Monitoring the Whole Cycle Length Change of Cement Mortar Incorporated with SRA by CMOS Image Sensor This paper introduces a new method to measure whole cycle length change nondestructively and continuously using a digital image analysis system. The macroscale length changes of mortars containing different shrinkagereducing admixture (SRA) dosages (0%, 1%, 2% and 5% by cement weight) were first determined using a complementary metal oxide semiconductor (CMOS) image sensor under alternating dry and wet curing conditions. After that, the length change was calculated using developed digital image processing technology (DIPT) software. After that, several significant conclusions could be drawn by combining with the results of systematic tests of the macroscopic and microscale physical properties of the cement mortar using Xray diffraction, scanning electron microscopy, mercury intrusion porosimetry (MIP) and nuclear magnetic resonance (NMR) methods. The test results indicated that SRAs exhibited significant effects on the shrinkage inhibition of cement mortars, whereas the shrinkage reduction behaviour was also affected by varying the curing conditions. The MIP and NMR analyses demonstrated that SRAs reduced the irreversible shrinkage of the cement mortars by decreasing the volume percentage of the 3xe2x80x9350 nm pores and promoting the conversion of calcium silicate hydrate gel from an oligomeric to a high polymerization state thereby improving the volume stability of cement mortars.

Artificial neural networks for nondestructive identification of the interlayer bonding between repair overlay and concrete substrate Abstract   The article presents the application of artificial neural networks (ANNs) for the nondestructive identification of the pulloff adhesion fb values between the repair overlay with variable thickness and the substrate in concrete surfacerepaired elements. For this purpose, a large database was built on the basis of the tests of model concrete elements. The numerical analyses were performed using this data and ANNs with various learning algorithms. Based on these analyses, it was shown that the ANN with the BroydenFletcherGoldfarbShanno learning algorithm, with thirtyone input parameters and twenty hidden neurons, is the most useful for identifying the interlayer pulloff adhesion in repaired concrete elements. The reliability of the presented application of ANNs was confirmed on the basis of carried out validation, using a part of the database not used in the learning and testing. The applicationu0027s reliability was also confirmed on the basis of experimental verification carried out using the results of tests performed on an additional model element made exclusively for this purpose. This is an important and original issue presented in the article. Another novelty presented in the article is the application of ANNs for a much more difficult case, which is the identification of the pulloff adhesion fb value of the repair overlay of variable thickness from the repaired element and in a very wide range of identified pulloff adhesion fb within the range of 0.5xe2x80x933.60xc2xa0MPa. Moreover, the unique value of the article is the use for the first time of spatial and function related parameters to describe the concrete surface morphology of a repaired element. The investigation presented in the article has also confirmed the high usefulness of these parameters for identifying the value of pulloff adhesion fb.