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Numerical study of the effects of groundwater drawdown on ground settlement for excavation in residual soils

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Abstract

For deep excavations in residual soils that are underlain by highly fissured or fractured rocks, it is common to observe the drawdown of the groundwater table behind the excavation, resulting in seepage-induced ground settlement. In this study, finite element analyses are firstly performed to assess the critical parameters that influence the ground settlement performance in residual soil deposits subjected to groundwater drawdown. The critical parameters that influence the ground settlement performance were identified as the excavation width, the excavation depth, the depth of groundwater drawdown, the thickness of the residual soil, the average SPT N60 value of the residual soil, the location of the moderately weathered rock, and the wall system stiffness. Subsequently, an artificial neural network (ANN) model was developed to provide estimates of the maximum ground settlement. Validation of the performance of ANN model was carried out using additional data derived from finite element analyses as well as with measured data from a number of excavation sites.

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Acknowledgements

The authors would like to acknowledge the financial support from LTIF project funded by the Land Transport Authority (LTA) Singapore, Natural Science Foundation of Chongqing, China (cstc2018jcyjAX0632), the China Postdoctoral Science Foundation (Grant No. 2017M620414), and the Special Funding for Postdoctoral Researchers in Chongqing (No. Xm2017007). Our special thanks to the following LTA engineers Dr Goh Kok Hun, Otard Chew, D.C. Chen, Ang Kok Hua, Soh Kin Meng, Tang Yew Hoe, Wong Wing Choi, and Kong Jian Yuan for their invaluable assistance in this project.

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Authors and Affiliations

  1. Key Laboratory of New Technology for Construction of Cities in Mountain Area, Chongqing University, Chongqing, China

    R. H. Zhang, H. L. Liu & W. G. Zhang

  2. School of Civil Engineering, Chongqing University, Chongqing, China

    R. H. Zhang, W. Wang, L. Wang, H. L. Liu & W. G. Zhang

  3. School of Civil and Environmental Engineering, Nanyang Technological University, Singapore, Singapore

    A. T. C. Goh

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  1. A. T. C. Goh
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  2. R. H. Zhang
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  3. W. Wang
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  4. L. Wang
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  5. H. L. Liu
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  6. W. G. Zhang
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Correspondence to W. G. Zhang.

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Appendix

Appendix

1.1 Calculation of settlement δvm using trained neural network

From the connection weights for a trained neuron network, it is possible to develop a mathematical equation relating the input parameters and the single output parameter Y.

$$ Y = f_{\text{sig}} \left\{ {b_{o} + \sum\limits_{k = 1}^{h} {\left[ {w_{k} \cdot f_{\text{sig}} (b_{hk} + \sum\limits_{i = 1}^{m} {w_{ik} X_{i} )} } \right]} } \right\} $$

in which bo is the bias at the output layer, wk is the weight connection between neuron k of the hidden layer and the single output neuron, bhk is the bias at neuron k of the hidden layer (k = 1, h), wik is the weight connection between input variable i (i = 1, m) and neuron k of the hidden layer, Xi is the input parameter i, and fsig is the sigmoid (logistic) transfer function.

All inputs are scaled so that they correspond to roughly the same scale. Commonly chosen ranges are 0–1 or − 1 to 1. In this paper, the following linear scaling equation was used:

$$ x_{\text{norm}} = 2\frac{{x_{\text{actual}} - x_{\hbox{min} } }}{{x_{ \hbox{max} } - x_{ \hbox{min} } }} - 1 $$

in which xnorm is the normalized input value, xactual is the actual input value, xmax is the maximum value for x in the database, and xmin is the minimum value for x in the database.

Using the connection weights of the trained neural network, the following steps can be followed to calculate the surface settlement δvm:

  • Note—the following are names of inputs and outputs:

  • Note—inp(1) is S

  • Note—inp(2) is B

  • Note—inp(3) is He

  • Note—inp(4) is dG (GIII_level)

  • Note—inp(5) is dw

  • Note—inp(6) is T (thickness_of_GVI)

  • Note—inp(7) is N60

  • Note—outp(1) is δvm

  • if (inp(1) < 181) then inp(1) = 181

  • if (inp(1) > 2051) then inp(1) = 2051

  • inp(1) = 2 * (inp(1) − 181)/1870 − 1

  • if (inp(2) < 10) then inp(2) = 10

  • if (inp(2) > 80) then inp(2) = 80

  • inp(2) = 2 * (inp(2) − 10)/70 − 1

  • if (inp(3) < 17) then inp(3) = 17

  • if (inp(3) > 31) then inp(3) = 31

  • inp(3) = 2 * (inp(3) − 17)/14 − 1

  • if (inp(4) < − 18) then inp(4) = − 18

  • if (inp(4) > 14) then inp(4) = 14

  • inp(4) = 2 * (inp(4) + 18)/32 − 1

  • if (inp(5) < 0) then inp(5) = 0

  • if (inp(5) > 25) then inp(5) = 25

  • inp(5) = 2 * inp(5)/25 − 1

  • if (inp(6) < 2.6) then inp(6) = 2.6

  • if (inp(6) > 35) then inp(6) = 35

  • inp(6) = 2 * (inp(6) − 2.6)/32.4 − 1

  • if (inp(7) < 2) then inp(7) = 2

  • if (inp(7) > 36) then inp(7) = 36

  • inp(7) = 2 * (inp(7) − 2)/34 − 1

  • netsum = − 1.291875

  • netsum = netsum + inp(1) * − 0.6901473

  • netsum = netsum + inp(2) * − 0.3615491

  • netsum = netsum + inp(3) * 4.198993

  • netsum = netsum + inp(4) * 2.188482

  • netsum = netsum + inp(5) * − 1.155417

  • netsum = netsum + inp(6) * − 1.598871

  • netsum = netsum + inp(7) * 1.164234

  • feature2(1) = 1/(1 + exp(− netsum))

  • netsum = 0.7919517

  • netsum = netsum + inp(1) * 0.61715

  • netsum = netsum + inp(2) * 0.4019681

  • netsum = netsum + inp(3) * 0.1866688

  • netsum = netsum + inp(4) * − 4.599372E−02

  • netsum = netsum + inp(5) * − 1.746516E−02

  • netsum = netsum + inp(6) * 1.20566

  • netsum = netsum + inp(7) * − 0.4409994

  • feature2(2) = 1/(1 + exp(− netsum))

  • netsum = 1.085821

  • netsum = netsum + inp(1) * − 0.4582809

  • netsum = netsum + inp(2) * 6.445678E−02

  • netsum = netsum + inp(3) * 0.6881049

  • netsum = netsum + inp(4) * 1.213748

  • netsum = netsum + inp(5) * − 0.8388367

  • netsum = netsum + inp(6) * − 1.167404

  • netsum = netsum + inp(7) * − 8.517503E−02

  • feature2(3) = 1/(1 + exp(− netsum))

  • netsum = 1.856037

  • netsum = netsum + inp(1) * 0.2115245

  • netsum = netsum + inp(2) * − 4.320706E−02

  • netsum = netsum + inp(3) * 4.040041

  • netsum = netsum + inp(4) * − 4.886881

  • netsum = netsum + inp(5) * − 0.752166

  • netsum = netsum + inp(6) * − 1.786103

  • netsum = netsum + inp(7) * − 1.157851

  • feature2(4) = 1/(1 + exp(− netsum))

  • netsum = 1.061388

  • netsum = netsum + inp(1) * − 0.6574904

  • netsum = netsum + inp(2) * − 0.4014052

  • netsum = netsum + inp(3) * 0.1775331

  • netsum = netsum + inp(4) * 0.4532214

  • netsum = netsum + inp(5) * 0.1781249

  • netsum = netsum + inp(6) * 0.4760727

  • netsum = netsum + inp(7) * 0.2965346

  • feature2(5) = 1/(1 + exp(− netsum))

  • netsum = − 0.5267698

  • netsum = netsum + inp(1) * − 0.2550091

  • netsum = netsum + inp(2) * − 0.3073156

  • netsum = netsum + inp(3) * − 1.560975

  • netsum = netsum + inp(4) * 0.5731048

  • netsum = netsum + inp(5) * − 0.4519642

  • netsum = netsum + inp(6) * − 1.59372

  • netsum = netsum + inp(7) * 0.7332226

  • feature2(6) = 1/(1 + exp(− netsum))

  • netsum = 1.927015

  • netsum = netsum + inp(1) * 0.3402273

  • netsum = netsum + inp(2) * 2.628679

  • netsum = netsum + inp(3) * − 3.979784

  • netsum = netsum + inp(4) * − 0.330495

  • netsum = netsum + inp(5) * − 0.6688622

  • netsum = netsum + inp(6) * 1.308524

  • netsum = netsum + inp(7) * − 1.102017

  • feature2(7) = 1/(1 + exp(− netsum))

  • netsum = 8.759252

  • netsum = netsum + inp(1) * − 5.516189E−02

  • netsum = netsum + inp(2) * − 0.3365066

  • netsum = netsum + inp(3) * 0.8627753

  • netsum = netsum + inp(4) * 0.3397848

  • netsum = netsum + inp(5) * − 1.856737

  • netsum = netsum + inp(6) * − 0.7415764

  • netsum = netsum + inp(7) * 8.85948

  • feature2(8) = 1/(1 + exp(− netsum))

  • netsum = 2.466565

  • netsum = netsum + inp(1) * 0.2217221

  • netsum = netsum + inp(2) * 0.1526408

  • netsum = netsum + inp(3) * 1.34046

  • netsum = netsum + inp(4) * − 0.990819

  • netsum = netsum + inp(5) * − 1.822526

  • netsum = netsum + inp(6) * − 0.9529807

  • netsum = netsum + inp(7) * − 0.9858764

  • feature2(9) = 1/(1 + exp(− netsum))

  • netsum = 1.881297

  • netsum = netsum + inp(1) * 0.3176911

  • netsum = netsum + inp(2) * − 2.236971

  • netsum = netsum + inp(3) * 0.1299287

  • netsum = netsum + inp(4) * 2.105172E−04

  • netsum = netsum + inp(5) * − 1.174372

  • netsum = netsum + inp(6) * 1.467091

  • netsum = netsum + inp(7) * − 0.4857309

  • feature2(10) = 1/(1 + exp(− netsum))

  • netsum = 0.4162327

  • netsum = netsum + inp(1) * 0.129394

  • netsum = netsum + inp(2) * − 0.3469871

  • netsum = netsum + inp(3) * 0.4371619

  • netsum = netsum + inp(4) * − 0.9531114

  • netsum = netsum + inp(5) * − 2.983316

  • netsum = netsum + inp(6) * − 1.454373E−02

  • netsum = netsum + inp(7) * − 9.652765E−03

  • feature2(11) = 1/(1 + exp(− netsum))

  • netsum = 1.897018

  • netsum = netsum + inp(1) * 0.2087154

  • netsum = netsum + inp(2) * 3.596996E−02

  • netsum = netsum + inp(3) * − 0.8524722

  • netsum = netsum + inp(4) * 1.605694

  • netsum = netsum + inp(5) * − 0.7954149

  • netsum = netsum + inp(6) * − 1.616124

  • netsum = netsum + inp(7) * − 4.131005

  • feature2(12) = 1/(1 + exp(− netsum))

  • netsum = 0.5142819

  • netsum = netsum + inp(1) * 0.2149892

  • netsum = netsum + inp(2) * − 0.3928081

  • netsum = netsum + inp(3) * − 0.1017899

  • netsum = netsum + inp(4) * 0.4842592

  • netsum = netsum + inp(5) * 6.291191

  • netsum = netsum + inp(6) * 0.2999729

  • netsum = netsum + inp(7) * − 1.112641

  • feature2(13) = 1/(1 + exp(− netsum))

  • netsum = 2.069268

  • netsum = netsum + inp(1) * − 0.505486

  • netsum = netsum + inp(2) * − 0.6058267

  • netsum = netsum + inp(3) * 2.345797

  • netsum = netsum + inp(4) * 1.719014

  • netsum = netsum + inp(5) * 0.5359494

  • netsum = netsum + inp(6) * − 0.1276105

  • netsum = netsum + inp(7) * 0.6440793

  • feature2(14) = 1/(1 + exp(− netsum))

  • netsum = 0.6443895

  • netsum = netsum + inp(1) * − 7.038708E−02

  • netsum = netsum + inp(2) * − 0.2170187

  • netsum = netsum + inp(3) * − 0.312786

  • netsum = netsum + inp(4) * − 2.368865E−02

  • netsum = netsum + inp(5) * 0.257878

  • netsum = netsum + inp(6) * − 0.2491133

  • netsum = netsum + inp(7) * − 0.4220511

  • feature2(15) = 1/(1 + exp(− netsum))

  • netsum = 0.4904341

  • netsum = netsum + inp(1) * 0.1535041

  • netsum = netsum + inp(2) * − 0.4730047

  • netsum = netsum + inp(3) * − 0.1925746

  • netsum = netsum + inp(4) * − 0.4628625

  • netsum = netsum + inp(5) * 8.537738E−03

  • netsum = netsum + inp(6) * − 0.2814922

  • netsum = netsum + inp(7) * 0.1173803

  • feature2(16) = 1/(1 + exp(− netsum))

  • netsum = 0.4400855

  • netsum = netsum + inp(1) * 0.1396401

  • netsum = netsum + inp(2) * − 0.2313282

  • netsum = netsum + inp(3) * − 9.940303E−02

  • netsum = netsum + inp(4) * − 2.707647E−02

  • netsum = netsum + inp(5) * 2.895144E−02

  • netsum = netsum + inp(6) * − 0.3121159

  • netsum = netsum + inp(7) * − 0.2776932

  • feature2(17) = 1/(1 + exp(− netsum))

  • netsum = 0.5983613

  • netsum = netsum + inp(1) * 0.1868789

  • netsum = netsum + inp(2) * − 0.1206655

  • netsum = netsum + inp(3) * − 0.215089

  • netsum = netsum + inp(4) * − 0.2471716

  • netsum = netsum + inp(5) * − 0.2141457

  • netsum = netsum + inp(6) * − 0.329291

  • netsum = netsum + inp(7) * − 0.4845981

  • feature2(18) = 1/(1 + exp(− netsum))

  • netsum = 0.4151182

  • netsum = netsum + inp(1) * 8.671804E−03

  • netsum = netsum + inp(2) * 0.1263722

  • netsum = netsum + inp(3) * 5.846625E−03

  • netsum = netsum + inp(4) * 0.2899816

  • netsum = netsum + inp(5) * 0.2410112

  • netsum = netsum + inp(6) * 0.2769873

  • netsum = netsum + inp(7) * − 0.4476679

  • feature2(19) = 1/(1 + exp(− netsum))

  • netsum = − 0.4425702

  • netsum = netsum + inp(1) * − 1.434375

  • netsum = netsum + inp(2) * 0.4431465

  • netsum = netsum + inp(3) * − 1.025513

  • netsum = netsum + inp(4) * 0.1109371

  • netsum = netsum + inp(5) * − 9.429807E−02

  • netsum = netsum + inp(6) * 0.5597454

  • netsum = netsum + inp(7) * − 9.980071E−02

  • feature2(20) = 1/(1 + exp(− netsum))

  • netsum = 3.289471

  • netsum = netsum + inp(1) * − 0.6329505

  • netsum = netsum + inp(2) * − 0.665822

  • netsum = netsum + inp(3) * − 1.659413

  • netsum = netsum + inp(4) * 0.1685638

  • netsum = netsum + inp(5) * 0.2215762

  • netsum = netsum + inp(6) * − 1.89214

  • netsum = netsum + inp(7) * 1.266415

  • feature2(21) = 1/(1 + exp(− netsum))

  • netsum = 2.772295

  • netsum = netsum + inp(1) * − 0.7432334

  • netsum = netsum + inp(2) * − 0.3218333

  • netsum = netsum + inp(3) * 0.9600076

  • netsum = netsum + inp(4) * − 0.1534827

  • netsum = netsum + inp(5) * 0.1155839

  • netsum = netsum + inp(6) * 0.590845

  • netsum = netsum + inp(7) * 1.405272

  • feature2(22) = 1/(1 + exp(− netsum))

  • netsum = 0.699394

  • netsum = netsum + inp(1) * 0.5402433

  • netsum = netsum + inp(2) * − 0.570429

  • netsum = netsum + inp(3) * 1.212402

  • netsum = netsum + inp(4) * 0.248789

  • netsum = netsum + inp(5) * 0.4359573

  • netsum = netsum + inp(6) * − 1.275427

  • netsum = netsum + inp(7) * − 0.8044557

  • feature2(23) = 1/(1 + exp(− netsum))

  • netsum = − 0.6894171

  • netsum = netsum + inp(1) * 0.3192796

  • netsum = netsum + inp(2) * 1.542518E−02

  • netsum = netsum + inp(3) * − 2.872762

  • netsum = netsum + inp(4) * 3.81186

  • netsum = netsum + inp(5) * − 0.4486226

  • netsum = netsum + inp(6) * − 1.13396

  • netsum = netsum + inp(7) * − 4.139882

  • feature2(24) = 1/(1 + exp(− netsum))

  • netsum = − 1.298243

  • netsum = netsum + inp(1) * 0.2972046

  • netsum = netsum + inp(2) * 0.2035974

  • netsum = netsum + inp(3) * − 1.815358

  • netsum = netsum + inp(4) * 2.822014

  • netsum = netsum + inp(5) * − 1.268731

  • netsum = netsum + inp(6) * − 1.126329

  • netsum = netsum + inp(7) * − 3.281758

  • feature2(25) = 1/(1 + exp(− netsum))

  • netsum = 0.6860899

  • netsum = netsum + inp(1) * 0.265633

  • netsum = netsum + inp(2) * − 0.4884056

  • netsum = netsum + inp(3) * 2.743894

  • netsum = netsum + inp(4) * 1.703549

  • netsum = netsum + inp(5) * 0.5452071

  • netsum = netsum + inp(6) * 0.1099264

  • netsum = netsum + inp(7) * − 2.879378

  • feature2(26) = 1/(1 + exp(− netsum))

  • netsum = 0.7946451

  • netsum = netsum + inp(1) * 5.324183E−02

  • netsum = netsum + inp(2) * − 5.033239E−02

  • netsum = netsum + inp(3) * − 0.3123793

  • netsum = netsum + inp(4) * − 8.353267E−02

  • netsum = netsum + inp(5) * 0.1438493

  • netsum = netsum + inp(6) * − 0.2668161

  • netsum = netsum + inp(7) * − 0.2146216

  • feature2(27) = 1/(1 + exp(− netsum))

  • netsum = − 7.391814E−02

  • netsum = netsum + inp(1) * − 0.1939111

  • netsum = netsum + inp(2) * 0.7319494

  • netsum = netsum + inp(3) * − 0.5342189

  • netsum = netsum + inp(4) * 0.8869973

  • netsum = netsum + inp(5) * 2.731098

  • netsum = netsum + inp(6) * − 1.356026

  • netsum = netsum + inp(7) * − 0.5423686

  • feature2(28) = 1/(1 + exp(− netsum))

  • netsum = 1.13194

  • netsum = netsum + inp(1) * − 0.2158111

  • netsum = netsum + inp(2) * − 2.222815

  • netsum = netsum + inp(3) * 1.976162

  • netsum = netsum + inp(4) * 3.451401

  • netsum = netsum + inp(5) * 0.4072163

  • netsum = netsum + inp(6) * − 0.7472668

  • netsum = netsum + inp(7) * 0.5279559

  • feature2(29) = 1/(1 + exp(− netsum))

  • netsum = 0.3564414

  • netsum = netsum + inp(1) * − 0.7780534

  • netsum = netsum + inp(2) * 4.078527E−02

  • netsum = netsum + inp(3) * 6.616073

  • netsum = netsum + inp(4) * 4.244986

  • netsum = netsum + inp(5) * − 0.812762

  • netsum = netsum + inp(6) * − 0.6518921

  • netsum = netsum + inp(7) * 2.321477

  • feature2(30) = 1/(1 + exp(− netsum))

  • netsum = 0.9388736

  • netsum = netsum + inp(1) * − 0.3698379

  • netsum = netsum + inp(2) * − 0.3004004

  • netsum = netsum + inp(3) * − 2.026485

  • netsum = netsum + inp(4) * − 1.350157

  • netsum = netsum + inp(5) * 0.4177655

  • netsum = netsum + inp(6) * − 0.8056978

  • netsum = netsum + inp(7) * 2.132205

  • feature2(31) = 1/(1 + exp(− netsum))

  • netsum = 0.3580116

  • netsum = netsum + inp(1) * 0.1734966

  • netsum = netsum + inp(2) * 1.598544

  • netsum = netsum + inp(3) * − 0.4505351

  • netsum = netsum + inp(4) * 2.638014

  • netsum = netsum + inp(5) * − 0.3666077

  • netsum = netsum + inp(6) * − 0.1274794

  • netsum = netsum + inp(7) * − 4.812712E−02

  • feature2(32) = 1/(1 + exp(− netsum))

  • netsum = 0.6413804

  • netsum = netsum + inp(1) * − 9.110811E−02

  • netsum = netsum + inp(2) * − 5.199069E−02

  • netsum = netsum + inp(3) * − 0.1575698

  • netsum = netsum + inp(4) * 0.35893

  • netsum = netsum + inp(5) * − 0.1303621

  • netsum = netsum + inp(6) * − 0.2812227

  • netsum = netsum + inp(7) * − 0.2843491

  • feature2(33) = 1/(1 + exp(− netsum))

  • netsum = 0.6098645

  • netsum = netsum + inp(1) * − 0.1025472

  • netsum = netsum + inp(2) * − 0.2100613

  • netsum = netsum + inp(3) * − 0.2557751

  • netsum = netsum + inp(4) * 0.255746

  • netsum = netsum + inp(5) * 0.5620731

  • netsum = netsum + inp(6) * − 0.4973007

  • netsum = netsum + inp(7) * − 0.3934626

  • feature2(34) = 1/(1 + exp(− netsum))

  • netsum = 0.3177274

  • netsum = netsum + inp(1) * − 5.063349E−02

  • netsum = netsum + inp(2) * − 0.1723627

  • netsum = netsum + inp(3) * 0.1730534

  • netsum = netsum + inp(4) * 9.242076E−02

  • netsum = netsum + inp(5) * 9.507026E−02

  • netsum = netsum + inp(6) * − 0.3272622

  • netsum = netsum + inp(7) * − 0.3762533

  • feature2(35) = 1/(1 + exp(− netsum))

  • netsum = 2.820396

  • netsum = netsum + inp(1) * 0.9302508

  • netsum = netsum + inp(2) * 0.4490142

  • netsum = netsum + inp(3) * − 3.080552

  • netsum = netsum + inp(4) * 2.611539

  • netsum = netsum + inp(5) * 0.8675475

  • netsum = netsum + inp(6) * 1.274361

  • netsum = netsum + inp(7) * − 1.318633

  • feature2(36) = 1/(1 + exp(− netsum))

  • netsum = 0.1538475

  • netsum = netsum + feature2(1) * − 4.437468

  • netsum = netsum + feature2(2) * 0.712086

  • netsum = netsum + feature2(3) * 1.294311

  • netsum = netsum + feature2(4) * 1.704416

  • netsum = netsum + feature2(5) * 0.5779442

  • netsum = netsum + feature2(6) * − 1.645446

  • netsum = netsum + feature2(7) * 2.133373

  • netsum = netsum + feature2(8) * − 5.918158

  • netsum = netsum + feature2(9) * − 2.342819

  • netsum = netsum + feature2(10) * 1.459676

  • netsum = netsum + feature2(11) * 2.244305

  • netsum = netsum + feature2(12) * − 1.743306

  • netsum = netsum + feature2(13) * 1.00726

  • netsum = netsum + feature2(14) * − 2.884005

  • netsum = netsum + feature2(15) * − 0.2044309

  • netsum = netsum + feature2(16) * − 0.4903665

  • netsum = netsum + feature2(17) * − 0.220039

  • netsum = netsum + feature2(18) * − 0.1727754

  • netsum = netsum + feature2(19) * 0.1255937

  • netsum = netsum + feature2(20) * 1.176372

  • netsum = netsum + feature2(21) * 2.694249

  • netsum = netsum + feature2(22) * 1.716109

  • netsum = netsum + feature2(23) * 1.368555

  • netsum = netsum + feature2(24) * 3.22194

  • netsum = netsum + feature2(25) * − 3.121228

  • netsum = netsum + feature2(26) * − 1.761833

  • netsum = netsum + feature2(27) * − 0.2397653

  • netsum = netsum + feature2(28) * 1.420888

  • netsum = netsum + feature2(29) * 2.272231

  • netsum = netsum + feature2(30) * 3.941663

  • netsum = netsum + feature2(31) * − 2.163158

  • netsum = netsum + feature2(32) * 1.919529

  • netsum = netsum + feature2(33) * − 3.124574E−02

  • netsum = netsum + feature2(34) * − 2.081896E−02

  • netsum = netsum + feature2(35) * 4.236914E−03

  • netsum = netsum + feature2(36) * − 2.217847

  • outp(1) = 1/(1 + exp(− netsum))

  • outp(1) = 154.8 * (outp(1) − .1)/.8 + 4.2

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Goh, A.T.C., Zhang, R.H., Wang, W. et al. Numerical study of the effects of groundwater drawdown on ground settlement for excavation in residual soils. Acta Geotech. 15, 1259–1272 (2020). https://doi.org/10.1007/s11440-019-00843-5

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  • DOI: https://doi.org/10.1007/s11440-019-00843-5

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