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The Semi-Flexible Water Retaining Pavement (SFWRP) has the capability to cool down the temperature of the road surface through its evaporation behavior, including absorbing and evaporating water; this is an efficient approach to relieve the heat island effect in a big city. The temperature feedback from different material surface were investigated in this paper in the same test condition, it has been proved that the SFWRP material can remarkably cool down the temperature of the road surface. The mechanism of the material evaporation behavior, including flux calculation formula of the water vapor inside the air void, were studied by inter-phase continuous function, in which the structural properties of the SFWRP material was taken into account. Furthermore, the function calculating the evaporation of the water vapor was then developed in this research through heat and mass transfer analogy. Besides, the calculating results can be captured by the self-coding program in Finite Element Modeling (FEM) for water evaporation simulation. Also, the results of laboratory tests were adopted to validate the calculating model. Finally, it has been proved that the mortar was recommended to be used in semi-flexible water retaining pavement to serve as material with permeable and water retaining property, and the semi-flexible water retaining pavement material is recommended to applied in the surface layer of the permeable pavement.

Real-time mixed gas detection has attracted significant interest for being a key factor for applications of the electronic nose (E-nose). However, mixed gas detection still faces the challenge of long detection time and a large amount of training data. Therefore, in this work, we propose a feasible way to realize low-cost fast detection of mixed gases, which uses only the part response data of the adsorption process as the training set. Our results indicated that the proposed method significantly reduced the number of training sets and the prediction time of mixed gas. Moreover, it can achieve new concentration prediction of mixed gas using only the response data of the first 10 s, and the training set proportion can reduce to 60%. In addition, the convolutional neural network model can realize both the smaller training set but also the higher accuracy of mixed gas. Our findings provide an effective way to improve the detection efficiency and accuracy of E-noses for the experimental measurement.

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