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This study investigates the application of Electrical Resistivity Tomography (ERT) and Induced Polarization (IP) methods for subsurface characterization at a proposed construction site in Ipoh, Perak, Malaysia. The research aims to determine the subsurface resistivity distribution and identify geological features critical for construction planning. By employing ERT and IP techniques along 2 profiles, L1 and L2 at the site, the study evaluates variations in soil and rock characteristics and potential instability zones. The results indicate that areas with high resistivity (11 to 93 Ωm), corresponding to fractured limestone, exhibit greater stability, while regions with low resistivity (20-69 Ωm) and higher chargeability (5-10 msec) are clay-rich and pose potential risks. Borehole data were integrated to validate geophysical findings, revealing an absence of solid bedrock, necessitating careful foundation design to ensure structural stability. The combined use of ERT and IP using machine learning (twostep clustering) analysis provides a comprehensive understanding of subsurface conditions, supporting informed decision-making for construction projects and mitigating risks associated with subsurface uncertainties.

The multiple linear regression (MLR) soil strength models developed from electrical resistivity tomography and seismic refraction tomography are presented in this paper. The multiple linear regression method was used to estimate two dependent values, namely soil cohesion and friction angle, based on the values of two independent variables, namely resistivity and velocity. These parameters were regressed using regression statistics to create a multiple linear regression model using SPSS software. At the first stage, the MLR model results were needed to be evaluated to avoid bias. In this stage, the MLR for both soil cohesion and friction angle were checked for the coefficient of multiple determination, significance level (p-value), and multicolinearity. The next is the second stage, where the accuracy assessment of the MLR models was validated using root mean squared error (RMSE) and mean absolute percentage error (MAPE) for the statistical analysis. Based on the results of these analyses, the newly soil strength models from the geophysical data set for the near-surface study were successfully created. The soil strength models developed using MLR are reliable for imaging the subsurface in two-dimensional form, covering a larger area than the traditional method rather than laboratory tests, especially a large number of samples for site investigation. [ABSTRACT FROM AUTHOR]

Archaeological evidence has demonstrated that the Bujang Valley is Malaysia's richest archaeological site and served as the primary coastal centre. A study in the Bujang Valley found monuments related to trading activities and others that functioned as a temple related to the Hindu-Buddhist period. The main purpose of this study was to resolve issues and problems arising from previous studies related to the Bujang Valley civilisation, particularly in terms of iron studies. Geophysics plays a vital role in assisting archaeologists to obtain excellent preliminary results before they proceed with excavation and digging works. Therefore, the 2-D resistivity and ground- penetrating radar (GPR) methods were conducted to locate and map the potential iron smelting site at Site B2 (SB2). Three main characteristics that can be observed on the surface are a mound area, exposed clay bricks and surface finds. Two-D resistivity showed the resistivity values of a possible buried structure, with values > 800 Ωm. Radargram profiles showed the highest amplitude, indicating the reflections uncovered in the location in certain survey lines. This paper presents the first summary of research on the metallurgical sites in the Bujang Valley, the most important site in Malaysia. Geophysical methods, which rely on a physical contrast between buried archaeological features and the properties of the surrounding subsoil, can assist archaeological investigations. [ABSTRACT FROM AUTHOR]

This paper presents multiple linear regression (MLR) soil shear strength models developed from electrical resistivity and seismic refraction tomography data. The MLR technique is used to estimate the value of dependent variables of soil shear strength based on the value of two independent variables, namely, resistivity and velocity. These parameters were regressed using regression statistics technique for generating MLR model. The results of MLR model, which is based on the estimation of model dependent parameters (Log10 resistivity and Log10 velocity), calculated for p-value, are less than 0.05 and VIF value less than 10 for cohesion and friction angle models. This result shows that there is a statistically significant relationship between cohesion and friction angle with geophysical parameters (independent variables). The estimation accuracy of the MLR models is also conducted for verification, and the result shows that RMSE value for predicted cohesion and predicted friction angle is 0.77 kN/m2 and 1.73° which is close to zero. Meanwhile, MAPE value was found to be 4.57 % and 7.61 %, indicating highly accurate estimation for the MLR models of predicted cohesion and predicted friction angle. Based on the application of near surface, the study area was successfully classified into two regions, namely, medium and hard clayey sand. Thus, it is concluded that MLR method is suitable in estimating the subsurface characterization that covered more regions compared to the traditional method (laboratory test). [ABSTRACT FROM AUTHOR]

Geophysicists use electrical methods to investigate and characterise the earth's subsurface geology. This study aims to evaluate the performance of copper and conventional stainless-steel electrodes in subsurface tomographic investigations using electrical resistivity tomography (ERT) and induced polarisation (IP) at two sites in Penang, Malaysia. Site 1 and Site 2 employed profile lengths of 200 m and 100 m, with electrodes spacing of 5.0 m and 2.5 m, respectively. In the results of the final data inversion, it was observed that the ERT and IP tomographic models of Site 1 have the best convergence limits with percentage relative differences (copper as reference model) ranging from -70% to 70%, while Site 2 recorded -8% to 8%. The electrodes performance evaluation showed that population root mean square (RMS) error and population mean absolute percentage error (MAPE) of data points between copper and stainless-steel electrodes yielded large values for Site 1 with values above 28% and that of Site 2 was less than 4%. Hence, copper (good electrical conductivity and non-polarisable) electrodes have improved the quality and quantity of infield data which give low values of population RMS error and population MAPE compared to conventional stainless-steel electrodes, especially for large unit electrode spacing surveys. Most notably, this work has contributed to the understanding of the capability of copper electrodes in providing precise and reliable inversion models for subsurface tomographic investigations in pre- and post-land uses (engineering work), hydrogeology/groundwater, environmental studies, etc. [ABSTRACT FROM AUTHOR]