Interpretation of 2D resistivity with engineering characterization of subsurface exploration in Nusajaya Johor, Malaysia

2-D resistivity technique and pole-dipole array with spacing of 2 m electrode and total spacing of 80 m were adopted to map and characterize the shallow subsurface in a sedimentary area at Nusajaya, Johor. Geological field mapping and laboratory testing were conducted to determine weathering grades.Res2Dinv software was used to generate the inversion model resistivity. The result shows sandstone contains iron mineral (30-1000ohm-m) and weathered sandstone (500-1000 ohm-m). The lowest layer represents sandstone and siltstone with the highest range from 1500 through 5000 ohm-m. The weathering grade IV and V of sandstone in the actual profile indicates the range from 30 to 1000 ohm-m, whereas grade II and III in ground mass matched the higest range. Overall, the increase of weathering grade influenced both the physical properties and strength of rocks

Physico-mechanical characteristics of tropical granite boulders in weathered heterogeneous zones for geotechnical design purposes

The presence of isolated or clustered granite boulders in tropical weathered masses commonly formed a very thick heterogeneous zone in weathering Zone 3 to 5. The boulders with various characteristics are always misinterpreted during the geotechnical design process due to being poorly understood and studied. This study aims to determine the physico-mechanical characteristics of boulders in different weathering zones for design purposes. The parameters studied include boulder types, porosity, density, durability, point load and uniaxial compressive strength. Results revealed boulders in weathering Zone 3, 4 and 5 can be classified into Type A, B and C, respectively. These boulders have significant differences in physical and mechanical properties. The boulders in Zone 4 and 5 consists of corestone, ringlets and saprolites and the boulders in Zone 3 have no ringlets. Corestones have the highest durability, point load and compressibility strength with a median of 94.5%, 7.80 MPa and 187.07 MPa, respectively. The ringlets possess the highest porosity range of 23.3%–31.3% compared with saprolite. Saprolite in Zone 5 has the lowest durability, point load and compressive strength with respectively less than 7%, 0.22 MPa and 1 MPa. The various characteristics of the boulders in different weathering zones could influence the geotechnical design model

Potential effects of quarry operations on workforce safety

It has been globally acknowledged that the important source of materials for the ever-active construction industry comes from quarry sites. Owing to this, the demand for building materials such as aggregates, cement and sand has increased to accommodate the building of houses, offices, industrial buildings, road systems and other infrastructures. Aggregates, as one of the basic building materials produced by the quarry industry is critically needed. The quarry and mines industry is one of the important contributors to the country's economy. However, accidents also do occur within the workforce of the Malaysian quarry industry

Assessment of quarry volume using 2-D resistivity imaging method

A geophysical survey was carried out at Masai quarry, Johor Darul Takzim to estimate the volume of overburden and rock can be excavated. A total of 6 survey lines were conducted in the study area using 2-D resistivity imaging; carried out with pole-dipole array, 5 m minimum electrode spacing with ABEM SAS4000 system. The results show the study area was divided into two main zones. The first zone was top layer/residual soil with resistivity value of 700 Ωm. The second zone was fractured granitic bedrock with resistivity value of >1000 Ωm and depth of 10-75 m. The overburden consists of residual soil which mixed with boulders. The estimated volume of the overburden is 9,811,831.15 m3

Estimating the friction angle of black shale core specimens with hybrid-ann approaches

Shear strength parameters of rock play a significant role in the design stage of various geotechnical structures such as earth dams, embankments, foundations and tunnels. The direct determination of these parameters in laboratory is time consuming and expensive. Additionally, preparing core specimens with a good-quality is sometimes difficult, especially in weathered and highly fractured rocks. This paper presents an indirect determination of internal friction angle of shale rock specimens through two hybrid neural net based models that combine artificial neural net with genetic algorithm (GA-ANN) and imperialist competitive algorithm (ICA-ANN). In fact, GA and ICA were utilized to improve the efficiency of ANN predictive model via the weights and biases adjustment. To achieve this aim, an extensive experimental program was designed, according to which a series of black shale specimens were characterized using various laboratory tests, including p-wave velocity, Schmidt hammer, point load and triaxial compression. After establishing a proper database for the analysis, simple and multiple regression as well as hybrid intelligent models were developed to predict the internal friction angle of the shale specimens. To compare the obtained results from the models, several performance statistical indices were computed. The results indicated that simple and multiple regression models are not good enough in predicting the internal friction angles. Concluding remark is that the proposed intelligent models are superior in comparison with simple and multiple regression models. Using the coefficient of determination as performance measure, the quality of the developed GA-ANN model was evaluated as 0.917 and 0.909 for training and testing datasets, respectively whereas these values were achieved as 0.960 and 0.956 for the ICA-ANN model. This means that the ICA-ANN model can provide higher performance capacity in estimating the internal friction angles as compared to the GA-ANN. In addition, the results of other performance indices, i.e. variance account for and root mean square error confirmed that the hybrid ICA-ANN predictive model can be introduced as a new technique for predicting the internal friction angle of shale rock specimens in practice

Preliminary assessment of tropically weathered limestone in Sri Lanka for blastability

Sri Lanka is situated in a tropical climate where rainfall takes place throughout the year. As limestones in Sri Lanka are frequently exposed to rain water, limestone absorbs water and forms carbonic acid. With various geological discontinuities such as faults, folds, joints, water flows through these cavities. The weathering process along these cracks or cavities takes place at a faster rate. This further enhances the process of dissolution resulting in change in geomechanical properties of limestone. Limestone deposit at Aruwakkalu based on rock structure is classified as (i) Heavily cracked, frequent weak joints, weakly cemented layers (ii) Thin, well-cemented layers with tight joints (iii) Massive intact rock. This limestone deposit is also classified as a bedding plane dipping into the slope face, bedding plane dipping into a cut slope face and other cases. Existing system of rockmass classification at Sri Lanka is described in this paper. Thus for Aruwakkalu limestone deposit, rockmass can be classified based on type of rock structure, Blastability Index (BI), RQD%, degree of weathering and degree of hardness. Average powder factor of 0.15 kg/t can be correlated with BI

Advanced Analysis of Collision-Induced Blast Fragmentation in V-Type Firing Pattern

The firing pattern of blastholes influences the geometric aspects of a blast design in terms of change in blasting burden and spacing. This in turn changes the effective stiffness of a blasthole and confinement of the explosive and aids in better fragmentation. However, during the blasting, the fragments tend to collide and further fragment the rock. In comparison with other patterns, the V-type firing pattern increases the chances of collision between the fragments during flight. The process is scantly documented and accordingly field experiments were conducted using three firing patterns, viz., line, diagonal, and V-type, in a mine with minor variation in rock factor and minor to moderate changes in blast design variables. Sixteen blast design variables such as burden, spacing, charge per hole, in-hole charge density, etc. along with firing pattern were considered for the analysis and fragmentation modeled with the help of surface response analysis and artificial neural networks. The analysis revealed that there is a significant influence of firing patterns on fragmentation. The V-type pattern showed significant reduction in fragment sizes that can be ascribed to in-flight collision processes. A surface response model was developed using advanced ANOVA and resulted in an adjusted R2 and RMSE of 0.89, 0.025, respectively. Further, modeling with ANN was attempted that showed better results than ANOVA with R2 and RMSE of 0.96 and 0.040 in training, and 0.884 and 0.049 in validation tests. Since, diagonal and V-type patterns have similar design parameters, the reduction in fragment size in the former pattern can be ascribed to the collision of rock fragments during their flight in blasting

Computational optimized finite element modelling of mechanical interaction of concrete with fiber reinforced polymer

This paper presents a computational rational model to predict the ultimate and optimized load capacity of reinforced concrete (RC) beams strengthened by a combination of longitudinal and transverse fiber reinforced polymer (FRP) composite plates/sheets (flexure and shear strengthening system). Several experimental and analytical studies on the confinement effect and failure mechanisms of fiber reinforced polymer (FRP) wrapped columns have been conducted over recent years. Although typical axial members are large-scale square/ rectangular reinforced concrete (RC) columns in practice, the majority of such studies have concentrated on the behavior of small-scale circular concrete specimens. A high performance concrete, known as polymer concrete, made up of natural aggregates and an orthophthalic polyester binder, reinforced with non-metallic bars (glass reinforced polymer) has been studied. The material is described at micro and macro level, presenting the key physical and mechanical properties using different experimental techniques. Furthermore, a full description of non-metallic bars is presented to evaluate its structural expectancies, embedded in the polymer concrete matrix. In this paper, the mechanism of mechanical interaction of smooth and lugged FRP rods with concrete is presented. A general modeling and application of various elements are demonstrated. The contact parameters are defined and the procedures of calculation and evaluation of contact parameters are introduced. The method of calibration of the calculated parameters is presented. Finally, the numerical results are obtained for different bond parameters which show a good agreement with experimental results reported in literature

Characterization and classification of tropical weathered shale in Ayer Hitam, Johor for excavation purposes

Tropical weathered rocks exhibit a complex structure and difficult to be interpreted during earthwork excavation due to heterogeneity characteristics, degree of weathering, and structure formation. In fact, the weathering profile of problematic tropical sedimentary rocks of shale in Ayer Hitam, Johor is poorly understood and studied. This study aims to characterize and classify tropical weathered shale in Ayer Hitam, Johor based on qualitative and quantitative assessment for excavation purposes. An outcrop of weathered shale with inclined bedding of 30° to 50° located in Ayer Hitam, Johor was studied. The outcrop with 50 m length and 6 to 10 m high was divided into five sub-panels to map and classify the distribution of weathering grades based on the physical appearance and index properties. Point load test and jar slake index were conducted on various weathering grades. Result indicates the weathered shale profile in Ayer Hitam is an interbedded structure with various weathering grades of II, III, IV and V. The highest point load strength is 4.95 MPa recorded for grade II. The increase of weathering grade significantly decreased the point load strength up to 94.5% and shows reduction of jar slake value from index 2 to 6. The distribution grade of weathering of weathered shale profile in Ayer Hitam, Johor was successfully classified and mapped. The dominant weathering grades is highly and completely weathered (Grade IV and V), which can be used as a reference for excavation purposes

Intelligent techniques for prediction of drilling rate for percussive drills in topically weathered limestone

Physico-mechanical properties of rocks have a direct correlation with the drilling rate of percussive drill. The prediction of drilling rate is important for the deployment of drills during the planning stage. In tropical climatic regions, limestone is classified as blocky, very blocky, blocky/ seamy and disintegrated based on the degree of weathering. Weathering of limestone takes place very rapidly in tropical (wet) climatic regions. Previous researchers have correlated different individual rock mass properties with rate of drilling. However, single property of limestone is not adequate to correlate with the drilling rate. In this study, sensitivity analysis of different properties of weathered limestone was carried out with respect to drilling rate. Rock density, rock quality designation (RQD), geological strength index (GSI), point load index (PLI) and Schmidt hammer rebound number (SHRN) were identified as crucial input parameters. 113 data sets were collected with the foregoing five input parameters and the output parameter as drilling rate of percussive drills. Data was analysed with multi variable regression analysis (MVRA) which showed R2 value as 0.54. Artificial neural network (ANN) has been widely used for solving various engineering problems. On the other hand, optimization problems are solved by the Biogeography Based Optimization (BBO) model. Further this data was analysed with a hybrid intelligent model namely BBO- ANN. The R2 values for training data set and testing data set 0.638 and 0.761 respectively