Journal of Irrigation and Drainage Engineering

The instability of earthen embankments caused by subsurface flow draining out of the banks has been a major concern. In an effort to prevent embankment failures, tension resisting synthetic fibers may be an effective additive to increase their mechanical properties such that drainage does not cause failure. In this study, triaxial compression tests measured the increase in peak deviatoric stress with increase in fiber content and length. In addition, laboratory lysimeter experiments (total of eight experiments) were conducted on sandy slopes reinforced with polypropylene (PP) fiber using two different fiber lengths (6 and 12 mm) and gravimetric fiber contents (0.3 and 1.0%) under two different constant piezometric head boundary conditions (25 and 50 cm) maintained in the water reservoir of the lysimeter. Fiber-reinforced sand was compacted in the lysimeter to obtain a 45° slope with dimensions of 55 cm height, 20 cm width, and 100 cm base length. The only experiment that experienced seepage erosion by particle mobilization under 25-cm water pressure head boundary condition was the sand slope reinforced with fiber length and content of 6 mm and 0.3%, respectively. The increase in the water pressure head boundary condition to 50 cm resulted in small-scale sapping of slopes reinforced with 0.3% fiber content, independent of the fiber length. For slopes reinforced with 1.0% fiber content, sapping did not occur, only erosion of sand particles caused by seepage was observed. A slope stability analysis reflected the favorable effect of fiber inclusion, as the increased effective cohesion increased the factor of safety of slopes.

Although furrow irrigation is one of the dominant methods used for growing cotton in Australia as well as other parts of the world because of its inherent advantages including low capital cost, energy, and ease of operation, its efficiency is low and it is still labor intensive. Various previous attempts to develop automated surface irrigation systems have not been adopted commercially despite a demonstrated potential to save labor and water. The major limitations were the complexity of the integration of hardware, software, and cost. Therefore, a commercial prototype smart automation system for the furrow irrigation of cotton utilizing commercially available hardware and software was designed, trialed, and evaluated for cotton crops in Australia. It consisted of (1) automation hardware and software for the control of flows in a farm open channel delivery system, (2) sequencing of the irrigation of fields and sets of furrows, (3) specifically designed in-field flow control infrastructure for water flows into the furrows, and (4) the sensing and simulation required for the real-time selection of optimum times to cutoff for each furrow set. Extensive field trials were conducted during two seasons in a commercial irrigated cotton field to evaluate the performance of the various individual components as well as the performance of the integrated system. These include the performance of different in-field water delivery systems, advance sensors, a telemetry system, and techniques to determine the optimum irrigation duration. The field trials showed that the integrated smart system is able to control the irrigation events with a cut-off time obtained in real time from the real-time optimization or from a simple field specific algorithm to achieve maximum application efficiency. The study demonstrated that the automation of furrow irrigation in row crops is feasible and practical with commercially available equipment and provides the potential for water, labor, and energy savings.

Using two coupled models, this study predicts the maximum local scour depth downstream of sluice gates. The models are an artificial neural network (ANN) coupled with the harmony search (HS) algorithm, and an ANN coupled with a generalized reduced gradient (GRG) method. The models are trained and tested using extensive observations obtained from the literature. The main parameters used to predict the scour are apron length, densimetric Froude number, tailwater depth, and median sediment size. In addition, multiple linear regression (MLR) is applied to express the relationship between independent and dependent variables. Results of the ANN model coupled with HS and with GRG and of the MLR are compared. The performance of ANN is more effective when coupled with the HS algorithm. To increase the ability of the HS algorithm, a parameter varying method is applied. Results lead to the conclusion that ANN coupled with the HS algorithm is an accurate and simple method for predicting the maximum scour depth downstream of sluice gates.