This study investigated the effects of acid-treated MWCNTs on the workability, compressive and tensile strength, porosity, and microstructure of CNT/cement composites. While workability decreased with acid treatment of CNTs, compressive and tensile strength improved significantly. Strength was also enhanced by using acid-treated CNTs alone, compared to using a surfactant with acid-treated CNTs. MIP analysis revealed that the porosity decreases from using acid-treated CNTs and that using acid-treated CNTs without a surfactant was more effective in reducing the size of micropores. A SEM analysis revealed improved CNT dispersion and dense hydration products in cement composites containing acid-treated CNTs. Ultimately, the use of acid-treated CNTs improved the CNT dispersion within CNT/cement composites, enhanced the pore structure, and formed denser hydration products around CNTs.

Cracking in concrete structures affects performance and is a major durability problem. Cracks must be detected and repaired in time in order to maintain the reliability and performance of the structure. This study focuses on vision-based crack detection algorithms, based on deep convolutional neural networks that detect and classify cracks with higher classification rates by using transfer learning. The image dataset, consisting of two subsequent image classes (no-cracks and cracks), was trained by the AlexNet model. Transfer learning was applied to the AlexNet, including fine-tuning the weights of the architecture, replacing the classification layer for two output classes (no-cracks and cracks), and augmenting image datasets with random rotation angles. The fine-tuned AlexNet model was trained by stochastic gradient descent with momentum optimizer. The precision, recall, accuracy, and F₁ metrics were used to evaluate the performance of the trained AlexNet model. The accuracy and loss obtained through the training process were 99.9% and 0.1% at the learning rate of 0.0001 and 6 epochs. The trained AlexNet model accurately predicted 1998/2000 and 3998/4000 validation and test images, which demonstrated the prediction accuracy of 99.9%. The trained model also achieved precision, recall, accuracy, and F₁ scores of 0.99, respectively.

A series of experiments and numerical analyses have been carried out to derive a model of the moisture diffusion coefficient in concrete structures. The moisture diffusion coefficient generally depends on the relative humidity and temperature as well as the age of concrete, because microstructures of concrete develop with aging, especially in the early stage of aging. Therefore, the modified model of the moisture diffusion coefficient proposed here takes these three parameters into consideration. Experiments on moisture diffusion were performed to investigate the variation of the moisture diffusion coefficient with age and temperature under three temperature conditions (20, 50, and 80°C). Based on the experimental results, a new model of the moisture diffusion coefficient considering the aging and temperature was developed by a numerical inverse analysis. As it considers porosity, humidity, and temperature, beyond the existing model for hardened concrete, the suggested diffusion coefficient model is applicable to early age concrete.

In this paper, the post-cracking tensile behavior of Ultra-High Performance Cementitious Composites (UHPCC) was studied and an improved analytical model to predict the behavior depending on the fiber orientation distribution was proposed. Two different casting methods were adopted to estimate the influence of the casting method on the tensile behavior. The direct tensile test results showed that the post-cracking tensile behavior was considerably dependent on the casting method. The influence of the casting method was quantified by image analysis of the fiber distribution. The fiber orientation distribution obtained by image analysis may sometimes include considerable error according to the image resolution, which may cause inaccuracy when predicting the post-cracking tensile behavior based on the fiber orientation distribution. To overcome this dependency, the tensile bridging behavior by the fibers in UHPCC was simulated considering the obtained fiber orientation distribution as well as the number of fibers detected. The post-cracking behavior was then simulated by combining the bridging behavior and tension softening behavior of the matrix. The approach adopted in this study to simulate the post-cracking behavior of UHPCC showed good agreement with the experimental results.

The sabkha soil spreads extensively in the Arabian Gulf Coast region. Sabkha is known as a geotechnically problematic soil because of its loose density, soft consistency, high salinity and water content, and occurrence of fine sands and clays. It is generally highly compressible and requires ground improvement for highway and railway construction. The purpose of this study is to provide a guideline for dynamic compaction to improve the bearing capacity of the coastal sabkha deposit. The ground behavior during dynamic compaction was evaluated for various compaction energy conditions using numerical analysis, and field dynamic compaction tests were also performed and compared with the numerical analysis results. It was found that the bearing capacity of sabkha deposit can be effectively improved by dynamic compaction. However, care must be taken to ensure that excessive porewater pressure is sufficiently dissipated during the application of dynamic compaction because the permeability is pretty low due to the high salt content in groundwater in the sabkha area.