Optimization of Groundwater Level Monitoring Network Using GIS-based Geostatistical Method and Multi-parameter Analysis:A Case Study in Wainganga Sub-basin,India

Groundwater is one of the most important resources, its monitoring and optimized management has now become the priority to satisfy the demand of rapidly increasing population. In many developing countries, optimized groundwater level monitoring networks are rarely designed to build up a strong groundwater level data base, and to reduce operation time and cost. The paper presents application of geostatistical method to optimize existing network of observation wells for 18 sub-watersheds within the Wainganga Sub-basin located in the central part of India. The average groundwater level fluctuation(GWLF) from 37 observation wells is compared with parameters like lineament density, recharge, density of irrigation wells, land use and hydrogeology(LiRDLH) of Wainganga Sub-basin and analyzed stochastically in Geographic Information System(GIS) environment using simple, ordinary, disjunctive and universal kriging methods. Semivariogram analyses have been performed separately for all kriging methods to fit the best theoretical model with experimental model. Results from gaussian, spherical, exponential and circular theoretical models were compared with those of experimental models obtained from the groundwater level data. Spatial analyses conclude that the exponential semivariogram model obtained from ordinary kriging gives the best fit model. Study demonstrates that ordinary kriging gives the optimal solution and additional number of observation wells can be added utilizing the error variance for optimal design of groundwater level monitoring networks. This study describes the use of Geostatistics methods in GIS to predict the groundwater level and upgrade groundwater level monitoring networks from the randomly distributed observation wells considering multiple parameters such as GWLF and LiRDLH. The method proposed in the present study is observed to be an efficient method for selecting observation well locations in a complex geological set up. The study concludes that minimum 82 wells are required for proper monitoring of groundwater level in the study area.

Line-integral representations for extended displacements, stresses,and interaction energy of arbitrary dislocation loops in transversely isotropic magneto-electro-elastic bimaterials

In addition to the hexagonal crystals of class 6 mm, many piezoelectric materials （e.g., BaTiO3）, piezomagnetic materials （e.g., CoFe2O4）, and multiferroic com-posite materials （e.g., BaTiO3-CoFe2O4 composites） also exhibit symmetry of transverse isotropy after poling, with the isotropic plane perpendicular to the poling direction. In this paper, simple and elegant line-integral expressions are derived for extended displace-ments, extended stresses, self-energy, and interaction energy of arbitrarily shaped, three-dimensional （3D） dislocation loops with a constant extended Burgers vector in trans-versely isotropic magneto-electro-elastic （MEE） bimaterials （i.e., joined half-spaces）. The derived solutions can also be simply reduced to those expressions for piezoelectric, piezo-magnetic, or purely elastic materials. Several numerical examples are given to show both the multi-field coupling effect and the interface/surface effect in transversely isotropic MEE materials.

Statistical Analysis of the Asphalt Mixtures Volumetric Properties

Based on statistics principle,random error and systematic error were considered and the volumetric properties of the two mixtures types,namely A and B,were statistically analyzed using different distribution methods.Seventy-two samples of mixture A and fifty-two of mixture B were fabricated using the Marshall method.The probability distributions were compared on the basis of goodness of fit.Weibull model was found to be most appropriate model for describing the asphalt mixtures volumetric properties distribution.The two-parameter Weibull distribution function applied well to model the bulk specific gravity and voids filled with asphalt data,whereas,the three-parameter Weibull distribution appeared to be more appropriate in the discussing of air voids and voids in mineral aggregate.The experimetal results is revealed that compared with the mean value,the peak value of Weibull distribution was suggested as an alternative and more powerful parameter for describing the test data distribution characteristic.The analysis of test results also revealed that there were significant differences in the volumetric properties of the two tested mixtures for the same confidence level.The confidence interval decreased with the decreasing in reliability.

Repair and Restoration of the Historical Wellesley Bridge at Sriran­gapatna:A Case Study

The Historical Wellesley Bridge,built by the Krishnaraja Wadiyar under the supervision of Dewan Purnaih across river Cauvery at Srirangapatna.Situation of bridge is got when heavy rainfall followed by heavy inflow from Cauvery Catchment area in Kodagu District.At present,the Government of Karnataka has taken measures to do the restoration works using the same previously used materials with slight changes.Hence,in the present investigation the authors are doing a case study on the above structure by testing the ingredients of the materials used for it and also by conducting Non-Destructive Test on the structure to know its strength before and after restoration.Based on the test results obtained,the authors will give a conclusion with respect to durability aspects.In addition to the above,the authors will test for few alternative materials i.e.,lime mortar with Cement(i.e.MM2 Grade Masonary mortar).Finally,from the obtained test results here the authors can suggest suitable material for the structures.

Strengthening reinforced concrete beams using prestressed glass fiber-reinforced polymer-Part II:Analytical study

Strengthening reinforced concrete (R. C.) beams using prestressed glass fiber-reinforced polymer (PGFRP) was studied experimentally as described in Part I of this paper (Huang et al., 2005). In that paper, R. C. beams, R. C. beams with GFRP (glass fiber-reinforced polymer) sheets, and R. C. beams with PGFRP sheets were tested in both under-strengthened and over-strengthened cases. The test results showed that the load-carrying capacities (ultimate loads) of the beams with GFRP sheets were greater than those of the beams without polymer sheets. The load-carrying capacities of beams with PGFRP sheets were greater than those of beams with GFRP sheets. The objective of this work is to develop an analytical method to compute all of these load-carrying capacities. This analytical method is independent of the experiments and based only on the traditional R. C. and P. C. (prestressed concrete) theory. The analytical results accorded with the test results. It is suggested that this analytical method be used for analyzing and designing R. C. beams strengthened using GFRP or PGFRP sheets.

Data Exploration and Reconnaissance to Identify Ocean Phenomena: A Guide for In Situ Data Collection

Marine pollution is a serious geoenvironmental problem affecting the Lebanese coast. It mainly affects the coastal zone adjacent to areas of dense population. To detect the sources of pollution along this zone, as well as to identify their characteristics, remote sensing data is used. Landsat 8 Operational Land Imager (OLI) satellite images, which have medium spatial resolution, are analyzed using ENVI 5.2 and ArcGIS 10.3.1 geospatial software for the years of 2014 and 2015. Different routines are applied to reveal anomalous features with the goal being to discriminate polluted water in the marine environment. Results showed anomalies in Akkar region. This might be due to the presence of basalts rocks, and geothermal heating, or the pollution of Oustowan river that flows into the sea. The results also showed that during the dry season, there is low movement of water causing a least extension of the anomalies. In contrary, during the wet season, rivers had an intense flow into the sea which caused an intense water movement and wide extension of anomalies on the coast. Permanently polluted coastal sites are evident in Tripoli, Kalamoun, Chekka, Batroun, Amchit, Jbeil, Jounieh, Nahr Beirut and Ouzai with the most presumed polluted months being in 2014 during April and November and in 2015 in April. The least extended pollution is during July 2014 and 2015. The length and width of each anomaly at each site shows that during the year of 2015;most of the anomalies are larger than in 2014.

A Three-Dimensional Size-Dependent Layered Model for Simply-Supported and Functionally Graded Magnetoelectroelastic Plates

A three-dimensional size-dependent layered model for simply-supported and func- tionally graded magnetoelectroelastic plates is presented based on the modified couple-stress theory. The functionally graded material is assumed to be exponential in the thickness direc- tion of the plate. The final governing equations are reduced to an eigensystem by expressing the extended displacements in terms of two-dimensional Fourier series. Using the propagator matrix method, the exact solutions of the magnetic, electric and mechanical fields of sandwich nanoplates with couple-stress effect and under the surface loads are derived. Numerical examples for two functionally graded sandwich plates made of piezoelectric BaTiO3 and magnetostrictive CoFe2O4 materials are presented to demonstrate the effect of the functional gradient factor and material length-scale parameter on the induced fields. The exact solutions presented in this work can also serve as benchmarks to various numerical methods for analyzing the size-dependent features in layered systems.

Deep learning based on connected vehicles for icing pavement detection

Slippery road conditions,such as snowy,icy or slushy pavements,are one of the major threats to road safety in winter.The U.S.Department of Transportation(USDOT)spends over 20%of its maintenance budget on pavement maintenance in winter.However,despite extensive research,it remains a challenging task to monitor pavement conditions and detect slippery roadways in real time.Most existing studies have mainly explored indirect estimates based on pavement images and weather forecasts.The emerging connected vehicle(CV)technology offers the opportunity to map slippery road conditions in real time.This study proposes a CV-based slippery detection system that uses vehicles to acquire data and implements deep learning algorithms to predict pavements’slippery conditions.The system classifies pavement conditions into three major categories:dry,snowy and icy.Different pavement conditions reflect different levels of slipperiness:dry surface corresponds to the least slippery condition,and icy surface to the most slippery condition.In practice,more attention should be paid to the detected icy and snowy pavements when driving or implementing pavement maintenance and road operation in winter.The classification algorithm adopted in this study is Long Short-Term Memory(LSTM),which is an artificial Recurrent Neural Network(RNN).The LSTM model is trained with simulated CV data in VISSIM and optimized with a Bayesian algorithm.The system can achieve 100%,99.06%and 98.02%prediction accuracy for dry pavement,snowy pavement and icy pavement,respectively.In addition,it is observed that potential accidents can be reduced by more than 90%if CVs can adjust their driving speed and maintain a greater distance from the leading vehicle after receiving a warning signal.Simulation results indicate that the proposed slippery detection system and the information sharing function based on the CV technology and deep learning algorithm(i.e.,the LSTM network implemented in this study)are expected to deliver real-time detec-tion of slippery pavement conditions,thus significantly eliminating the potential risk of accidents.