Effects of differently stabilised lateritic soils on its geotechnical properties and dust generations
Well designed roads are the vital socio-economic pathways of a nation. However, construction of unpaved roads in rural areas are often hindered by geographical limitation and are deemed costly and energy inefficient. This research assesses the soil stabiliser performance through the evaluation of th...
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| Format: | Thesis |
| Published: |
2015
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/7486/ http://eprints.uthm.edu.my/7486/1/lim_sin_mei.pdf |
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| Summary: | Well designed roads are the vital socio-economic pathways of a nation. However,
construction of unpaved roads in rural areas are often hindered by geographical
limitation and are deemed costly and energy inefficient. This research assesses the
soil stabiliser performance through the evaluation of the geotechnical properties of
the unstructured and structured soil using frontline laboratory testing on soils
collected from 2 case study sites in Johor, Malaysia. There were treated with 2, 4,
and 6% of a powder stabiliser (PPS) and 0.096, 0.1 19, and 0.144% of a liquid
stabiliser (PLS) and individually cured under controlled conditions to 7, 14 and 28
days. The outcomes confirmed that that with the use of reliable stabilisers and ageing,
the strength and stiffness behaviour of the structured soils improved significantly as
desired. The 44 in-house research datasets collected were complemented with nearly
300 sets of published data to establish correlations through simple and multiple
regression analysis using the tools made available in MINITAB 17. Various multiple
correlation equations presented in this thesis bore high coefficient of significance (pvalue
< 0.05); conclusively demonstrating good prediction. A new soil stabilisation
model: [S] [MI = [L] where [ ] represents the appropriate matrix formulation for [S],
properties of the unstructured soil; [MI, stabilised soil properties and [L], desired
designed properties for the structured (treated) soils through the conceptual use of
Artificial Neural Network (ANN) is proposed and demonstrated in this research.
"NeuroShell Predictor" was the engine used in the ANN training and testing. The
reliable correlations developed concurrently in this research formed the hidden
neuron layer of the ANN. Performance and acceptance of the ANN is evaluated
using R-squared ( R >~ 0.9) and the accuracy of the ANN models were measured
using the Root Mean Square Error (RMSE). This thesis also adopted the holistic
approach to emphasise that soil stabilisation must globally and concurrently
encompass satisfactorily the two extreme scenarios of soil softening under wet
conditions and hazardous dust formation during extreme dry conditions. Hence, an
innovative dust assessment technique was developed to assess the improvement in
the "soil erosion index" with stabilisation. |
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