Experimental Study on Hydrocarbon Liquids Migration in Double-Porosity Medium Using Digital Image Analysis

The development activity of the country has played a part in natural disasters and climate change such as earthquake, El-Nino, tsunamis and water pollution have caused negative impact on human health and geo-environment. More complicated problems arise when the subsurface has experienced earthquake vibration, which no doubt influences the migration of hydrocarbon liquid into the groundwater sources. These problems need to be addressed in ensuring sustainable groundwater utilization. This paper aims to study the characteristics of hydrocarbon liquid migration that are important for the remediation cleanup of contaminated groundwater. The danger of reproductive toxic hydrocarbon chemicals has made actual on-site study infeasible and has been more practically replaced by physical model simulations. For this purpose, a physical laboratory experimental study was conducted to investigate the pattern and characteristics of different quantity toluene hydrocarbon migration in double-porosity medium under the vibration effect by using digital image analysis. The results of the experiments show that lower quantity of toluene hydrocarbon will take longer time to migrate to the bottom compared to higher quantity of toluene hydrocarbon. During experiment, air bubbles were continuously observed at the soil surface of toluene reducing due to the wettability of the liquids in the soil sample and the air trapped between the fractured aggregate and intra-inter aggregate pores. This study indicate that the digital image analysis is capable to provide the hydrocarbon flow rate and useful information for researchers and professionals to comprehensively understand migration characteristic

Investigation of Aqueous and Non-Aqueous Phase Liquids Migration in Fractured Double-Porosity Soil

The issue of leakage from underground storage tank and spillage of contaminate liquids can contribute to the aqueous and non-aqueous phase liquids contamination into the groundwater, resulting in groundwater pollution and rendering the quality of groundwater unsafe for consumption. Ensuring availability and sustainable management of water and sanitation for all was the goal and target in the 2030 agenda for sustainable development, consisting of a plan of action for people, planet and prosperity of the United Nations. This paper is intended to investigate the aqueous and non-aqueous phase liquid migrations in the fractured double-porosity soil, which become important for sustainability of groundwater utilisation and a comprehensive understanding of the pattern and behaviour of liquid migration into the groundwater. For this aim, an experiment model was conducted to study the pattern and behaviour of aqueous and non-aqueous phase liquid migration in fractured double-porosity soil using digital image processing technique. Outcome of the experiments show that the fractured double-porosity soil has faster liquid migration at the cracked soil surface condition compared to intact soil surface. It can concluded that the factors that significantly influence the aqueous and non-aqueous phase liquids migration was the soil sample structure, soil sample fractured pattern, physical interaction bonding between the liquid and soil, and the fluid capillary pressure. This study demonstrates that the hue saturation intensity contour plot of liquids migration behaviour can provide detailed information to facilitate researchers and engineers to better understand and simulate the pattern of liquids migration characteristics that influence the groundwater resources

Study of Aqueous and Non-Aqueous Phase Liquid in Fractured Double-Porosity Soil Using Digital Image Processing

The leakage and spillage of non-aqueous phase liquids (NAPLs) and aqueous phase liquids (APLs) contribute to groundwater contamination, resulting in groundwater pollution and rendering the quality of groundwater unsafe for drinking and agriculture. Ensuring the availability and sustainable management of water and sanitation for all was the goal and target of the 2030 United Nations agenda for sustainable development, consisting of a plan of action for the population, the planet and general prosperity. This paper is intended to investigate the aqueous and non-aqueous phase liquid migrations in a deformable double-porosity soil, which has become important for both sustainable groundwater use and the comprehensive understanding of the behaviour of liquid migration into groundwater. A modelling experiment was conducted in an attempt to study the pattern and behaviour of aqueous and non-aqueous phase liquid migration in fractured double-porosity soil using a digital image processing technique. The results of the experiments show that the flow of the APL and NAPL migration was not uniformly downward. Faster migration occurred where the soil surface was cracked compared to other locations where the soil surface was not cracked, even when liquids such as toluene were not used. It was concluded that the factors that significantly influenced the APL and NAPL migration were the structure of the soil sample, fracture pattern of the soil sample, physical interaction i.e. bonding between the liquid and soil sample, and the capillary pressure of the fluid. This study indicates that digital image analysis can provide detailed information to help researchers better understand and be able to simulate the pattern and characteristics of liquid migration that have an influence on groundwater resources

Effect of applied load on the stability of unreinforced and reinforced slopes

Recent development in hilly area has highlighted issues related to instability in engineered as well as natural slopes, hence; it is a challenge to both professionals and researches in Malaysia to address the problem related to landslides and slope failures. Several occurrences during the past few years include Highland Tower collapse in December 1993, Bungalow collapse at Taman Hillview in November 2002, the massive landslide occurred at Bukit Antarabangsa in December 2008, and the recent landslide at Hulu Langat in May 2011. Therefore, uncontrolled development which place structural foundation too near to the slope crest on hilly area may lead to slope failure. Thus, it is essential to consider the safe distance between the foundation and the slope crest. The aim of this study is to determine the safe distance of foundation on slope crest using a commercial software Slope/W (Geostudio, 2007). Morgenstern-Price and bishop method are selected for slope stability analysis due to the flexibility in selecting the critical slip surface. A case study at PT 4697, Seksyen7, Shah Alam, Selangor was selected for the analysis. Applied loading of 10kPa, 15kPa, 20kPa, 30kPa and 50kPa will be placed at various distances from the crest of the slope. The result from the slope stability analysis had indicated that the construction of reinforced earth wall improved the stability of slope but the loading on slope crest decreases the factor of safety of both unreinforced slope and reinforced slope. Therefore the load within unstable area will results in the depressions at the point of applied load, however once the load is moved beyond the unstable area, its effect become significantly minimized and the pattern of safety factor is constant start from a stable distance where the increasing of loading will remain constant for the factor of safety

Teaching–Learning–Based Optimization (TLBO) in Hybridized with Fuzzy Inference System Estimating Heating Loads

Nowadays, since large amounts of energy are consumed for a variety of applications, more and more emphasis is placed on the conservation of energy. Recent investigations have experienced the significant advantages of using metaheuristic algorithms. Given the importance of the thermal loads’ analysis in energy-efficiency buildings, a new optimizer method, i.e., the teaching–learning based optimization (TLBO) approach, has been developed and compared with alternative techniques in the present paper to predict the heating loads (HLs). This model is applied to the adaptive neuro–fuzzy interface system (ANFIS) in order to overcome its computational deficiencies. A literature-based dataset acquired for residential buildings is used to feed these models. According to the results, all the applied models can appropriately predict and analyze the heating load pattern. Based on the value of R2 calculated for both testing and training (0.98933, 0.98931), teaching–learning-based optimization can help the adaptive neuro–fuzzy interface system to enhance the results’ correlation. Also, the high R2 value means that the model has high accuracy in the HL prediction. In addition, according to the estimated RMSE, the training error of TLBO–ANFIS in the testing and training stages was 0.07794 and 0.07984, respectively. The low value of root–mean–square error (RMSE) indicates that the TLBO–ANFIS method acts favorably in the estimation of the heating load for residential buildings

Slope Stability Monitoring Using Novel Remote Sensing Based Fuzzy Logic

By the assist of remotely sensed data, this study examines the viability of slope stability monitoring using two novel conventional models. The proposed models are considered to be the combination of neuro-fuzzy (NF) system along with invasive weed optimization (IWO) and elephant herding optimization (EHO) evolutionary techniques. Considering the conditioning factors of land use, lithology, soil type, rainfall, distance to the road, distance to the river, slope degree, elevation, slope aspect, profile curvature, plan curvature, stream power index (SPI), and topographic wetness index (TWI), it is aimed to achieve a reliable approximation of landslide occurrence likelihood for unseen environmental conditions. To this end, after training the proposed EHO-NF and IWO-NF ensembles using training landslide events, their generalization power is evaluated by receiving operating characteristic curves. The results demonstrated around 75% accuracy of prediction for both models; however, the IWO-NF achieved a better understanding of landslide distribution pattern. Due to the successful performance of the implemented models, they could be promising alternatives to mathematical and analytical approaches being used for discerning the relationship between the slope failure and environmental parameters

Slope Stability Monitoring Using Novel Remote Sensing Based Fuzzy Logic

By the assist of remotely sensed data, this study examines the viability of slope stability monitoring using two novel conventional models. The proposed models are considered to be the combination of neuro-fuzzy (NF) system along with invasive weed optimization (IWO) and elephant herding optimization (EHO) evolutionary techniques. Considering the conditioning factors of land use, lithology, soil type, rainfall, distance to the road, distance to the river, slope degree, elevation, slope aspect, profile curvature, plan curvature, stream power index (SPI), and topographic wetness index (TWI), it is aimed to achieve a reliable approximation of landslide occurrence likelihood for unseen environmental conditions. To this end, after training the proposed EHO-NF and IWO-NF ensembles using training landslide events, their generalization power is evaluated by receiving operating characteristic curves. The results demonstrated around 75% accuracy of prediction for both models; however, the IWO-NF achieved a better understanding of landslide distribution pattern. Due to the successful performance of the implemented models, they could be promising alternatives to mathematical and analytical approaches being used for discerning the relationship between the slope failure and environmental parameter

Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles

Regular optimization techniques have been widely used in landslide-related problems. This paper outlines two novel optimizations of artificial neural network (ANN) using grey wolf optimization (GWO) and biogeography-based optimization (BBO) metaheuristic algorithms in the Ardabil province, Iran. To this end, these algorithms are synthesized with a multi-layer perceptron (MLP) neural network for optimizing its computational parameters. The used spatial database consists of fourteen landslide conditioning factors, namely elevation, slope aspect, land use, plan curvature, profile curvature, soil type, distance to river, distance to road, distance to fault, rainfall, slope degree, stream power index (SPI), topographic wetness index (TWI) and lithology. 70% of the identified landslides are randomly selected to train the proposed models and the remaining 30% is used to evaluate the accuracy of them. Also, the frequency ratio theory is used to analyze the spatial interaction between the landslide and conditioning factors. Obtained values of area under the receiver operating characteristic curve, as well as mean square error and mean absolute error showed that both GWO and BBO hybrid algorithms could efficiently improve the learning capability of the MLP. Besides, the BBO-based ensemble surpasses other implemented models

Machine-Learning-Based Classification Approaches toward Recognizing Slope Stability Failure

In this paper, the authors investigated the applicability of combining machine-learning-based models toward slope stability assessment. To do this, several well-known machine-learning-based methods, namely multiple linear regression (MLR), multi-layer perceptron (MLP), radial basis function regression (RBFR), improved support vector machine using sequential minimal optimization algorithm (SMO-SVM), lazy k-nearest neighbor (IBK), random forest (RF), and random tree (RT), were selected to evaluate the stability of a slope through estimating the factor of safety (FOS). In the following, a comparative classification was carried out based on the five stability categories. Based on the respective values of total scores (the summation of scores obtained for the training and testing stages) of 15, 35, 48, 15, 50, 60, and 57, acquired for MLR, MLP, RBFR, SMO-SVM, IBK, RF, and RT, respectively, it was concluded that RF outperformed other intelligent models. The results of statistical indexes also prove the excellent prediction from the optimized structure of the ANN and RF techniques

Spatial Landslide Susceptibility Assessment Based on Novel Neural-Metaheuristic Geographic Information System Based Ensembles

Regular optimization techniques have been widely used in landslide-related problems. This paper outlines two novel optimizations of artificial neural network (ANN) using grey wolf optimization (GWO) and biogeography-based optimization (BBO) metaheuristic algorithms in the Ardabil province, Iran. To this end, these algorithms are synthesized with a multi-layer perceptron (MLP) neural network for optimizing its computational parameters. The used spatial database consists of fourteen landslide conditioning factors, namely elevation, slope aspect, land use, plan curvature, profile curvature, soil type, distance to river, distance to road, distance to fault, rainfall, slope degree, stream power index (SPI), topographic wetness index (TWI) and lithology. 70% of the identified landslides are randomly selected to train the proposed models and the remaining 30% is used to evaluate the accuracy of them. Also, the frequency ratio theory is used to analyze the spatial interaction between the landslide and conditioning factors. Obtained values of area under the receiver operating characteristic curve, as well as mean square error and mean absolute error showed that both GWO and BBO hybrid algorithms could efficiently improve the learning capability of the MLP. Besides, the BBO-based ensemble surpasses other implemented models