It is important to carry out this study since this type of marine clay covers large coastal area of west coast Malaysia. This type of marine clay was found on the main road connecting Klang to Perak and the road keeps experiencing undulation and uneven settlement which jeopardise the safety of the road users. The soil is indicated in the Generalised Soil Map of Peninsular Malaysia as a CLAY with alluvial soil on recent marine and riverine alluvium. Based on the British Standard Soil Classification and Plasticity Chart, the soil is classified as a CLAY with very high plasticity (CV). Results from laboratory test on physical properties and compressibility parameters show that Sabak Bernam Marine Clay (SBMC) is highly compressible, has low permeability and poor drainage characteristics. The compressibility parameters obtained for SBMC is in a good agreement with other researchers in the same field.

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This paper presents the development of quadriceps muscle model based on Functional Electrical Stimulation (FES).Artificial Neural Network (ANN) were used to study the impact of stimulation frequency, pulse width and pulse duration towards the output torque produce by paraplegic.722 data are used by randomly divide 70% for training, 15% for validation and another 15% is for testing process. Two types of training approaches which is Levenberg Marquardt Back propagation (LM) and Resilient Back propagation (RP) are used in developing of quadriceps muscle model. The model developed are validate with the clinical data to see the accuracy of the torque output predicted with the identified parameter. From the study, LM is found to be the most accurate with accuracy up to 99.98% The identified parameter used from model developed in this study will be used to control various strategies on the Functional Electrical Stimulation (FES) system.

Two methods of ground improvement have been proposed to overcome excessive and differential settlement problem of soft ground foundation for infrastructure such as road, highway and parking space namely polyurethane (PU) foam and cement grouting slab. It has been executed by excavating and replacing the soft soil at shallow depth with the proposed ground improvement methods. The ground improvement methods able to minimise the excessive and differential settlement as the shallow depth of soft soil is removed and replaced by the stiff materials, thus the load can be distributed evenly to the underlying soil. The comparison of performance between both methods are evaluated in this study by carried out finite element analysis for soft ground problem namely PLAXIS. The results show that the settlement can be reduced significantly to the tolerable amount by applying PU foam instead of cement grouting slab as the increase in thickness of cement grouting slab cause the increase in settlement. On the other hand, the increase in PU foam thickness has not contributed to further settlement as the PU foam is lightweight, however, the soil may experience upward displacement due to insufficient overburden load to counter uplift.

This paper presents the development of Quadriceps muscle model by using Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) based on Functional Electrical Stimulation (FES). The impacts of the output torque with different stimulation parameters (frequency, pulse width and sampling time) are investigated. These parameters will be used to develop the paraplegic muscle models. Muscle models developed are validated with the clinical data to evaluate the accuracy of the output torque predicted compare to the actual paraplegic muscle torque. From the study, ANN is found to be the most accurate model compare to ANFIS with the value of mean squared error of 0.3758. Both developed models in this study can be used in a various control strategies to control FES parameters during rehabilitation proses using FES.