With the increasing number of aging buildings across Korea, emerging maintenance technologies have surged. One such technology is the non-contact detection of concrete cracks via thermal images. This study aims to develop a technique that can accurately predict the depth of a crack by analyzing the temperature difference between the crack part and the normal part in the thermal image of the concrete. The research obtained temperature data through thermal imaging experiments and constructed a big data set including outdoor variables such as air temperature, illumination, and humidity that can influence temperature differences. Based on the collected data, the team designed an algorithm for learning and predicting the crack depth using machine learning. Initially, standardized crack specimens were used in experiments, and the big data was updated by specimens similar to actual cracks. Finally, a crack depth prediction technology was implemented using five regression analysis algorithms for approximately 24,000 data points. To confirm the practicality of the development technique, crack simulators with various shapes were added to the study.

Visual inspection methods have limitations, such as reflecting the subjective opinions of workers. Moreover, additional equipment is required when inspecting the high-rise buildings because the height is limited during the inspection. Various methods have been studied to detect concrete cracks due to the disadvantage of existing visual inspection. In this study, a crack detection technology was proposed, and the technology was objectively and accurately through AI. In this study, an efficient method was proposed that automatically detects concrete cracks by using a Convolutional Neural Network(CNN) with the Orthomosaic image, modeled with the help of UAV. The concrete cracks were predicted by three different CNN models: AlexNet, ResNet50, and ResNeXt. The models were verified by accuracy, recall, and F1 Score. The ResNeXt model had the high performance among the three models. Also, this study confirmed the reliability of the model designed by applying it to the experiment.

National R&D programs play an important role in the development of a country in this age of the knowledge economy. Since many numbers of R&D programs compete for limited resources such as national R&D budget, the R&D program evaluation problem is a challe

Various humic substances are widely distributed in natural water body, such as rivers and lakes and cause the yellowish or brownish color to water. The evidence that humic substances are precursors of THMs formation in chlorinated drinking water has been reported m the Jiteratures. For the reason of public health as well as aesthetics, needs for humic substances removal have been increased in the conventional water treatment processes. In this research, the characteristics of aluminium coagulation of humic acids and humic acids were investigated. The optimum pH and coagulants dosage to remove these materials simultaneously by coagulation were alto studied. The results are as followed; 1. UV-254 absorptiometry for measuring the concentration of aquatic humic acids showed good applicability and stable results. 2. The optimal pH range for humic acids removal by aluminium coagulation was 5 to 5.5, however, an increase in aluminium coagulant dosage could enhance the removal rate of humic acids in the wide pH range. 3. Coprecipitation of humic acids in the typical pH range of 6.5 to 8 in water treatment processes may require the sweep coagulation mechanism with the excess aluminium coagulant dosage. 4. Using PAC(poly aluminium chloride) or PASS(poly aluminium silica sulfate) as coagulants was able to expand the operating range for removing humic acids. 5. From the coagulation of humic substances(UV-254) and turbidity at pH range of 5.5 - 6.0 and alum dose of 86 ppm, the removal efficiency of turbidity from the reservoir water was above 90% and that of UV-254 was above 70%. 6. By using the reservoir water, the optimum condition of rapid mixing for simultaneous removal of turbidity and UV-254 absorbance was pH of 5.8 and LAS dose of 86 ppm, in this study.