International Journal of Civil Engineering
No. 11, 2003

This Paper is the result of a research project on a pavement management system that was performed by the Transportation Division of Iran University of Science and Technology. Information used in the project was gathered from 20 zones of the Tehran Municipality. Any maintenance and repair system for roads has a number of general and coordinated activities in conjunction with programming, designing, construction, Maintenance, Evaluation, and research on road pavement. Prediction of pavement condition is one of the most important parts of, such system. Prediction models have their application at the network level as well as project level activities. At the network level it is used in predicting the condition for budget programming. While in project level it is used in economical analysis. Many factors have been used in determination of pavement condition. These factors are the design life of the pavement, loading, climatic condition, and the type of road. To be able to plan for future improvements we need to predict the future condition of the pavement. In this paper, factors affecting the prediction of pavement condition are discussed. A model is developed exclusively for Tehran based on the distress data collected.

Ambient vibration test is an effective and economical method for identification of dynamic properties of structures such as dams. Mathematical models generally are developed for the design purpose. Structural and material parameter are assumed from similar projects or limited material tests. Therefore it is usually desirable to verify the results obtained from mathematical model by performing vibration test on the actual as-built structure and process its, data correctly. There are addressed in this paper. A modification of mathematical model could then be performed.

An experimental study was carried out to evaluate the influence of silt content on cone penetration measurements and its implication for soil classification. The investigation includes twenty-seven peizocone tests in saturated salty sand samples, which had been prepared in a big rigid thick walled steel cylinder-testing chamber. The samples were prepared with several different silt contents ranging from 0 to 50 percent and were consolidated at three-overburden effective stresses including 100, 200 and 300 kPa. This study showed that, the amount of silt content in sand is an important parameter affecting CPT results. As the silt content increases, the cone tip resistance decreases. The recorded excess pore water pressure during sounding was increased with increasing silt content. It is also concluded that friction ratio, in general, increases with increasing silt content. The method presented by Robertson and Wride [25] and Olsen [17] to evaluate soil classification are also verified.

The reliable operation of spillways, in emergency as well as normal conditions, is one of the vital components in dam safety. Free or uncontrolled overflow spillways are the most reliable choice; however. They usually impose higher construction cost and /or results in wasting a considerable amount of water or live capacity of the reservoirs. Employing fuse gates might be a way of reconciling dam safety with maximized storage capacity. The operation of the system can be controlled to within a few centimeters, and the entire installation is not lost for floods less than the maximum design flood. The installation offers more or less the same level of safety as ungated spillways, but avoids their inherent storage capacity loss. Optimum design of fuse gates in particular installation calls for a mathematical model. The model developed in this work includes structural, hydraulics and operational constraints while maximizing the expected cost over the useful life of the project. Accounting for the lost benefit (i.e., water lost as a result of gate tilting) has an influenced effect on the optimum design. To test the performance of the model, data from Zarineh Rud dam in Iran has been used and its result is compared with a direct search technique. The model is capable of helping the design engineer to select the best alternative considering different types of constraints.

In the present paper, ANN is used to predict the tidal level fluctuations, which is an important parameter in maritime areas. A time lagged recurrent network (TLRN) was used to train the ANN model. In this kind of networks, the problem is representation of the information in time instead of the information among the input patterns, as in the regular ANN models. Two sets of data were used to test the proposed model. San Francisco Bay tidal levels were used to test the performance of the model as a predictive tool. The second set of data was collected in Gouatr Bay in southeast of Iran. This data set was used to show the ability of the ANN model in predicting and completing of data in a station, which has a short period of records. Different model structures were used and compared with each other. In addition, an ARMA model was used to simulate time series data to compare the results with the ANN forecasts. Results proved that ANN can be used effectively in this field and satisfactory accuracy was found for the two examples. Based on this study, an operational real time environment could be achieved when using a trained forecasting neural network.

In this paper the analysis of the pipe networks is formulated as a nonlinear unconstrained optimization problem and solved by a general purpose optimization tool. The formulation is based on the minimization of the total potential energy of the network with respect to the nodal heads. An analogy with the analysis of the skeletal structures is used to derive tire formulation. The proposed formulation owes its significance for use in pipe network optimization algorithms. The ability and versatility of the method to simulate different pipe networks are numerically tested and the accuracy of the results is compared with direct network algorithms.