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To control the level of tensile stress in the section of reinforced concrete arch cantilever construction, a construction control method of temporary prestressing reinforcement is proposed in this study. The influence law of temporary prestressing on the arch ring section stress is revealed based on the engineering background (Shatuo Special Bridge in Guizhou). Meanwhile, a temporary prestressing test is carried out on the ring section of the main arch to verify the effectiveness of the method. The results show that the prestressing configuration during the construction of the main arch ring of the suspension arch bridge has obvious influence on the control of tensile stress. The method can also effectively improve the force uniformity of the buckle cables, reduce the variation range of cable forces during construction, and improve the safety of arch ring construction.

We present a novel numerical method for solving ordinary differential equations (ODEs) using Radial Basis Function (RBF) Network with Extreme Learning Machine Algorithm. A single layer Radial Basis Functional Link Neural Network (RBFNN) model has been developed for the proposed method. The weight from the hidden layer to the output layer can be calculated efficiently by Extreme Learning Machine algorithm. The experimental comparison of various methods proves that the proposed method shows better performance than the existing methods.

Longchang Aqueduct is a reinforced concrete box deck type arch with clear span 200m,located in Guizhou Province of China, inclined cable-stayed buckle and cantilever erection is adopted for rib assemble. This paper introduces the arch ring of arch foot section of the construction of a unique process, the Reverse tension Bailey truss method, based on two different casting scheme comparisons, the key of the construction control parameters of the technology is put forward. It is of guiding function for engineering practice.

We present a novel numerical method for solving ordinary differential equations (ODEs) using Radial Basis Function (RBF) Network with Extreme Learning Machine Algorithm. A single layer Radial Basis Functional Link Neural Network (RBFNN) model has been developed for the proposed method. The weight from the hidden layer to the output layer can be calculated efficiently by Extreme Learning Machine algorithm. The experimental comparison of various methods proves that the proposed method shows better performance than the existing methods.