The first step is to determine the principal dimensions of the design ship, such as length between perpendiculars, beam, draft and depth when accomplishing the design of a new vessel. To make this process easier, a database with a large amount of existing ship data and a regression analysis technique are needed. Recently, deep learning, a branch of artificial intelligence (AI) has been used in regression analysis. In this paper, deep learning neural networks are used for regression analysis to find the regression function between the input and output data. To find the neural network structure with the highest accuracy, the errors of neural network structures with varying the number of the layers and the nodes are compared. In this paper, Python TensorFlow Keras API and MATLAB Deep Learning Toolbox are used to build deep learning neural networks. Constructed DNN (deep neural networks) makes helpful in determining the principal dimension of the ship and saves much time in the ship design process.

In this study, natural fine aggregates(NFA) and recycled fine aggregates(RFA) was applying in the lightweight foamed concrete. To replacement of OPC, high volume blast furnace slag was used and incineration ash and recycled aggregates powder were used as alrali activator, Fundamental performances of lightweight famed concrete has been tested. Results showed that as the increase of foam conduction ratio, the strength and the density has been decreased

Comparing with using ordinary Portland cement, using blast furnace slag as binder in concrete shows low resistance for carbonation phenomenon. In this research, we focus on carbonation of high volume blast furnace-blended concrete, analysis the reason and find a more efficient method which comparing with using ordinary Portland cement, using FC shows better effort on concrete carbonate resistant.