Introduction to the development of an information management system for soil mix technology using artificial neural networks

This paper introduces current work in collating data from different projects using soil mix technology and establishing trends using artificial neural networks (ANNs). Variation in unconfined compressive strength as a function of selected soil mix variables (e.g., initial soil water content and binder dosage) is observed through the data compiled from completed and on-going soil mixing projects around the world. The potential and feasibility of ANNs in developing predictive models, which take into account a large number of variables, is discussed. The main objective of the work is the management and effective utilization of salient variables and the development of predictive models useful for soil mix technology design. Based on the observed success in the predictions made, this paper suggests that neural network analysis for the prediction of properties of soil mix systems is feasible. © ASCE 2011.

Soil mix technology for land remediation: Recent innovations

The past 15 years have seen increasing applications of soil mix technology in land remediation, mainly in stabilisation/solidification treatments and the construction of low-permeability cut-off walls and permeable reactive barriers; clear evidence of the versatility of the technology and its wide-ranging applications. This paper provides an overview of some of the recent innovations of soil mix technology in land remediation covering equipment developments and applications, including systems for rectangular panels and trenching systems, treatments, such as chemical oxidation, and additives, such as modified clays, zeolites and reactive magnesia. The paper also provides case studies for such innovations. The paper concludes with an overview of an on-going research and development project SMiRT (Soil Mix Remediation Technology) which will involve field trials on a contaminated site and will employ some of the innovations discussed in the paper. The range of significant advantages that soil mix technology now offers compared to other remediation techniques is likely to place this remediation method at the forefront of remedial options for future brownfield projects.

Metal Retention Experiments for the Design of Soil-Mix Technology Permeable Reactive Barriers

Soil-mix technology is effective for the construction of permeable reactive barriers (PRBs) for in situ groundwater treatment. The objective of this study was to perform initial experiments for the design of soil-mix technology PRBs according to (i) sorption isotherm, (ii) reaction kinetics and (iii) mass balance of the contaminants. The four tested reactive systems were: (i) a granular zeolite (clinoptilolite-GZ), (ii) a granular organoclay (GO), (iii) a 1:1-mixture GZ and model sandy clayey soil and (iv) a 1:1:1-mixture of GZ, GO and model soil. The laboratory experiments consisted of batch tests (volume 900mL and sorbent mass 18g) with a multimetal solution of Pb, Cu, Zn, Cd and Ni. For the adsorption experiment, the initial concentrations ranged from 0.01 to 0.5mM (2.5 to 30mg/L). The maximum metal retention was measured in a batch test (300mg/L for each metal, volume 900mL, sorbent mass 90-4.5g). The reactive material efficiency order was found to be GZ>GZ-soil mix>GZ-soil-GO mix>GO. Langmuir isotherms modelled the adsorption, even in presence of a mixed cations solution. Adsorption was energetically favourable and spontaneous in all cases. Metals were removed according to the second order reaction kinetics; GZ and the 1:1-mix were very similar. The maximum retention capacity was 0.1-0.2mmol/g for Pb in the presence of clinoptilolite; for Cu, Zn, Cd and Ni, it was below 0.05mmol/g for the four reactive systems. Mixing granular zeolite, organoclay and model soil increased the chemisorption. Providing that GZ is reactive enough for the specific conditions, GZ can be mixed to obtain the required sorption. Granular clinoptilolite addition to soil is recommended for PRBs for metal contaminated groundwater. The laboratory experiments consisted of batch tests with a multimetal solution of Pb, Cu, Zn, Cd and Ni. The four reactive materials chosen were granular zeolite, clinoptilolite and model sandy clayey soil, granular organoclay and a mix of clinoptilolite, model soil and organoclay. The reactive material efficiency order was found to be granular clinoptilolite>clinoptilolite-soil mix>clinoptilolite-soil-organoclay mix>granular organoclay. © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A simple prediction model for asphalt surface texture incorporating mix gradation and air voids

This paper presents the results of a preliminary study that seeks to show how asphalt grading and air voids are related to the texture depth of asphalt. The fiftieth percentile particle size (D50) is shown to be a good predictor of texture depth measurements from a collected database of field and laboratory studies. The D50 is used to normalise collected texture data and this 'relative texture' is shown to correlate with air voids. Regression analyses confirm that air voids should be included along with a measure of gradation in the interpretation of asphalt surface texture.The derived formulae are used to develop correlation charts.

Context-dependent decay of motor memories during skill acquisition.

Current models of motor learning posit that skill acquisition involves both the formation and decay of multiple motor memories that can be engaged in different contexts. Memory formation is assumed to be context dependent, so that errors most strongly update motor memories associated with the current context. In contrast, memory decay is assumed to be context independent, so that movement in any context leads to uniform decay across all contexts. We demonstrate that for both object manipulation and force-field adaptation, contrary to previous models, memory decay is highly context dependent. We show that the decay of memory associated with a given context is greatest for movements made in that context, with more distant contexts showing markedly reduced decay. Thus, both memory formation and decay are strongest for the current context. We propose that this apparently paradoxical organization provides a mechanism for optimizing performance. While memory decay tends to reduce force output, memory formation can correct for any errors that arise, allowing the motor system to regulate force output so as to both minimize errors and avoid unnecessary energy expenditure. The motor commands for any given context thus result from a balance between memory formation and decay, while memories for other contexts are preserved.

Soil mix technology for integrated remediation and ground improvement: From laboratory work to field trials

Relatively new in the UK, soil mix technology applied to the in-situ remediation of contaminated land involves the use of mixing tools and additives to construct permeable reactive in-ground barriers and low-permeability containment walls and for hot-spot soil treatment by stabilisation/ solidification. It is a cost effective and versatile approach with numerous environmental advantages. Further commercial advantages can be realised by combining this with ground improvement through the development of a single integrated soil mix technology system which is the core objective of Project SMiRT (Soil Mix Remediation Technology). This is a large UK-based R&D project involving academia-industry collaboration with a number of tasks including equipment development, laboratory treatability studies, field trials, stakeholder consultation and dissemination activities. This paper presents aspects of project SMiRT relating to the laboratory treatability study work leading to the design of the field trials. © 2012 American Society of Civil Engineers.