On the Multiple Linear Regression and Artificial Neural Networks for Strength Prediction of Soil-Based Controlled Low-Strength Material

Huang, Li Jeng; Sheen, Yeong Nain; Le, Duc-Hien

doi:10.4028/www.scientific.net/amm.597.349

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…There are several studies on the engineering properties of CLSMs by laboratory experiments [5][6][7][8][9][10], and numerical analyses of applications of CLSM to civil engineering, such as excavation and backfill after retaining walls [11][12][13], bridge abutments [14][15][16][17], pipeline and trench ducts [18], pavement bases [19][20][21][22][23][24], and so on. All these studies reflect requirement of the identification of mechanical constants of the CLSMs.…”

Section: Introductionmentioning

confidence: 99%

Parameter Recognition of Engineering Constants of CLSMs in Civil Engineering Using Artificial Neural Networks

Huang¹

2018

Advanced Applications for Artificial Neural Networks

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Controlled low-strength materials (CLSMs) had been widely applied to excavation and backfill in civil engineering. However, the engineering properties of CLSM in these embankments vary dramatically due to different contents involved. This study is proposed to employ the ANSYS software and two different artificial neural networks (ANNs), that is, back-propagation artificial neural network (BPANN) and radial basis function neural network (RBFNN), to determine the engineering properties of CLSM by considering an inverse problem in which elastic modulus and the Poisson's ratio can be identified from inputting displacements and stress measurements. The PLANE42 element of ANSYS was first used to investigate a 2D problem of a retaining wall with embankment, with E = 0.02~3 GPa, ν= 0.1~0.4 to obtain totally 270 sampling data for two earth pressures and two top surface settlements of embankment. These data are randomly divided into training and testing set for ANNs. Practical cases of three kinds of backfilled materials, soil, and two kinds of CLSMs (CLSM-B80/30% and CLSM-B130/30%) will be used to check the validity of ANN prediction results. Results showed that maximal errors of CLSM elastic parameters identified by well-trained ANNs can be within 6%.

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Section: Introductionmentioning

confidence: 99%

Parameter Recognition of Engineering Constants of CLSMs in Civil Engineering Using Artificial Neural Networks

Huang¹

2018

Advanced Applications for Artificial Neural Networks

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“…The special features of CLSM include: durable, excavatable, erosion-resistant, self-leveling, rapid curing, flowable around confined spacing, wasting material usage and elimination of compaction labors and equipment, etc. The authors have also conducted some preliminary studies on engineering properties of CLSM [4] and the numerical analyses on static and free vibration analysis of CLSM bases in flexible pavements [5,6].…”

Section: Introductionmentioning

confidence: 99%

Finite Element Analysis of Cracked Flexible Pavements Embedded with Controlled Low-Strength Material Bases

Tu¹,

Wang²,

Li-min³

2017

Proceedings of the Second International Conference on Mechanics, Materials and Structural Engineering (ICMMSE 2017)

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Abstract. Cracks resulting from shrinkage due to temperature or moisture usually exist in the flexible pavements in highways and runways of airport. This paper presents at finite element analysis of 4-layered flexible pavements with a penetrated longitudinal crack on surface layer embedded with controlled low-strength materials (CLSM) bases which are well-known recycling sustainable materials. Two-dimensional deformation assumption is employed. Emphasis is put on the effect of using CLSM bases on the reduction surface settlement, horizontal and vertical stresses as well as crack face displacements. Two kinds of CLSMs, i.e., CLSM-B80/30% and CLSM-B130/30% are compared with graded crushed stones and AC used for base materials. Numerical study shows that vertical settlements, vertical and horizontal stresses by using CLSM-B130/30% base is smaller than that by using graded crushed stone and is shown to be good material employed as base substitute for graded crushed stone in flexible pavement design.

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“…The special features of CLSM include: durable, excavatable, erosion-resistant, self-leveling, rapid curing, flowable around confined spacing, wasting material usage and elimination of compaction labors and equipments, etc. The authors also conducted some preliminary studies on engineering properties of CLSM [4] and the numerical analyses on static and free vibration analysis of CLSM bases in flexible pavements [5,6].…”

Section: Introductionmentioning

confidence: 99%

Viscoelastic Analysis of Flexible Pavements Embedded with Controlled Low-Strength Material Bases Using Finite Element Method

Huang¹,

Wang²,

Li-min³

2017

Proceedings of the Second International Conference on Mechanics, Materials and Structural Engineering (ICMMSE 2017)

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Abstract. This paper presents finite element analysis of 4-layered flexible pavements embedded viscoelastic asphalt concrete (AC) top layer and controlled low-strength materials (CLSM) sublayers as well as graded crushed stone subgrades layers. Two-dimensional deformation assumption is employed. Time-dependent stress-strain relations are treated using Laplace transform based on correspondence principle. Collocation method is employed for inversion of Laplace transformed results into time-domain results. A typical layered pavement is analyzed in which relaxation modulus of top viscoelastic asphalt layer is expressed in Prony series. Two kinds of CLSMs, i.e., CLSM-B80/30% and CLSM-B130/30% are compared with graded crushed stones and AC used for base materials. Time-domain responses of vertical displacement, vertical stress and horizontal stress at the bottom of AC layer are compared. The steady state vertical settlements using CLSM bases (B80/30% and B130/30%) are smaller than that using graded crushed stones even considering viscoelastic behavior of asphalt layer and show to be good materials employed as base substitutes for graded crushed stone in flexible pavement design.

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On the Multiple Linear Regression and Artificial Neural Networks for Strength Prediction of Soil-Based Controlled Low-Strength Material

Cited by 8 publications

References 6 publications

Parameter Recognition of Engineering Constants of CLSMs in Civil Engineering Using Artificial Neural Networks

Parameter Recognition of Engineering Constants of CLSMs in Civil Engineering Using Artificial Neural Networks

Finite Element Analysis of Cracked Flexible Pavements Embedded with Controlled Low-Strength Material Bases

Viscoelastic Analysis of Flexible Pavements Embedded with Controlled Low-Strength Material Bases Using Finite Element Method

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