Mark Jaksa

Seismic site classification based on constrained modeling of measured HVSR curve in regolith sites

Publisher: Elsevier BV

Date: 07-2018

DOI: 10.1016/J.SOILDYN.2017.08.006

Data division for developing neural networks applied to geotechnical engineering

Publisher: American Society of Civil Engineers (ASCE)

Date: 04-2004

DOI: 10.1061/(ASCE)0887-3801(2004)18:2(105)

Settlement prediction of shallow foundations on granular soils using B-spline neurofuzzy models

Publisher: Elsevier BV

Date: 12-2003

DOI: 10.1016/J.COMPGEO.2003.09.004

ANN-based model for predicting the bearing capacity of strip footing on multi-layered cohesive soil

Publisher: Elsevier BV

Date: 04-2009

DOI: 10.1016/J.COMPGEO.2008.07.002

Identifying areas susceptible to high risk of riverbank collapse along the Lower River Murray

Publisher: Elsevier BV

Date: 09-2015

DOI: 10.1016/J.COMPGEO.2015.05.019

Three-Dimensional Discrete-Element Modeling of Geocell-Reinforced Ballast Considering Breakage

Publisher: American Society of Civil Engineers (ASCE)

Date: 04-2020

DOI: 10.1061/(ASCE)GM.1943-5622.0001552

Ground response to rolling dynamic compaction

Publisher: Thomas Telford Ltd.

Date: 06-2019

DOI: 10.1680/JGELE.18.00208

Abstract: Rolling dynamic compaction (RDC) is typically used for improving ground in situ or compacting fill in thick lifts. In many project applications, the effects of RDC are verified by way of testing that is undertaken pre- and/or post-compaction. This study presents results from a full-scale field trial that involved placing an earth pressure cell (EPC) and accelerometers at a depth of 0·7 m within a 1·5 m thick layer of homogeneous sandy gravel to measure the response to RDC in real-time. Double integration of acceleration–time data enabled settlement to be inferred, while the EPC measured the change in stress due to impact. The maximum change in vertical stress recorded over the 80 passes undertaken was approximately 1100 kPa. During a typical module impact, the loading and unloading response occurred over a duration of approximately 0·05 s. The acceleration response of RDC was measured in three orthogonal directions, with the vertical accelerations dominant.

Three-dimensional modeling of geocell-reinforced straight and curved ballast embankments

Publisher: Elsevier BV

Date: 10-2018

DOI: 10.1016/J.COMPGEO.2018.05.011

Closure to inaccuracies associated with estimating random measurement errors

Publisher: American Society of Civil Engineers (ASCE)

Date: 1999

DOI: 10.1061/(ASCE)1090-0241(1999)125:1(81.X)

Predicting the effectiveness of rolling dynamic compaction using genetic programming

Publisher: Thomas Telford Ltd.

Date: 11-2017

DOI: 10.1680/JGRIM.17.00009

Abstract: Rolling dynamic compaction (RDC) is a soil compaction method that involves impacting the ground with a non-circular roller. This technique is currently in widespread use internationally and has proven to be suitable for many compaction applications, with improved capabilities over traditional compaction equipment. However, there is still a lack of knowledge about a priori estimation of the effectiveness of RDC on different soil profiles. To this end, the aim of this paper is to develop a reliable predictive tool based on a machine-learning approach: linear genetic programming (LGP). The models are developed from a database of cone penetration test (CPT)-based case histories. It is shown that the developed LGP-based correlations yield accurate predictions for unseen data and, in addition, that the results of a parametric study demonstrate its generalisation capabilities. Furthermore, the selected optimal LGP-based model is found to yield superior performance when compared with an artificial neural network model recently developed by the authors. It is concluded that the LGP-based model developed in this study is capable of providing reliable predictions of the effectiveness of RDC under various ground conditions.

Improved analytical solution for toppling stability analysis of rock slopes

Publisher: Elsevier BV

Date: 12-2008

DOI: 10.1016/J.IJRMMS.2008.01.009

Application of artificial neural networks for predicting the impact of rolling dynamic compaction using dynamic cone penetrometer test results

Publisher: Elsevier BV

Date: 04-2017

DOI: 10.1016/J.JRMGE.2016.11.011

A field-based study of the effectiveness of rolling dynamic compaction

Publisher: Elsevier

Date: 2015

DOI: 10.1016/B978-0-08-100698-6.00014-3

Estimating bedrock depth in the case of regolith sites using ambient noise analysis

Publisher: Elsevier BV

Date: 09-2018

DOI: 10.1016/J.ENGGEO.2018.06.022

Neural network prediction of the reliability of heterogeneous cohesive slopes

Publisher: Wiley

Date: 18-01-2016

DOI: 10.1002/NAG.2496

A method for generating virtual soil profiles with complex, multi-layer stratigraphy

Publisher: Informa UK Limited

Date: 18-12-2019

DOI: 10.1080/17499518.2018.1554817

Failure mechanisms of geocell walls and junctions

Publisher: Elsevier BV

Date: 04-2019

DOI: 10.1016/J.GEOTEXMEM.2018.11.003

The failure behaviour of poorly cemented sands at a borehole wall using laboratory tests

Publisher: Elsevier BV

Date: 07-2015

DOI: 10.1016/J.IJRMMS.2015.03.037

Load-settlement behavior modeling of single piles using artificial neural networks and CPT data

Publisher: Elsevier BV

Date: 09-2017

DOI: 10.1016/J.COMPGEO.2017.04.003

Inaccuracies associated with estimating random measurement errors

Publisher: American Society of Civil Engineers (ASCE)

Date: 1999

DOI: 10.1061/(ASCE)1090-0241(1999)125:1(79.2)

Intelligent computing for predicting axial capacity of drilled shafts

Publisher: American Society of Civil Engineers

Date: 10-03-2009

DOI: 10.1061/41022(336)4

Genetic programming for predictions of effectiveness of rolling dynamic compaction with dynamic cone penetrometer test results

Publisher: Elsevier BV

Date: 08-2019

DOI: 10.1016/J.JRMGE.2018.10.007

Toppling mechanisms of rock slopes considering stabilization from the underlying rock mass

Publisher: Elsevier BV

Date: 02-2010

DOI: 10.1016/J.IJRMMS.2009.11.008

The Influence of Site Investigation Scope on Pile Design in Multi-Layered, 2D Variable Ground

Publisher: American Society of Civil Engineers

Date: 06-2017

DOI: 10.1061/9780784480717.037

Predicting settlement of shallow foundations using neural networks

Publisher: American Society of Civil Engineers (ASCE)

Date: 09-2002

DOI: 10.1061/(ASCE)1090-0241(2002)128:9(785)

Editorial for Special Issue “Applications of Artificial Intelligence and Machine Learning in Geotechnical Engineering”

Publisher: MDPI AG

Date: 22-09-2021

DOI: 10.3390/GEOSCIENCES11100399

Abstract: Since its inception in the mid-1950s [...]

Effect of borehole location on pile performance

Publisher: Informa UK Limited

Date: 04-05-2020

DOI: 10.1080/17499518.2020.1757721

Neural network based stochastic design charts for settlement prediction

Publisher: Canadian Science Publishing

Date: 02-2005

DOI: 10.1139/T04-096

Abstract: Traditional methods of settlement prediction of shallow foundations on granular soils are far from accurate and consistent. This can be attributed to the fact that the problem of estimating the settlement of shallow foundations on granular soils is very complex and not yet entirely understood. Recently, artificial neural networks (ANNs) have been shown to outperform the most commonly used traditional methods for predicting the settlement of shallow foundations on granular soils. However, despite the relative advantage of the ANN based approach, it does not take into account the uncertainty that may affect the magnitude of the predicted settlement. Artificial neural networks, like more traditional methods of settlement prediction, are based on deterministic approaches that ignore this uncertainty and thus provide single values of settlement with no indication of the level of risk associated with these values. An alternative stochastic approach is essential to provide more rational estimation of settlement. In this paper, the likely distribution of predicted settlements, given the uncertainties associated with settlement prediction, is obtained by combining Monte Carlo simulation with a deterministic ANN model. A set of stochastic design charts, which incorporate the uncertainty associated with the ANN method, is developed. The charts are considered to be useful in the sense that they enable the designer to make informed decisions regarding the level of risk associated with predicted settlements and consequently provide a more realistic indication of what the actual settlement might be.Key words: settlement prediction, shallow foundations, neural networks, Monte Carlo, stochastic simulation.

Effect of sample location on the reliability based design of pad foundations

Publisher: Informa UK Limited

Date: 09-2007

DOI: 10.1080/17499510701697377

Prediction of ultimate axial load-carrying capacity of piles using a support vector machine based on CPT data

Publisher: Elsevier BV

Date: 2014

DOI: 10.1016/J.COMPGEO.2013.08.001

Depth of influence of rolling dynamic compaction

Publisher: Thomas Telford Ltd.

Date: 05-2019

DOI: 10.1680/JGRIM.18.00117

Abstract: The depth of influence of rolling dynamic compaction (RDC) was investigated in a field trial using a four-sided impact roller. Earth pressure cells (EPCs) were placed at varying depths at a site consisting of homogeneous soil conditions. EPCs measured pressures imparted by RDC at 3·85 m depth however, the largest magnitudes of pressure were confined to the top 2 m beneath the ground surface. These results were complemented by field density data, penetrometer and geophysical testing. A number of published case studies using the 8 t four-sided impact roller, for either improving ground in situ or compacting soil in thick layers, are summarised in this paper. Finally, equations are presented that predict first, the effective depth of improvement, appropriate for determining the depth to which the ground can be significantly improved in situ, and, second, the depth of major improvement for RDC, appropriate for thick-layer compaction.

Towards reliable and effective site investigations

Publisher: Thomas Telford Ltd.

Date: 03-2005

DOI: 10.1680/GEOT.2005.55.2.109

Abstract: It is widely appreciated that, in civil engineering and building projects, the largest element of financial and technical risk usually lies in the ground. Almost exclusively, the scope of geotechnical investigations is governed not by what is needed to characterise the subsurface conditions appropriately but, rather, by how much the client and project manager are willing to spend. There is often little correlation between the variability of the ground and the scope of the investigation. This paper presents the results of a Monte Carlo simulation incorporating many 3D single-layer soil profiles with different statistical characteristics. A three-storey building founded on nine pad footings is used to assess the reliability of various site investigation scopes and test methods. The pad footings are designed on the basis of settlement, and are examined using 3D finite element analysis and Schmertmann's method. It is observed, as expected, that the likelihood of underdesigning or overdesigning a footing decreases as the scope of the investigation increases. The relationship between these likelihoods and the variability of the ground is presented.

An experimental study of the active arching effect in soil using the digital image correlation technique

Publisher: Elsevier BV

Date: 04-2019

DOI: 10.1016/J.COMPGEO.2018.12.023

Prediction of pile settlement using artificial neural networks based on standard penetration test data

Publisher: Elsevier BV

Date: 09-2009

DOI: 10.1016/J.COMPGEO.2009.04.003

Closure to ‘predicting settlement of shallow foundations using neural networks’

Publisher: American Society of Civil Engineers (ASCE)

Date: 12-2003

DOI: 10.1061/(ASCE)1090-0241(2003)129:12(1175)

Measuring the Risk of Geotechnical Site Investigations

Publisher: American Society of Civil Engineers

Date: 14-10-2007

DOI: 10.1061/40914(233)2

Mechanical Performance of Jute Fiber-Reinforced Micaceous Clay Composites Treated with Ground-Granulated Blast-Furnace Slag

Publisher: MDPI AG

Date: 14-02-2019

DOI: 10.3390/MA12040576

Abstract: The combined capacity of Jute Fibers (JF), the reinforcement, and Ground-Granulated Blast-Furnace Slag (GBFS), the binder, was examined as a sustainable solution towards ameliorating the inferior engineering properties of micaceous clays. A total of sixteen JF + GBFS mix designs, i.e., JF (% by total mass) = {0, 0.5, 1.0, 1.5} and GBFS (% by total mass) = {0, 3, 6, 9}, were tested for unconfined compression (UC) strength for those mix designs containing GBFS, curing was allowed for 7 and 28 days prior to testing. Scanning electron microscopy (SEM) studies were also carried out to observe the evolution of fabric in response to JF, GBFS and JF + GBFS amendments. The greater the JF content the higher the developed strength and stiffness up to 1% JF, beyond of which the effect of JF-reinforcement led to some adverse results. The JF inclusions, however, consistently improved the ductility and toughness of the composite. The addition of GBFS to the JF-reinforced s les improved the soil–fiber connection interface, and thus led to further improvements in the composite’s strength, stiffness and toughness. The mix design “1% JF + 9% GBFS” managed to satisfy ASTM’s strength criterion and hence was deemed as the optimum choice in this investigation. Finally, a non-linear, multivariable regression model was developed and validated to quantify the peak UC strength as a function of the composite’s index properties. The proposed model contained a limited number of fitting parameters, all of which can be calibrated by little experimental effort, and thus implemented for preliminary design assessments.

Identification of sample path smoothness in soil spatial variability

Publisher: Elsevier BV

Date: 11-2019

DOI: 10.1016/J.STRUSAFE.2019.101870

Random field modeling of cpt data

Publisher: American Society of Civil Engineers (ASCE)

Date: 12-2000

DOI: 10.1061/(ASCE)1090-0241(2000)126:12(1212)

Influence of river level fluctuations and climate on riverbank stability

Publisher: Elsevier BV

Date: 2015

DOI: 10.1016/J.COMPGEO.2014.08.012

Prediction of pile settlement using artificial neural networks based on cone penetration test data

Publisher: American Society of Civil Engineers

Date: 15-02-2010

DOI: 10.1061/41095(365)144

Neural network prediction of pullout capacity of marquee ground anchors

Publisher: Elsevier BV

Date: 04-2005

DOI: 10.1016/J.COMPGEO.2005.02.003

A dimensional description of the unconfined compressive strength of artificially cemented fine-grained soils

Publisher: Informa UK Limited

Date: 12-02-2020

DOI: 10.1080/01694243.2020.1717804

Pullout capacity of small ground anchors by direct cone penetration test methods and neural networks

Publisher: Canadian Science Publishing

Date: 06-2006

DOI: 10.1139/T06-029

Abstract: Marquees are temporary light structures that are connected to the ground by small anchors that act in tension and are designed to resist uplift forces. Due to the temporary nature of these structures, little, if any, attention is given to the pullout capacity of the anchors used to secure them. Failures of such structures are not rare and have resulted in deaths and tens of thousands of dollars of damage. This paper reports on a series of 119 in situ anchor pullout tests conducted on rough mild steel anchors of various lengths, cross-sectional shapes, and areas. Comparison tests are carried out to investigate the impact of the factors affecting the pullout capacity of small anchors. Six methods that determine the axial pile capacity directly from cone penetration test (CPT) data are presented and used to calculate the pullout capacity of small ground anchors. The capacities obtained from these CPT-based methods are compared with predictions from a recently developed artificial neural network (ANN) model. The actual pullout loads are compared with predictions from the CPT and ANN methods, and statistical analyses are carried out to evaluate and rank their performance. The results indicate that the ANN-based method provides superior predictions of the pullout capacity of small ground anchors, whereas the Schmertmann method provides the best performance of the CPT-based techniques examined.Key words: ground anchors, pullout capacity, cone penetration test, artificial neural networks.

Discussion: Towards reliable and effective site investigations

Publisher: Thomas Telford Ltd.

Date: 10-2005

DOI: 10.1680/GEOT.2005.55.8.625

Passive noise datasets at regolith sites

Publisher: Elsevier BV

Date: 10-2018

DOI: 10.1016/J.DIB.2018.08.055

Foundation Engineering in the Face of Uncertainty

Publisher: American Society of Civil Engineers

Date: 04-03-2013

DOI: 10.1061/9780784412763

Characterising site investigation performance in multiple-layer soils and soil lenses

Publisher: Informa UK Limited

Date: 12-08-2020

DOI: 10.1080/17499518.2020.1806332

A transfer coefficient method for rock slope toppling

Publisher: Canadian Science Publishing

Date: 2009

DOI: 10.1139/T08-094

Abstract: A common block toppling mode is adopted for analysing toppling stability of rock slopes. A transfer coefficient method is developed based on Goodman and Bray’s kinematic model, by introducing two parameters: the transfer coefficient and equivalent toppling weight, which, given the geometrical and geotechnical parameters of the slope, can be determined as functions of the relative position of the block to the slope crest. Accounting for the case that the block base is not normal to the dominant discontinuities, the arithmetic is extended. The method describes a concise relationship between the normal force of the blocks and their weight represented by their equivalent toppling weight. The analysis process is formulated and easily implemented. A spreadsheet procedure is presented and a typical case is illustrated. It is demonstrated that the transfer coefficient method provides a considerably straightforward means for analysing block toppling of rock slopes and is computationally very efficient.

Inaccuracies associated with estimating random measurement errors

Publisher: American Society of Civil Engineers (ASCE)

Date: 05-1997

DOI: 10.1061/(ASCE)1090-0241(1997)123:5(393)

University Of Adelaide

Location: Australia

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Coffey And Partners

Location: Australia

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B. C. Tonkin & Associates

Location: Australia

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Linkage Projects - Grant ID: LP0560741

Start Date: 02-2005

End Date: 12-2009

Amount: $86,793.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP160101008

Start Date: 12-2016

End Date: 06-2020

Amount: $375,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP220100186

Start Date: 2023

End Date: 12-2025

Amount: $450,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP0561914

Start Date: 03-2006

End Date: 02-2011

Amount: $735,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP0211195

Start Date: 2002

End Date: 12-2006

Amount: $223,000.00

Funder: Australian Research Council

Linkage - International - Grant ID: LX0667328

Start Date: 02-2006

End Date: 12-2008

Amount: $17,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP220103611

Start Date: 10-2022

End Date: 10-2025

Amount: $250,000.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP120101761

Start Date: 2012

End Date: 12-2017

Amount: $320,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP180100235

Start Date: 2019

End Date: 12-2023

Amount: $342,831.00

Funder: Australian Research Council

Discovery Projects - Grant ID: DP230100047

Start Date: 2023

End Date: 12-2026

Amount: $450,000.00

Funder: Australian Research Council

Linkage Projects - Grant ID: LP200200216

Start Date: 03-2022

End Date: 03-2025

Amount: $376,265.00

Funder: Australian Research Council

Linkage Infrastructure, Equipment And Facilities - Grant ID: LE120100011

Start Date: 01-2012

End Date: 12-2012

Amount: $700,000.00

Funder: Australian Research Council