Mohd For Mohd Amin

Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems

A realistic analysis of rock deformation in response to any change in stresses is heavily dependent on the reliable determination of the rock properties as analysis inputs. Young’s modulus (E) provides great insight into the magnitude and characteristics of the rock mass/material deformation, but direct determination of Young’s modulus in the laboratory is time-consuming and costly. Therefore, basic rock properties such as point load strength index, P-wave velocity and Schmidt hammer rebound number have been used to estimate Young’s modulus. These rock properties can be easily measured in the laboratory. The main aim of this study was to develop two intelligent models based upon fuzzy logic and biological nervous systems in order to estimate Young’s modulus of sandstone for a set of known index properties drawn from laboratory tests. The database required to construct these models comprised a series of drill cores (96 samples of sandstone) from site investigation operations for a hydroelectric roller-compacted concrete (RCC) dam located in the Malaysian state of Sarawak. In the final stage of the present study, using the same data sets, multiple regression (MR) analysis was also proposed for comparison with the prediction results of both the fuzzy inference system (FIS) and artificial neural network (ANN) models. The ANN model was found to be far superior to FIS and MR in terms of several performance indices including root-mean-square error and ranking. Thus, from the results of this study, it was concluded that the models proposed herein could be utilised to estimate the E of similar rock types in practice.

A 3-tier tsunami vulnerability assessment technique for the north-west coast of Peninsular Malaysia

The 2004 tsunami that struck the Sumatra coast gave a warning sign to Malaysia that it is no longer regarded as safe from a future tsunami attack. Since the event, the Malaysian Government has formulated its plan of action by developing an integrated tsunami vulnerability assessment technique to determine the vulnerability levels of each sector along the 520-km-long coastline of the north-west coast of Peninsular Malaysia. The scope of assessment is focused on the vulnerability of the physical characteristics of the coastal area, and the vulnerability of the built environment in the area that includes building structures and infrastructures. The assessment was conducted in three distinct stages which stretched across from a macro-scale assessment to several local-scale and finally a micro-scale assessment. On a macro-scale assessment, Tsunami Impact Classification Maps were constructed based on the results of the tsunami propagation modelling of the various tsunami source scenarios. At this stage, highly impacted areas were selected for an assessment of the local hazards in the form of local flood maps based on the inundation modelling output. Tsunami heights and flood depths obtained from these maps were then used to produce the Tsunami Physical Vulnerability Index (PVI) maps. These maps recognize sectors within the selected areas that are highly vulnerable to a maximum tsunami run-up and flood event. The final stage is the development of the Structural Vulnerability Index (SVI) maps, which may qualitatively and quantitatively capture the physical and economic resources that are in the tsunami inundation zone during the worst-case scenario event. The results of the assessment in the form of GIS-based Tsunami-prone Vulnerability Index (PVI and SVI) maps are able to differentiate between the various levels of vulnerability, based on the tsunami height and inundation, the various levels of impact severity towards existing building structures, property and land use, and also indicate the resources and human settlements within the study area. Most importantly, the maps could help planners to establish a zoning scheme for potential coastline development based on its sensitivity to tsunami. As a result, some recommendations on evacuation routes and tsunami shelters in the potentially affected areas were also proposed to the Government as a tool for relief agencies to plan for safe evacuation.

Uniaxial compressive strength prediction through a new technique based on gene expression programming

Proper estimation of rock strength is a critical task for evaluation and design of some geotechnical applications such as tunneling and excavation. Uniaxial compressive strength (UCS) test can be measured directly in the laboratory; nevertheless, the direct UCS determination is time-consuming and expensive. In this study, feasibility of gene expression programming (GEP) model in indirect determination of UCS values of sandstone rock samples is examined. In this regard, several laboratory tests including Brazilian test, density test, slake durability test and UCS test were conducted on 47 samples of sandstone which were collected from the Dengkil, Malaysia. Considering multiple inputs, several GEP models were constructed to estimate UCS of the rock and finally, the best GEP model was selected. In order to indicate capability of the proposed GEP model, linear multiple regression (LMR) was also performed. It was found that the GEP model is superior to LMR one in terms of applied performance indices. Based on coefficient of determination (R2) of testing datasets, by proposing GEP model, it can be improved from 0.930 (which was obtained by LMR model) to 0.965. As a result, it is concluded that the proposed models in this study, could be utilized to estimate UCS of similar rock type in practice.