The study used the whole-life carbon assessment method to conduct a thorough carbon-neutral evaluation of a standard steel structure. To further assess carbon emissions, 11 design-changed models were evaluated, with changes made to the span between beams and columns. The results of the carbon emission assessment showed savings of approximately 13.1% by implementing the stage of the beyond life cycle. Additionally, the evaluation of carbon emissions through design changes revealed a difference of up to 42.2%. These findings confirmed that recycling and structural design changes can significantly reduce carbon emissions by up to 48.6%, making it an effective means of achieving carbon neutrality. It is therefore necessary to apply the stage of beyond life cycle and structural change to reduce carbon emissions.

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.

This study presents the estimation of crack depth by analyzing temperatures extracted from thermal images and environmental parameters such as air temperature, air humidity, illumination. The statistics of all acquired features and the correlation coefficient among thermal images and environmental parameters are presented. The concrete crack depths were predicted by four different machine learning models: Multi-Layer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), and AdaBoost (AB). The machine learning algorithms are validated by the coefficient of determination, accuracy, and Mean Absolute Percentage Error (MAPE). The AB model had a great performance among the four models due to the non-linearity of features and weak learner aggregation with weights on misclassified data. The maximum depth 11 of the base estimator in the AB model is efficient with high performance with 97.6% of accuracy and 0.07% of MAPE. Feature importances, permutation importance, and partial dependence are analyzed in the AB model. The results show that the marginal effect of air humidity, crack depth, and crack temperature in order is higher than that of the others.

In the era of the Fourth Industrial Revolution, Various attempts are being made to converge new industries with IT industry to find new growth engines in the field of IT, maximizing efficiency in terms of productivity. 3D printers are also related to this, and various studies have been conducted worldwide to utilize them in the construction industry. At present, there is an active effort to study atypical structures using 3D printers. The most widely used method is the use of glass panels, however, the additional cost of the manufacturing process and thus the overall project cost cannot be ignored. In addition, the construction of the curvature of the existing two-way curved surface in the conventional flat joint method is not suitable for implementing an amorphous shape. In this paper, we propose an optimized shape through Abaqus analysis of various shapes of Space Truss interior using 3D printing technology using polymer.

Crack in concrete surfaces is one of the earliest signs of decomposition of the essential structure and constant exposure will cause serious damage to the structure and environment. In most of the safety assessment and fracture mechanic applications proposed that these cracks and defects eventually will grow and will have potential lead to in-service failure. Crack in concrete surfaces is one of the earliest signs of decomposition of the essential structure and constant exposure will cause serious damage to the structure and environment. Currently, non-destructive methods are getting popular in the field of inspecting defects in structure and one of them in trends is that using the thermographic image to detect hidden effects. However, the accuracy of the thermal camera, also called resolution, is highly dependent on camera variables such as lens, detector, sensitivity etc. Also, the most important question that needs to be answered for this research is what happens to the image in fog, rain or other climatic conditions where the camera detects crack which exceptionally smaller than most thermographic applications detects. This paper investigates the accuracy of thermal images obtained by the thermal camera under various weather condition and aims at providing information about optimum choice of environmental condition where the more favorable thermal images can be obtained and increase survey reliability and accuracy of the analysis.

Construction of irregular-shaped concrete structures requires a lot of time and money. To reduce the cost and time, the F3D(Free-Form Formwork 3D Printer) technology was adopted in manufacturing EPS(Expanded Polystyrene) formwork for irregular-shaped concrete structure. To design EPS formwork precisely, lateral pressure acting on irregular-shaped formwork and deformation of EPS form liner should be evaluated. However, in current Korean formwork standard, there are no standards for irregular-shaped formwork as it includes a lot of complex variables. For this reason, several researchers developed 3-dimensional finite element analysis model to calculate lateral pressure exerted by fresh concrete. In this study, deformation of irregular-shaped EPS formwork and lateral pressure acting on formwork was examined using finite element analysis model.