Zamri Chik

Disaster in Bangladesh and management with advanced information system

Purpose – This paper aims to document a case study of a disaster in Bangladesh and the role of an information management system for disaster management planning.Design/methodology/approach – The paper uses a methodology that considers perceptions or constructions – including the role of information systems – to be dependent on the social and cultural structures, which is helpful in reducing destruction in disaster‐prone areas.Findings – Advances in information technology in the form of the internet, geographic information systems (GIS), remote sensing, satellite communication, etc. are beneficial in many aspects of the planning and implementation of hazard reduction arrangements.Research limitations/implications – Natural disasters strike countries, both developed and developing, cause enormous destruction and create human suffering, and have negative impacts on national economies. Bangladesh suffers regularly and frequently from disasters like floods, cyclone storms, tidal surges, river bank erosion and ...

Tenfold cross validation artificial neural network modeling of the settlement behavior of a stone column under a highway embankment

Construction of embankments in engineering structures on soft clay soils normally encounters problems related to excessive settlement issues. The conventional methods are inadequate to analyze and predict the surface settlement when the necessary parameters are difficult to determine in the field and in the laboratory. In this study, artificial neural network systems (ANNs) were used to predict settlement under embankment load using soft soil properties together with various geometric parameters as input for each stone column (SC) arrangement and embankment condition. Data from a highway project called Lebuhraya Pantai Timur2 in Terengganu, Malaysia, were investigated. The FEM package of Plaxis v8 program analysis was utilized. The actual angle of internal friction, spacing between SC, diameter of SC, length of SC, and height of embankment were used as the input parameters, and the settlement was used as the main output. Non cross validation (NCV) and tenfold cross validation (TFCV) were used to build the ANN model. The results of the TFCV model were more accurate than those of the NCV model. Comparisons made with the predictions of the Priebe model showed that the proposed TFCV model could provide better predictions than conventional methods.

Near surface soil characterizations through soil apparent resistivity: A case study

Soil electric resistivity is used to obtain near surface soil properties as non destructive testing at field investigations. Testing of soil electric resistivity has been applied in various contexts like: detection of soil type and rock, detection of anomalous materials of soil, groundwater exploration, landfill and solute transfer delineation. Researchers also incorporate 2-D tomography technique with apparent resistivity profile as Electrical Resistivity Tomography (ERT) method. Existing soil resistivity model shows limitations due to empirical relationship to get true resistivity profile. So, researchers concentrate on optimization criteria for nonlinear apparent resistivity data to obtain more reliable resistivity profile. Optimizations of soil apparent resistivity profile are also performed based on multi-layer soil parameters in recent research work which also includes the empirical relationships of apparent resistivity data. Moreover, electrical resistivity of soil is changed due to soil physical properties like soil structure, water content and chemical properties which can change the results during soil characterizations through electric resistivity. In this case history, we expose the background study of different surveying criteria of apparent resistivity data. The advantages and limitations of this apparent resistivity method are shown in this case history.

Model with artificial neural network to predict the relationship between the soil resistivity and dry density of compacted soil

This paper presents a technique to obtain the outcomes of soil dry density and optimum moisture contents with artificial neural network ANN for compacted soil monitoring through soil resistivity measurement in geotechnical engineering. The compacted soil monitoring through soil electrical resistivity shows the important role in the construction of highway embankments, earth dams and many other engineering structure. Generally, soil compaction is estimated through the determination of maximum dry density at optimum moisture contents in laboratory test. To estimate the soil compaction in conventional soil monitoring technique is time consuming and costly for the laboratory testing with a lot of samples of compacted soil. In this work, an ANN model is developed for predicting the relationship between dry density of compacted soil and soil electrical resistivity based on experimental data in soil profile. The regression analysis between the output and target values shows that the R2 values are 0.99 and 0.93 for the training and testing sets respectively for the implementation of ANN in soil profile. The significance of our research is to obtain an intelligent model for getting faster, cost-effective and consistent outcomes in soil compaction monitoring through electrical resistivity for a wide range of applications in geotechnical investigation.

Simple equation guide for multi-layer earth structure with soil electrical properties: Multi-layer soil electrical profile

This paper presents the useful equations for the estimation of apparent resistivity with depth and layer thickness in a multi-layer earth structure for geotechnical investigations. In recent advances of soil characterization with soil resistivity measurements, a two-layer soil model is implemented for obtaining near surface soil profile. Although geo-electric techniques offer an improvement to traditional soil sampling methods, the resulting data are still often misinterpreted for two-layer soil model, especially in terms of the interrelationships between soil apparent electrical resistivity and several soil physical or chemical properties. In this study, the soil electrical properties are measured along the depth for geotechnical characterizations. Moreover, the theoretical relationships of the electric field density and voltage difference with the thickness of soil layer are revealed in this study. The equations and relationship for multi-layer soil resistivity profile are validated with the numerical formulations and fundamental electromagnetic equations of electrical and electronics engineering. The crucial equations based on the layered earth model can significantly reduce the complexity of estimation of depth and thickness corresponding soil resistivity profile. The nobility of the research is obtaining the simple equations and providing the easy means of soil resistivity measurements with depth and layer thickness in multi-layer soil characterizations.

Prediction of External Stability for Geogrid-Reinforced Segmental Walls

Prediction of external stability for segmental retaining walls reinforced with geogrid and backfilled with residual soil was carried out using statistical methods and artificial neural networks (ANN). Prediction was based on data obtained from 234 segmental retaining wall designs using procedures developed by the National Concrete Masonry Association (NCMA). The study showed that prediction made using ANN was generally more accurate to the target compared with statistical methods using mathematical models of linear, pure quadratic, full quadratic and interactions.

Preparation of residual soil samples by using modified method

The purpose of this study was to establish an alternative method in preparing the residual soil samples for laboratory tests. The soil was compacted by using the modified method in order to get the desired values of dry unit weight that was equivalent to the values obtained from standard compaction method. The advantage of using this method was that, due to its larger size, more samples could be taken and tested under the same compaction condition and the mould could be directly mounted on the shaking table with the addition of the air bags to avoid the occurrence of boundary effects. The soil was compacted in a 300mm x 300mm x 300mm mould by using the vibrating hammer. The results of this study showed that the average dry unit weight value of 13.4 kN/m3, 13.6 kN/m3 and 13.7 kN/m3 obtained from 5 rounds/layer, 6 rounds/layer and 7 rounds/layer of compaction of modified method, was equivalent to about 92%, 93 % and 94% of maximum dry unit weight obtained from standard compaction method, respectively.

Prediction of Internal Stability for Geogrid-Reinforced Segmental Walls

Prediction of internal stability for segmental retaining walls reinforced with geogrid and backfilled with residual soil was carried out using statistical methods and artificial neural networks (ANN). Prediction was based on data obtained from 234 segmental retaining wall designs using procedures developed by the National Concrete Masonry Association (NCMA). The study showed that prediction made using ANN was generally more accurate to the target compared with statistical methods using mathematical models of linear, pure quadratic, full quadratic and interactions.

Modelling and Assessment of a Single Pile Subjected to Lateral Load

Abstract A three-dimensional finite element technique was used to analyse single pile lateral response subjected to pure lateral load. The main objective of this study is to assess the influence of the pile slenderness ratio on the lateral behaviour of single pile. The lateral single pile response in this assessment considered both lateral pile displacement and lateral soil resistance. As a result, modified p-y curves for lateral single pile response were improved when taking into account the influence lateral load magnitudes, pile cross sectional shape and flexural rigidity of the pile. The finite element method includes linear elastic, Mohr-Coulomb and 16-nodes interface models to represent the pile behaviour, soil performance and interface element, respectively. It can be concluded that the lateral pile deformation and lateral soil resistance because of the lateral load are always influenced by lateral load intensity and soil type as well as a pile slenderness ratio (L/D). The pile under an intermediate and large amount of loading (in case of cohesionless soil) has more resistance (low lateral displacement) than the pile embedded on the cohesion soil. In addition, it can be observed that the square-shaped pile is able to resist the load by about 30% more than the circular pile. On the other hand, pile in cohesionless soil was less affected by the change in EI compared with that in cohesive soil.

Performance of Multi-Layer Soil Electric Resistivity Model Comparing with Two-Layer Characterizations in Geotechnical Investigations

This paper shows the performance of multi-layer soil electric resistivity model comparing with two-layer characterizations in geotechnical investigations. In conventional model, there are inter-relationships between soil apparent electrical resistivity (ρ) and several soil physical or chemical properties. These empirical relationships show limitations to obtain specific soil characterizations of different layers with. Multi-layer true resistivity model is the improvement of conventional two-layer earth model including the criteria of four points probe method. Multi-layer soil resistivity profile shows more accuracy to obtain near surface soil characteristics including the types of soil and rocks, and to detect anomalous materials in soil profile. Alternatively, apparent resistivity in two-layer model can be used to obtain deeper profile of soil characteristics. In this multi-layer soil model, the soil resistivity and resistivity ratio corresponding to the depth in soil medium are considered for geotechnical investigations. Two-layer model includes soil apparent resistivity according to probe distances in depth corresponding resistivity profile. This paper is important for including criteria and performance of multi-layer soil resistivity model and conventional two-layer model for geotechnical characterizations.