AR, Parhizkar T (2010) Prediction of the compressive strength of no-slump concrete: a comparative study of regression, neural network and ANFIS models. Constr Build Mater 24(5):709–718CrossRef
78.
Kumar S, Basudhar PK (2018) A neural network model for slope stability computations. Geotech Lett 8(2):149–154CrossRef
79.
Stone M (1974) Cross-validatory choice and assessment of statistical predictions. J R Stat Soc Ser B 36(2):111–113MathSciNetMATH
80.
Soleimanbeigi A, Hataf N (2006) Prediction of settlement of shallow foundations on reinforced soils using neural networks. Geosynth Int 13(4):161–170CrossRef
81.
Das SK, Sivakugan N (2010) Discussion of “intelligent computing for modeling axial capacity of pile foundations.” Can Geotech J 47(8):928–930CrossRef
82.
Ranganathan A (2004) The Levenberg–Marquardt algorithm. Tutor LM Algorithm 11(1):101–110
83.
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366MATHCrossRef
84.
Aamir M, Tolouei-Rad M, Vafadar A, Raja MNA, Giasin K (2020) Performance analysis of multi-spindle drilling of Al2024 with TiN and TiCN coated drills using experimental and artificial neural networks technique. Appl Sci 10(23):8633CrossRef
85.
Yaseen ZM, Deo RC, Hilal A et al (2018) Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Adv Eng Softw 115:112–125CrossRef
86.
Deo RC, Kisi O, Singh VP (2017) Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model. Atmos Res 184:149–175CrossRef
87.
88.
Montgomery DC, Peck EA, Vining GG (2012) Introduction to linear regression analysis. Wiley, New YorkMATH
89.
Alsharari B, Olenko A, Abuel-Naga H (2020) Modeling of electrical resistivity of soil based on geotechnical properties. Expert Syst Appl 141:112966CrossRef
Metadaten
Titel
Soil–conduit interaction: an artificial intelligence application for reinforced concrete and corrugated steel conduits
Publikationsdatum
28.05.2021
Erschienen in
Neural Computing and Applications / Ausgabe 21/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-021-06125-0

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