Probability and statistics in Civil Engineering: by G.N. Smith, Nichols Publishing Company, New York, NY, 1986, 244 pp.

Bond strength prediction of the composite rebars in concrete using innovative bio‐inspired models

A Machine Learning Technique for Spatial Interpolation of Solar Radiation Observations

Particulate matter (PM2.5) concentrations are a serious human health concern and global models are the common methods for PM2.5 particle estimation disregarding the local changes and factors. In th...

Predicting PM2.5 levels over the north of Iraq using regression analysis and geographical information system (GIS) techniques

Wash-waters and wastewaters from the fruit and vegetable processing industry are characterized in terms of solids and organic content that requires treatment to meet regulatory standards for purpose-of-use. In the following, the efficacy of 13 different water remediation methods (coagulation, filtration, bioreactors, and ultraviolet-based methods) to treat fourteen types of wastewater derived from fruit and vegetable processing (fruit, root vegetables, leafy greens) were examined. Each treatment was assessed in terms of reducing suspended solids, total phosphorus, nitrogen, biochemical and chemical oxygen demand. From the data generated, it was possible to develop predictive modeling for each of the water treatments tested. Models to predict post-treatment water quality were studied and developed using multiple linear regression (coefficient of determination (R2) of 30 to 83%), which were improved by the generalized structure of group method of data handling models (R2 of 73–99%). The selection of multiple linear regression and the generalized structure of group method of data handling models was due to the ability of the models to produce robust equations for ease of use and practicality. The large variability and complex nature of wastewater quality parameters were challenging to represent in linear models; however, they were better suited for group method of data handling technique as shown in the study. The model provides an important tool to end users in selecting the appropriate treatment based on the original wastewater characteristics and required standards for the treated water.

Machine Learning Models for Predicting Water Quality of Treated Fruit and Vegetable Wastewater

Constrained soil investigation works in tunnel construction projects brings inevitable uncertainties in capturing the exact subsurface profile. This uncertainty leads to misinterpretation of tunnel analysis, which can later cause unreliable prediction of settlements trough in tunnel analysis. The initial stage of interpretation of the subsurface soil profile is generating reliable top-surface grid profile. Increasing the number of boreholes can provide more information for modelling the grid profile. The profile is conventionally developed using borehole information and survey plan, which are interpreted manually and sometimes can be erroneous. This situation proves that the lack of method for data interpretation should be supported with another approach such as Inverse Distance Weighting (IDW). IDW is categorized as a type of deterministic method for multivariate interpolation with a known scattered set of points. The assign values for unknown points will be determined by a weighted average of the values available at the known points. IDW interpolation influenced by weight decreases as the distance increases from the interpolated point. This study focused on the development of top-surface grid for Klang Valley Mass Rapid Transit (KVMRT) project using data from borehole investigation and survey plan along the tunnel alignment. Reduced level of boreholes and information from survey plans that were completed for this project were analyzed. Based on the study results, the Model 2 assigned with exponent 5 for IDW analysis is more preferable for subsurface ground modelling in tunnel analysis for this particular project.

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Interpretation and development of top-surface grid in subsurface ground profile using Inverse Distance Weighting (IDW) method for twin tunnel project in Kenny Hill Formation

Soil investigation (SI) work is a process of collecting subsurface ground profile information in evaluating soil engineering properties for a construction project. The standard penetration method (SPT) is widely accepted worldwide as a feasible and relatively inexpensive method over other field tests. Pressuremeter test (PMT), on the other hand, is costly and more popular in situ method in interpreting soil deformation behavior. Considering SPT is available in almost site investigation works for all sizes of project, it was tempting to establish the correlation between SPT and PMT results, specifically in local geologic setting. The study was conducted in Kenny Hill Formation, Kuala Lumpur, Malaysia. The correlation established in this study is between pressuremeter modulus (EM) and SPT blow count (N60). In addition to in situ methods, the physical properties of soil extruded from boreholes were tested in geotechnical laboratories to determine soil properties, such as particle size distribution, liquid limit, and plastic limit. These parameters need to be considered during prediction of EM. Group method of data handling (GMDH) neural network has been used to achieve this objective. The mean absolute error (MAE) results show that the GMDH neural networks produce values of 8.04 and 8.59 for training and testing. The root mean square error (RMSE) yields 10.61 and 10.84 for training and testing, respectively. Further, the results of the coefficients of determination (R2) are 0.794 and 0.726 for training and testing demonstrates a good correlation exists between predicted and measured values. Based on the GMDH results, N60, sand, and clay are required input variables for determination of EM.

Prediction of pressuremeter modulus ( E M ) using GMDH neural network: a case study of Kenny Hill Formation

Volume loss during tunneling excavation leads to ground deformation, which can damage adjacent surfaces or subsurface structures. Thus, tunnel design with proper estimation of ground deformation and realistic geotechnical simulation is essential for large-scale urban underground construction. In this study, subsurface characterization of the tunnel excavation section in Kenny Hill Formation (KHF) was conducted to develop 3D ground model and tunnel-filtered models and obtain ground sections through the spatial interpolation of borehole data using the Inverse Distance Weighting (IDW) method. Six greenfield ground sections were selected by using the tunnel-filtered model’s configuration of tunnels and available tunneling-induced ground movement data. Conceptual models for finite element modeling were developed based on soil profiles, and the corresponding soil parameters were determined from ground sections. The strength and stiffness parameters of the Hardening Soil (HS) model were established using data from site investigation, in situ and laboratory tests, and empirical correlations with standard penetration test. The effectiveness of empirical correlations was determined through back analysis of twin-tunnel excavation in 2D finite element analysis using the contraction method and verified with monitored ground movement data. The numerical back-analyzed results of twin-tunnel excavation simulation using HS parameters obtained from a selected empirical correlation showed good agreement with construction-monitored ground movements. The application range of the values of contraction ratio was from 0.3 to 0.95%.

Assessment of the Twin-Tunnel Interaction Mechanism in Kenny Hill Formation Using Contraction Ratio Method

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Calibration of stiffness parameters for Hardening Soil Model in residual soil from Kenny Hill Formation

In urban area tunnel construction growing rapidly due to urbanization and increase in population rate. Tunnels are excavated at different depths from ground surface, but this will cause ground settlement in the excavation area which impact the surrounding structures. This paper is focused on effects of the surface ground movement prediction using numerical approach at various depth with Mohr Coulomb (MC) model and Hardening Soil (HS) model. Kenny Hill Formation used as the study area particularly chainage NB 1960. In this paper, the shape and pattern of the ground movement that obtained from simulation in PLAXIS 2D using MC model was compared with HS model output. Various tunnel depth location was used in the analysis such as real site condition tunnel depth and the relationship of 1d, 2d, 3d and 4d where d is the diameter of the tunnel. From this study, it can be seen that when tunnel depth increases the surface settlement decreases for both MC and HS model. But, the MC model’s ground surface settlements were undoubtedly lower than HS model.

Darvintharen Govindasamy

Papers

Interpretation and development of top-surface grid in subsurface ground profile using Inverse Distance Weighting (IDW) method for twin tunnel project in Kenny Hill Formation

Prediction of pressuremeter modulus ( E M ) using GMDH neural network: a case study of Kenny Hill Formation

Assessment of the Twin-Tunnel Interaction Mechanism in Kenny Hill Formation Using Contraction Ratio Method

Calibration of stiffness parameters for Hardening Soil Model in residual soil from Kenny Hill Formation

Influence of Twin Tunnel Depth in Numerical Ground Movement Prediction Using Mohr Coulomb and Hardening Soil Model