ملخص البحث :

This investigation study was directed to establish the correlation between piled raft foundation and two soil improvement techniques, stone columns and lime columns to evaluate the bearing improvement ratio BCR for the soft clay soil with three values of undrained shear strength, 8 kPa, 10 kPa and 12 kPa. The 12 model tests was conducted in the present work, three models of untreated soil, three models of soil with piled raft, three models of soil treated with stone columns and three models of soil treated with lime columns. The container used in experimental works was made of steel with plane area of 500 mm* 500 mm and 500mm in height. The thickness of soil sample inside the container was 400 mm. The study showed that the piled raft was more efficient in the bearing capacity improvement than the two soil improvement techniques. The bearing improvement ratio were 3.39, 3.27 and 2.78 in the three model tests of piled-raft for three samples of soil, respectively, while the lime columns provided the lowest values of the bearing improvement ratio were 1.64, 1.67 and 1.8 respectively

ملخص البحث :

Strengthening of the concrete structure is one of the most difficult and important tasks of civil engineering. This paper presented the application of nonlinear finite element models in the analysis of Corner Strengthening of CFRP- Confined Concrete Column by using ANSYS software. The finite element models are build using a smeared cracking approach for concrete material and three dimensional layered element for the CFRP composite. The numerical results are compared with the corresponding experimental results of columns. The results show that the stress- strain curve obtained from the analytical data using ANSYS are in good agreement with experimental data. In this paper, the parameters considered are: CFRP strip thickness and its elasticity modulus (locally added at corner column regions for strengthening) in addition to the corner column radius.

ملخص البحث :

Autoregressive Integrated Moving Average (ARIMA) Box-Jenkins models combine the autoregressive and moving average models to a stationary time series after the appropriate transformation, while the nonlinear autoregres-sive (N.A.R.) or the autoregressive neural network (ARNN) models are of the kind of multi-layer perceptron (M.L.P.), which compose an input layer, hidden layer and an output layer. Monthly streamflow at the downstream of the Euphrates River (Hindiya Barrage) /Iraq for the period January 2000 to December 2019 was modeled utiliz-ing ARIMA and N.A.R. time series models. The predicted Box-Jenkins model was ARIMA (1,1,0) (0,1,1), while the predicted artificial neural network (N.A.R.) model was (M.L.P. 1-3-1). The results of the study indicate that the tra-ditional Box-Jenkins model was more accurate than the N.A.R. model in modeling the monthly streamflow of the studied case. Performing a one-step-ahead forecast during the year 2019, the forecast accuracy between the forecasted and recorded monthly streamflow for both models was as follows: the Box-Jenkins model gave root mean squared error (RMSE ¼ 48.7) and the coefficient of determination (R2 ¼ 0.801), while the (NAR) model gave (RMSE ¼ 93.4) and (R2 ¼ 0.269). Future projection of the monthly stream flow through the year 2025, utilizing the Box-Jenkins model, indicated the existence of long-term periodicity.

ملخص البحث :

Flood forecasting and management are one of the most important strategies necessary for water resource and decision planners in combating flood problems. The Muskingum model is one of the most popular and widely used applications for the purpose of predicting flood routing. The particle swarm optimization (PSO) methodology was used to estimate the coefficients of the nonlinear Muskingum model in this study, comparing the results with the methods of genetic algorithm (GA), harmony search (HS), least-squares method (LSM), and Hook-Jeeves (HJ). The average monthly inflow for the Tigris River upstream at the Al-Mosul dam was selected as a case study for estimating the Muskingum model's parameters. The analytical and statistical results showed that the PSO method is the best application and corresponds to the results of the Muskingum model, followed by the genetic algorithm method, according to the following general descending sequence: PSO, GA, LSM, HJ, HS. The PSO method is characterized by its accurate results and does not require many assumptions and conditions for its application, which facilitates its use a lot in the subject of hydrology. Therefore, it is better to recommend further research in the use of this method in the implementation of future studies and applications.