The disaggregation of coarser Precipitation data will help to adjust the deficit of unavailability of data in non-recording gauge stations. The Artificial Neural Network (ANN) facilitates to adjust the rainfall time steps into desired small scales. At first the Geostatistical method of Co-kriging was used for mapping purposes to find the missing duration and depth of rainfall of some incomplete data stations in Sydney Australia. Then, since there was no information about the breakpoint data in non-recording target central station 7261, a process was performed to disaggregate the data of recording gauge station sited besides this non-recording one. Definitely a similar station was delineated firstly using Thiessen polygon to be used instead of station 7261 and then the results of applying two different ANN models

a feed forward back propagation multilayer perceptron (MLP) and a radial basis function (RBF) network
were evaluated to disaggregate the data of this station and the best disaggregation model was introduced.

Keywords:
Rainfall disaggregation, Thiessen polygons, Co-kriging, non-recording gauges, Artificial Neural Network (ANN).

This paper addresses a numerical algorithm for nonlinear analysis of frames using unit displacement method in generating a reduced stiffness matrix of the structure. This algorithm can properly be used in nonlinear static analysis or incremental response spectral method. Here, the instantaneous reduced stiffness matrix of the structure is calculated, considering its linear behavior at the latest state, by performing a set of numerical tests on the whole structure. Each numerical test consists of imposing prescribed displacement fields on lateral displacement of stories and calculating the reactions of the structure. The solution procedure of each test is based on the division of degrees of freedom into three parts: 1-predefined lateral displacement of joints

2- vertical displacement of joints, considered as linear degrees of freedom
3- rotation of joints, regarded as nonlinear ones. The stiffness matrices are generated distinctly for all mentioned parts. Therefore, the linear stiffness matrix is inverted one time at the beginning of the analysis. The suggested method is not limited to any special case or physical assumptions. This model has good accuracy in representing structural responses and modal properties, confirmed by different numerical examples. Regarding the computational cost, the proposed algorithm is more efficient in comparison with the conventional method.

Keywords:
Nonlinear analysis
Reduction method
structural frames
Numerical test
Stiffness method
Displacement control.