مهسا دوست محمدی

Porous concrete refers to a combination of cement and water, without fine grains or with a small amount of fine grains, which is important in terms of water transferability and permeability. This material can act as a drainage and carry rainwater and enhance groundwater. In the present article, the effect of replacement of pumice mineral additive with aggregate of porous concrete with volumetric percentages of 25, 50, 75 and 100% was studied on compressive strength, permeability coefficient and porosity percentage. Laboratory analysis was performed using SAS 9.4 software at 95% confidence level for all samples. The results show that with the replacement of the pumice additive in the structure of the porous concrete and the corresponding removal of the aggregate, the compressive strength of the samples is reduced due to the porosity of the pumice structure and the permeability coefficient and porosity increase. The reduction of compressive strength with increasing pumice percentage for all samples was 60.49, 83.86, 85.96 and 86.98%, respectively. Increasing the permeability coefficient with increasing pumice percentage for all samples was 10.13, 14.96, 18.7 and 24.94%, respectively.

Increase of impervious surfaces in urban areas, has led to many problems, including flooding roads and streets during rainstorms which could create difficulties for pedestrians and cars. An effective way of reducing urban runoff and waterlogging is to use permeable concrete pavement. In the present study, the effect of replacing different percentages of travertine additive with the main aggregates of permeable concrete on physical and mechanical properties of this type of concrete has been studied. Samples development and testing with three replicates for each sample was carried out at the Concrete Technology Laboratory of Semnan University. Statistical analysis of the results was carried out using SAS 9.4 software at 95% confidence level for all the samples. Results showed that due to the porous structure of travertine, increasing the percentage of travertine replacement with aggregate increased the porosity and permeability coefficient. The highest porosity was observed for T-100 and T-75 specimens, which was 29.13% and 28.78%, respectively. Also, the highest permeability coefficient (1.96 mm/s) was for T-75 and T-100 specimens. Other results indicated that replacement of travertine with a percentage of aggregates in porous concrete reduced the compressive strength of the samples compared to the control sample. Maximum and minimum compressive strengths were 17.33 and 12.22 MPa, respectively, for T-25 and T-100 specimens; While the compression strength of the control sample was 18.45 MPa. Based on the results, due to porosity, high permeability and suitable resistance of this permeable concrete with travertine , this type of concrete has high potential to be used in pavements, especially in areas with low traffic.

Porous concrete pavement reduces surface runoff volume, prevents flood and reinforces groundwater resources. In this research, effects of replacing concrete aggregates (25, 50, 75 and 100%) by pumice, scoria and zeolite lightweight aggregates in porous concrete on physical properties including compressive strength and porosity were investigated. Preparation of concrete samples and testing was performed in the Concrete Technology Laboratory of Semnan University. Statistical analysis of the results was performed by using SAS 9.4 software. Results showed that by replacing lightweight aggregates in the porous concrete and removing the course aggregates, certain trend was observed among the samples. As the porosity increases, the compressive strength of the samples is reduced. The highest average compressive strength in porous concrete samples was observed in substitution of 50 and 25 percent scoria with aggregates (11.08 and 10.76 Mpa, respectively). The highest average porosity was observed in the sample containing 100% pumice as aggregates, which was 55.95% more than the control sample. Based on the obtained empirical relationship (R2=0.9), the compressive strength of porous concrete can be estimated for different porosities of porous concrete samples. Due to the abundance, economical values, high porosity and reduction of urban runoff, the proposed lightweight natural aggregates of this study have great potential to be used in pavements, especially in areas with low traffic.

The occurrence of a problem in each of the water supply network sections due to pressure or velocity fluctuations can cause disruptions in consumers’ regular life. To help avoid these problems, proper design and optimal management of the network is very important. In this study, control of water pressure and velocity to prevent problems in the water supply network is investigated, and hydraulic flow characteristics in pipes are predicted by artificial neural network. In this regard, first, by zoning of Kangavar city in Kermanshah province (as a case study), six zones were identified, based on distribution parameters, the water supply network for the green space of the city, for 10-year plan and target population of 95000, according to working pattern of 22 hours per day and per capita green space of 29.6 m2 at the end of design period was drawn. Then, EPANET software was used to analyze the pressure, velocity and flow in the pipe network. Based on the results, maximum pressure occurred in the 3-3 joint in the third pressure zone, which was about 100 m of water, and maximum velocity in the network was about 1.4 m/s. Also, results showed that the flow rate used for the network is due to the diameter of the pipes and selected paths in different zones in the appropriate range. Subsequently, artificial neural network was trained using the available quantities and the optimal network was selected with a correlation coefficient of 0.87 and 0.85, respectively, for training and testing phases, respectively. Then, the flow velocity and pipe friction-loss were predicted by the optimal network. Results indicated high potential of artificial neural network in analyzing and predicting hydraulic characteristics of water pipe networks.

Precipitation is one of the hydrological processes that play an important role in controlling water resources management. Shortage of rain causes some problems such as lack of drinking water. Due to the importance of the issue of water shortage, using modern methods to predict hydrological processes will play an important role in planning and management of water resources. Therefore, in this study, monthly shortage of water in Nazloo-chai watershed was predicted using Artificial Neural Network (ANN) and improved wavelet-neural network (IWNN) models, for the past 39 years (1973-2012). Performance of these two models was evaluated using statistical indicators including correlation coefficient (R), determination coefficient (R2) and root mean square error (RMSE). According to the results of IWNN model, the obtained correlation coefficient was 0.960 and 0.945 for testing and training modes, respectively, and this model has greater ability for predicting the shortage of water in comparison with ANN. Accordingly, the amount of monthly water shortage in this watershed was predicted for 2013 to 2020. Results indicated that shortage of water still remains as in the past years. The average water shortage was estimated nearly as 2.95 million cubic meters (MCM) in the next 7 years, while, this parameter for the past 39 years was 4.04 MCM. Therefore, it is required to take necessary measures for future years, and with careful management plans for exploitation of water resources (agriculture, industry, urban, etc.), it is possible to reduce water shortage in the coming years.