Journal of Civil and Environmental Engineering

Road pavement friction is a main physical phenomenon of interaction between tires and the road surface; it provides skid resistance during accelerations and decelerations. The paper focuses on the effects, at laboratory scale, of geometry (in terms of fine aggregates - minimum/maximum size of 0.063/2 mm - content), mineralogy (basalt or metamorphic - minimum/maximum size of 8/16 mm) and shape (in terms on Flakiness and Shape index) on friction performance of asphalt wearing courses for road pavements, in terms of British Pendulum Number and Mean Texture Depth. Laboratory results were statistically treated and used to define predictive formulas which correlate friction characteristics and the explanatory variables considered by the study. The amount of fine particles in the mix and the shape of coarse aggregates demonstrated to affect friction properties and macro texture.

Fire suppression systems are an extremely important part of every building that ensures the safety of the occupants and limits damage due to fire. Full scale model is set up for the purpose of predicting the geometry of fire spread and water mist particles distribution and movement. The results are based on full scale model simulation for several properties of water mist droplet. The water mist droplet speed, mist flux, droplet size and droplet distribution are discussed. It can be concluded that The water mist droplet movement, particle size and droplet distribution play a vital role to suppress fire.The present study is carried out using Fire dynamic simulator (FDS) to perform the model of room fire scenario require to investigate and compute particle distribution, droplet velocity, mist flux and droplet size.

Although beam to beam joints are a critical part of steel structures and connection strength varies with temperature considerably, regrettably very few works have been conducted under elevated temperature conditions on end plate moment connection. The strength and stiffness properties degrade with increase of temperature. This paper presents an investigation of a bolted extended end-plate beam to beam joint subjected to variation of temperature. 20°C, 204°C, 316°C, 427°C, 649°C temperatures were considered for finite element analysis and their effects on end-plate moment connection were discussed. It has been found that with the increase of temperature, end plate thickness does not vary but stiffness of end plate reduces considerably. Finally, the effects of the changes of different beam parameters on the end-plate moment connection were discussed.

In underground construction, ground water inflow and rock mass stability has been a challenging problem. Especially the tunnels constructed below ground water table are often exposed to risk of ground water inflow. Protection of underground structures against water ingress and rock mass failure is of paramount importance during construction as well as operation of the tunnel. Risks associated with these hydrogeological issues often pose challenge to tunneling engineers. Mitigation of these risks by means of pragmatic measures such as pre-grouting can improve the rock mass strength and restrict the water ingress to acceptable levels. The primary purpose of pre-grouting is to improve the rock mass strength and establish an impervious zone around the tunnel periphery, thus leading to reduced support system and permeability of rock mass. The aim of this article is to discuss the concept of pre-grouting as an effective mitigation measure for leakage control and rock mass improvement which is being successfully implemented in many countries.

The climatology provides, for a given location or region, the time series of drought strength, the number, the mean duration, and the maximum duration of droughts of a given intensity. Similarly to most hydrological phenomena, droughts are characterized by a number of features such as their severity, duration and magnitude. Multivariate drought characterization has not been carried out in the various regions of the African continent despite the disastrous environmental, economic and social impacts of droughts. In the present paper, drought characteristics are modeled jointly in a multivariate frequency analysis (FA) framework for a data set from the Medjerda River, the principal watercourse in Tunisia. To identify drought events, the adopted threshold levels are estimated using the Flow Duration Curve (FDC) method. A sensitivity analysis to the threshold level is conducted. Results indicate that the drought features are significantly dependent and should be considered simultaneously for effective and rational modeling. Frank copula is shown to be the most appropriate copula model to represent drought features for the considered data set. The joint probabilities and bivariate return periods, based on the developed two dimensional copula models, are estimated in order to evaluate the contribution and advantages of bivariate modeling of droughts. These results are of practical relevance to hydrologists and water resources managers in Tunisia for applications in drought risk analysis and drought management, and in general for the optimal planning and management of water resources systems.

An interval parameter two-stage stochastic model for project budgeting and scheduling is developed to dealing with the uncertainties residing in the project management. The model focuses on the probability distribution in activity durations and the uncertainties expressed as intervals in costs. It minimizes the inexact costs (direct costs, indirect costs and penalties) with reference to the specified project completion time and the durations of activities estimated from two-stage stochastic programming. The proposed approach for budgeting and scheduling is a hybrid of two stage stochastic programming and inexact optimization. Solutions obtained from the model will provide a reasonable crashing time plan to accomplish given projects on specific time and come up a lowest cost plan for tardiness. This approach can effectively reflect the interactive relationships among all the uncertain system components. The plan provides useful decision support for project managers through these post-optimality analyses. The developed model is applied to a case study to illustrate its feasibility of dealing the actual project management decision problems. The paper implements the model to a gas pipeline construction project with specified completion time and milestone allocating tasks as the case study. The proposed model provides a systematic framework that facilitates the decision making process and enable project managers to justify the range of the solutions when the decision variables are intervals.

Coal is the only natural resource and fossil fuel available in abundance in India. Consequently, it is used widely as a thermal energy source and also as fuel for thermal power plants producing electricity. India has about 90,000 MW installed capacity for electricity generation, of which more than 70% is produced by coal-based thermal power plants. Hydro-electricity contributes about 25%, and the remaining is mostly from nuclear power plants (NPPs). The problems associated with the use of coal are low calorific value and very high ash content. The ash content is as high as 55– 60%, with an average value of about 35–40%. Further, most of the coal is located in the eastern parts of the country and requires transportation over long distances, mostly by trains, which run on diesel. About 70% oil is imported and is a big drain on India’s hard currency. This paper evaluates an Environmental Impact Assessment (EIA) using the Rapid Impact Assessment Matrix (RIAM). It considers all 4 components: physical / chemical, biological / ecological, social / cultural, economic / operational. These are then evaluated using universal criteria common to all impact consideration. Figures and tables made comparisons much easier. This Matrix was applied to the EIA of the future Coal based thermal power plant at Sompeta village in Srikakulam District, Andhra Pradesh.

This study examines the geostatistics and spatial relationships between annual, seasonal and monthly rainfall in Iran for the period 1975-2014. Precipitation variation models were com-pared in Iran deriving from six geostatis-tical, four regression and five spatial models, using monthly data. A geostatistical and spatial statistical analysis consisting of two measurement sub-models was created based on monthly accumulated precipitation; the data was the monthly and seasonal amounts for the period 1975 - 2014, and were estimated from 140 stations. The results of the new geostatistical-spatial statistical analysis model showed that average monthly precipitation se-ries in Iran were revealed to follow Gaussian distribution given their histogram plots and closeness of their mean and median values. On average, monthly precipitation ranged from 3.22 mm in April to 47.157 mm in December in Iran. The suitable interpolation of monthly precipitation indicates that the accuracy of spring precipitation interpolation (RMSE=0.558) can be applied by IDW (Cross-validation). The kriging interpolation of monthly precipitation indicates that the accuracy of autumn precipitation interpolation (RMSE=0.0822) can be applied by probability kriging of autumn precipitation. The empirical Bayesian kriging interpolation of monthly precipitation indicates that the accuracy of autumn precipitation interpolation (RMSE=0.357) can be applied by empirical Bayesian kriging of autumn precipitation. The temporal-spatial distribution of the precipitation station locations has been studied using the ANN tool of the spatial statistics toolbox of ArcGIS 10.3. Based on the calculated Moran’s Index, approximately all months’ precipitation (with the exception of February) has the monthly spatial distribution of the clustered type. The High/Low Clustering of stations’ monthly precipitation has been studied using the HLC tool. Based on the calculated g-index, approximately all months’ precipitation (except for February and March) has the monthly spatial distribution of the high-clusters type.

Why do most natural slopes have curvilinear rather than planar profiles? What slope shape is best suited for minimizing erosion losses …. convex, concave, compound (convex-concave), or planar (uniform)? Why are most man-made slopes constructed with planar surfaces and uniform gradients? These are not trivial questions - perhaps no other variable affects the stability of slopes with regard to both surficial erosion and mass wasting as does topography or slope morphology. Topographic parameters normally considered in estimating soil erosion losses include inclination and length of slope. Surprisingly, slope shape is seldom if ever considered. Conceptual and mathematical models, as well as the results of laboratory tests and field observations, can be used to determine the effect of slope shape on both mass stability and resistance to rainfall erosion.

This study evaluates the natural water suitability for drinking and agricultural purposes in the Jungar Basin in Central Asia. The ionic chemistry of natural waters from the basin changes considerably on a regional scale. The waters are neutral to alkaline and most of them are soft-fresh waters. The total dissolved solid (TDS) varies over two orders of magnitude. Much of the solutes and physicochemical parameters in water are under the highest desirable limits of the World Health Organization (WHO) for drinking purpose and a relationship between sodium adsorption ratio versus EC shows that most waters are of good water quality for irrigation. The effects of local pollution are minimal in the montane and piedmont areas of these watersheds but are significant in the oases and central areas of the drainage basins.

This article shows the drones and possibilities of their using. First there was discussed construction of the drone, which the most important elements are frame, propellers, engine, system of power the electronic control and communication system. A drone is powered by batteries, which is the major drawback, because it is exhausted after 15 minutes of flight, causing a decrease drone on the ground. The lithium-polymer batteries are used for powering the drones. Then there were compared the military and civilian drones on selected examples. Military drones differ from civil of size and drive. They are bigger and powered by internal combustion engines. Civil drones are driven by electric motors. Next there were shown the possibilities of using the drones. They can be used by the public services (like police, fire brigades, border guards), by army, in industry, for taking photos and filming, in delivering shipments. The article shows the danger connecting with using the drones. The main danger of using the drones is the fall of a drone from a great height, which may be due discharge of the battery, damage caused by weather conditions (low air temperature, precipitation), hitting in an obstacle (tree, building, high-voltage line). Currently a lot of projects related to the development of power for drones are conducted like battery of grapheme, pure lithium anodes, and fuel cells. A very important risks associated with the extensive use of civilian drones is related with privacy and the rights of citizens.

The paper investigates the Ag release from textiles (socks available in shops, cotton and viscose filled with Ag nanoparticles–AgNPs) into a washing solution under different conditions. The released Ag amount depended on various factors, such as test media (chemical composition, temperature and water volume), metal contents, and textile impregnation conditions. Furthermore, it turned out that the textile type and colour were the main parameters affecting the Ag release. The Ag concentration in the washing solution ranged between 0.015 μg/l and 4.44 μg/l (socks) and between 0.61 μg/l and 103 μg/l (AgNPs–filled cotton). The total Ag percentage released into water during one washing varied considerably among textiles (from less than 1 to 34%). In the tap water presence, the released Ag load decreased from 6.82 to 1.35 μg/l due to the AgCl and/or Ag2CO3 precipitation. The bleaching process had a strong effect on the quality and speciation of Ag rleased during the washing process. For example, the Ag concentration in the washing solution changed from 0.8 μg/l to 12.9 μg/l and was higher by 20 times (white socks) to 170 times (black socks) when compared with washing in water without any oxidising agent. The washing of nano–Ag–treated textiles may have potentially released both ionic and particle forms as smaller AgNPs dissolved more rapidly than large AgNPs. The results of the present study demonstrate that the water solution containing Ag revealed a toxic effect on D. magna and V. fischeri. Among the tested species, D. magna was the most sensitive and revealed acute toxicity for low EC50 values. The V. fischeri response was less sensitive due to the physiological state of the bacteria.

Drive-thru users at fast food restaurants stay in their vehicle and have the engine running instead of walking into the restaurant to place an order for food or beverage. Although the drive-thrus are convenient, and they save time for costumers, they may have negative impacts on the air quality. Idling vehicles waiting in lines at drive-thru facilities waste gas, harm air quality, and increase greenhouse gas emissions. This study examined the emission rates at three fast food restaurants in Houston, TX, with different drive-thru configurations. By driving on each drive-thru facility in two different times of the day (peak hours and non-peak hours), instantaneous speed and acceleration of vehicles were collected on a second-by-second basis using Global Positioning System (GPS) devices. Then, for each second-bysecond data, Vehicle Specific Power (VSP) value was calculated using instantaneous speed and acceleration. VSP and instantaneous speeds of the vehicles were used to obtain the operating mode distribution bins according to the standard provided by the Motor Vehicle Emission Simulator (MOVES). The vehicle emissions were calculated based on the operating mode binning approach. Emission factors analyzed in this study are Carbon Monoxide (CO), Carbon Dioxide (CO2), Oxides of Nitrogen (NOx), and Hydrocarbons (HC). The results of the study showed that the estimated emission is lower at drive-thru facilities with fewer stops and number of lanes.