Effects of microfissures on the engineering properties of the marble at Wadi Lisb,Saudi Arabia

The marble at Wadi Lisb was investigated in detail in order to assess its engineering properties and to examine the effect of the microfissures on these properties. The marble is white with some dark bands containing the microfissures and parallel to the regional foliation direction. The samples that are cut parallel to these dark bands have better physical and mechanical properties than those cut perpendicular to them. The general effect of the microfissures on Wadi Lisb marble is similar to their effect on Al Madrakah marble indicating a regional phenomenon. During the quarrying operation, it is recommended to take the direction of the microfissures in consideration and to correct for any deviation during the slapping process.     

A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China

Bulletin of Engineering Geology and the Environment - The main objective of the current study is to apply a random forest (RF) data-driven model and prioritization of landslide conditioning factors...     

Thermal performance of a naturally ventilated building using a combined algorithm of probabilistic occupant behaviour and deterministic heat and mass balance models

This study explores the role of occupant behaviour in relation to natural ventilation and its effects on summer thermal performance of naturally ventilated buildings. We develop a behavioural algorithm (the Yun algorithm) representing probabilistic occupant behaviour and implement this within a dynamic energy simulation tool. A core of this algorithm is the use of Markov chain and Monte Carlo methods in order to integrate probabilistic window use models into dynamic energy simulation procedures. The comparison between predicted and monitored window use patterns shows good agreement. Performance of the Yun algorithm is demonstrated for active, medium and passive window users and a range of office constructions. Results indicate, for example, that in some cases, the temperature of an office occupied by the active window user in summer is up to 2.6 °C lower than that for the passive window user. A comparison is made with results from an alternative behavioural algorithm developed by Humphreys [H.B. Rijal, P. Tuohy, M.A. Humphreys, J.F. Nicol, A. Samuel, J. Clarke, Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings, Energy and Buildings 39 (7) (2007) 823-836.]. In general, the two algorithms lead to similar predictions, but the results suggest that the Yun algorithm better reflects the observed time of day effects on window use (i.e. the increased probability of action on arrival).     

Using multivariate factor analysis to assess surface/logged water quality and source of contamination at a large irrigation project at Al-Fadhli,Eastern Province,Saudi Arabia

A multivariate statistical technique, factor analysis, has been used to assess the impact of irrigation on near-surface water at a farm at Fadhli, Eastern Province, Saudi Arabia. A total of 34 surface water samples were analyzed for 23 different water quality parameters including pH, TDS, conductivity and alkalinity, sulphate, chloride, bicarbonate, nitrate, phosphate, bromide, fluoride, calcium, magnesium, sodium, potassium, arsenic, boron, copper, cobalt, iron, lithium, manganese, molybdenum, nickel, selenium, mercury, and zinc. The water quality data were subjected to both Q-mode and R-mode factor analysis. R-mode analysis resulted in three major factors for both summer and winter sampling events. Factor 1 (representing 48.91% of the summer and 53.66% of the winter variance) comprised elements likely to be due to the application of fertilizers and the dissolution of soil material. Factor 2 (14.04% of the summer and 11.94% of the winter variance) included elements common in irrigation water (related to the dissolution of aquifer materials). Factor 3 (7.36% of the summer and 10.18% of the winter variance) included molybdenum, manganese and nickel, which are common in micro-nutrients. Q-mode analysis showed that the most affected areas are located at the northwest corner of the farm.      

Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression,naive Bayes and RBFNetwork models for the Long County area (China)

The main goal of this study is to assess and compare three advanced machine learning techniques, namely, kernel logistic regression (KLR), naïve Bayes (NB), and radial basis function network (RBFNetwork) models for landslide susceptibility modeling in Long County, China. First, a total of 171 landslide locations were identified within the study area using historical reports, aerial photographs, and extensive field surveys. All the landslides were randomly separated into two parts with a ratio of 70/30 for training and validation purposes. Second, 12 landslide conditioning factors were prepared for landslide susceptibility modeling, including slope aspect, slope angle, plan curvature, profile curvature, elevation, distance to faults, distance to rivers, distance to roads, lithology, NDVI (normalized difference vegetation index), land use, and rainfall. Third, the correlations between the conditioning factors and the occurrence of landslides were analyzed using normalized frequency ratios. A multicollinearity analysis of the landslide conditioning factors was carried out using tolerances and variance inflation factor (VIF) methods. Feature selection was performed using the chi-squared statistic with a 10-fold cross-validation technique to assess the predictive capabilities of the landslide conditioning factors. Then, the landslide conditioning factors with null predictive ability were excluded in order to optimize the landslide models. Finally, the trained KLR, NB, and RBFNetwork models were used to construct landslide susceptibility maps. The receiver operating characteristics (ROC) curve, the area under the curve (AUC), and several statistical measures, such as accuracy (ACC), F-measure, mean absolute error (MAE), and root mean squared error (RMSE), were used for the assessment, validation, and comparison of the resulting models in order to choose the best model in this study. The validation results show that all three models exhibit reasonably good performance, and the KLR model exhibits the most stable and best performance. The KLR model, which has a success rate of 0.847 and a prediction rate of 0.749, is a promising technique for landslide susceptibility mapping. Given the outcomes of the study, all three models could be used efficiently for landslide susceptibility analysis.