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

Rolling dynamic compaction (RDC), which employs non-circular module towed behind a tractor, is an innovative soil compaction method that has proven to be successful in many ground improvement applications. RDC involves repeatedly delivering high-energy impact blows onto the ground surface, which improves soil density and thus soil strength and stiffness. However, there exists a lack of methods to predict the effectiveness of RDC in different ground conditions, which has become a major obstacle to its adoption. For this, in this context, a prediction model is developed based on linear genetic programming (LGP), which is one of the common approaches in application of artificial intelligence for nonlinear forecasting. The model is based on in situ density-related data in terms of dynamic cone penetrometer (DCP) results obtained from several projects that have employed the 4-sided, 8-t impact roller (BH-1300). It is shown that the model is accurate and reliable over a range of soil types. Furthermore, a series of parametric studies confirms its robustness in generalizing data. In addition, the results of the comparative study indicate that the optimal LGP model has a better predictive performance than the existing artificial neural network (ANN) model developed earlier by the authors.R.A.T.M.Ranasinghe, M.B.Jaks, F.Pooya Nejad, Y.L.Ku

Phytocapping as a cost-effective and sustainable cover option for waste disposal sites in developing countries

Few waste disposal sites in developing countries are designed and operated as engineered sanitary landfills due to common technical and financial constraints. Phytocapping presents a natural soil-plant alternative to the conventional engineered landfill cover design. It requires less engineering input and has a lower cost than conventional impermeable covers as it only utilizes local recourses. It also offers the advantage of oxidating methane to reduce landfill greenhouse emissions. This type of covers has the potential to make a significant difference in the way that developing countries are capping their waste sites. This paper introduces the phytocap concept as well as discusses its relevance and advantages for developing countries

A Pre-Landing Assessment of Regolith Properties at the InSight Landing Site

This article discusses relevant physical properties of the regolith at the Mars InSight landing site as understood prior to landing of the spacecraft. InSight will land in the northern lowland plains of Mars, close to the equator, where the regolith is estimated to be ≥3--5 m thick. These investigations of physical properties have relied on data collected from Mars orbital measurements, previously collected lander and rover data, results of studies of data and samples from Apollo lunar missions, laboratory measurements on regolith simulants, and theoretical studies. The investigations include changes in properties with depth and temperature. Mechanical properties investigated include density, grain-size distribution, cohesion, and angle of internal friction. Thermophysical properties include thermal inertia, surface emissivity and albedo, thermal conductivity and diffusivity, and specific heat. Regolith elastic properties not only include parameters that control seismic wave velocities in the immediate vicinity of the Insight lander but also coupling of the lander and other potential noise sources to the InSight broadband seismometer. The related properties include Poisson’s ratio, P- and S-wave velocities, Young’s modulus, and seismic attenuation. Finally, mass diffusivity was investigated to estimate gas movements in the regolith driven by atmospheric pressure changes. Physical properties presented here are all to some degree speculative. However, they form a basis for interpretation of the early data to be returned from the InSight mission.Additional co-authors: Nick Teanby and Sharon Keda

Toward a generalized guideline to inform optimal site investigations for pile design

Insufficient or inappropriate soil testing can lead to a range of undesirable consequences, and yet there is no guideline for optimal investigation. This study analyses the influence of test type, number of boreholes, data interpretation, soil conditions, and structural configuration on site investigation performance. In addition to providing general recommendations, the relative sensitivity of these variables on performance is determined. Performance is assessed in terms of total expected project cost while implicitly incorporating the risk of damage from poor investigation. The framework for this study involves the use of randomly generated, variable, single layer virtual soils in a Monte Carlo analysis. It was found that optimal investigations can produce net savings in the order of several hundreds of thousands of Australian dollars, and key features of a future site investigation guideline are identified.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author

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

Riverbank collapse is a natural phenomenon in the evolution of rivers. Along the lower reaches of the River Murray, from downstream of East Front Road to the town of Wellington in South Australia, there were more than 100 riverbank collapse-related incidents reported between 2005 and 2010 in the forms of mass riverbank collapse, erosion, cracking, riparian tree leaning or collapse, as well as levee-related problems. The River Murray is the largest river in Australia. The objective of this paper is to develop a topographically-based framework that can be used, prior to undertaking detailed cross-sectional modeling or site investigation, to identify high risk areas susceptible to riverbank collapse over extensive reaches of the river. The proposed framework is based on the results of numerical analyses that have been undertaken using an integration of several approaches, which includes slope stability analysis using the limit equilibrium method with the assumption of a steady-state condition, identifying the actual locations of previously known riverbank collapse sites through the visual interpretation of historical, high-resolution aerial images, topography mapping using digital elevation models and a geographic information system, and interpretation of field and laboratory test results for model construction and geological and soil stratigraphy mapping. Back-analyses were used to estimate the likely in situ shear strength at the historical collapse sites. The results from the back-analyses were compared with those from field and laboratory testing. A total of 69 numerical analyses were undertaken at three different regions along the Lower River Murray, to identify the factors influencing the stability of the riverbank. Finally, cross-validation was used to measure the predictive performance of the proposed framework. This paper has demonstrated the efficacy of the proposed predicting framework as a useful and reliable tool for riverbank collapse hazard mapping.C. Liang, M.B. Jaksa, Y.L. Kuo, B. Ostendor

Influence of river level fluctuations and climate on riverbank stability

Riverbank collapse is a natural part of the evolution of rivers. An unprecedented period of dry conditions and low flows between 2005 and 2010 led to more than 162 reported riverbank collapse-related incidents along the Lower River Murray, in South Australia (downstream of Lock 1 at Blanchetown to Wellington). On 4 February, 2009 a 60×20m (70,000m3) section of riverbank, near Long Island Marina, Murray Bridge, collapsed into the river, taking with it three unoccupied vehicles and several trees. This paper aims to: (i) model the Long Island Marina riverbank collapse incident in both 2D and 3D; (ii) examine the influence and sensitivity of river level fluctuations and climatic factors on riverbank stability; and (iii) determine the dominant triggers affecting collapse. The analysis has been undertaken using an integration of the limit equilibrium method, transient unsaturated flow modeling and digital elevation model and high resolution aerial images from a Geographic Information System. The paper demonstrates the efficacy of this framework and the accuracy of the predictions. It also reveals that river fluctuation, rather than climatic influences, dominates riverbank collapse in the Lower River Murray.C. Liang, M.B. Jaksa, B. Ostendorf, Y.L. Ku

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

Rolling dynamic compaction (RDC), which involves the towing of a noncircular module, is now widespread and accepted among many other soil compaction methods. However, to date, there is no accurate method for reliable prediction of the densification of soil and the extent of ground improvement by means of RDC. This study presents the application of artificial neural networks (ANNs) for a priori prediction of the effectiveness of RDC. The models are trained with in situ dynamic cone penetration (DCP) test data obtained from previous civil projects associated with the 4-sided impact roller. The predictions from the ANN models are in good agreement with the measured field data, as indicated by the model correlation coefficient of approximately 0.8. It is concluded that the ANN models developed in this study can be successfully employed to provide more accurate prediction of the performance of the RDC on a range of soil types

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

This paper presents a framework for generating multi-layer, unconditional soil profiles with complex stratigraphy, which simulates the effects of natural erosion and sedimentation processes. The stratigraphy can have varying degrees of randomness and can include features such as lenses, as well as sloped and undulating layers. The method generates the soil comprising the layers using local average subdivision (LAS), and a random noise component that is added to the layer boundaries. The layers are created by generating coordinates of key points in the simulated ground profile, which are then interpolated with a customised, 2D, linear interpolation algorithm. The resulting simulations facilitate more accurate probabilistic modelling of geotechnical engineering systems because they provide more realistic geologies, such as those usually encountered in the ground. Fortran code implementing this framework is included as supplementary material.M. P. Crisp, M. B. Jaks, Y. L. Kuo, G. A. Fenton and D. V. Griffith