In the era of the Fourth Industrial Revolution, artificial intelligence (AI) is a core technology, and AI-based applications are expanding in various fields. This research explored the influencing factors on end-user’s intentions and acceptance of AI-based technology in construction companies using the technology acceptance model (TAM) and technology–organisation–environment (TOE) framework. The analysis of end-users’ intentions for accepting AI-based technology was verified by applying the structure equation model. According to the research results, the technological factors along with external variables and an individual’s personality had a positive influence (+) on the perceived usefulness and the perceived ease of use of end-users of AI-based technology. Conversely, environmental factors such as suggestions from others appeared to be disruptive to users’ technology acceptance. In order to effectively utilise AI-based technology, organisational factors such as the support, culture, and participation of the company as a whole were indicated as important factors for AI-based technology implementation.
The demand for categorising technology that requires minimum manpower and equipment is increasing because a large amount of waste is produced during the demolition and remodelling of a structure. Considering the latest trend, applying an artificial intelligence (AI) model for automatic categorisation is the most efficient method. However, it is difficult to apply this technology because research has only focused on general domestic waste. Thus, in this study, we delineate the process for developing an AI model that differentiates between various types of construction waste. Particularly, solutions for solving difficulties in collecting learning data, which is common in AI research in special fields, were also considered. To quantitatively increase the amount of learning data, the Fréchet Inception Distance method was used to increase the amount of learning data by two to three times through augmentation to an appropriate level, thus checking the improvement in the performance of the AI model.
Global warming is now considered to be one of the greatest challenges worldwide. International environmental agreements have been developed in response to climate change since the 1970s. The construction industry is considered one of the main contributors to global warming. In order to mitigate global warming effects, the construction industry has been exploring various approaches to mitigate the impacts of carbon dioxide emissions over the entire life cycle of buildings. The application of different structural systems is considered a means of reducing the carbon dioxide emissions from building construction. The purpose of this research is to assess the environmental performance of three different slab systems during the construction phase. In this study, a process-based life cycle assessment (LCA) method was applied in order to evaluate the level of performance of the three slab systems. The results showed total CO2 emissions of 3,275,712, 3,157,260, and 2,943,695 kg CO2 eq. for the ordinary reinforced concrete slab, flat plate slab, and voided slab systems, respectively. The manufacturing of building materials is by far the main contributor to CO2 emissions, which indicate 3,230,945, 3,117,203, and 2,905,564 kg CO2 eq., respectively. Comparing the building materials in the three slab systems, reinforcing bars and forms were significant building materials to reduce the CO2 emissions in the flat plate slab and voided slab systems. In this study, reinforcing bars were the main contributor to lowering the carbon dioxide emissions in the flat plate slab and voided slab systems. The results of this study show that amongst all the three different slab systems, the voided slab system shows the greatest reduction potential. Moreover, replacing the ordinary reinforced concrete slab system by alternative methods would make it possible to reduce the carbon dioxide emissions in building projects.
Reinforced concrete is regarded as one of the ideal structural materials which comprises concrete with high compressive strength and reinforcing bars with high tensile strength. However, concrete has been pointed out that it consumes a large volume of energy and emits a lot of carbon dioxide during its manufacturing. In order to lower such environmental burdens of concrete structures, a number of studies and approaches have been carried out. The voided slab is also suggested as a new method to reduce the environmental burden since voided section of the slab would use less concrete compared with the normal reinforced concrete slab. However, no studies have evaluated the CO2 emissions and environmental performance of voided slabs. The purpose of this study was to evaluate the structural performance of voided slabs and empirically corroborate their environmental influence. The flexural performance test was carried out based on the variables of the depth of slab, types of the void former materials, and the hollowness ratio. In addition, comparison of the emission of CO2 was also performed by considering the hollowness ratio and types of void former materials over the normal reinforced concrete slab. The structural performance of the voided slab was similar or slightly higher than the normal reinforced concrete slab. The yield strength of specimens was increased approximately 10∼30% over the anticipated yield strength. Based on this result, it is considered that the voided slab would be sufficient to structural performance and beneficial to plane planning in buildings. In general, it is considered that the voided slab would be beneficial to both structural and environmental aspects. However, the test results in this research showed that the voided slab would emit more carbon dioxide emissions compared to the normal reinforced concrete slab. The main source of more CO2 emissions in the voided slab was the anchoring materials. In this research, wires were used to fix the void former materials to the reinforcing bars. In order for the voided slab to become a more eco-friendly and sustainable material, new anchoring methods such as use of recycled materials, new void former materials without anchoring, or other eco-friendly materials should be applied to reduce the emission of CO2.
Conventionally, the number of steel rebars at construction sites is manually counted by workers. However, this practice gives rise to several problems: it is slow, human-resource-intensive, time-consuming, error-prone, and not very accurate. Consequently, a new method of quickly and accurately counting steel rebars with a minimal number of workers needs to be developed to enhance work efficiency and reduce labor costs at construction sites. In this study, the authors developed an automated system to estimate the size and count the number of steel rebars in bale packing using computer vision techniques based on a convolutional neural network (CNN). A dataset containing 622 images of rebars with a total of 186,522 rebar cross sections and 409 poly tags was established for segmentation rebars and poly tags in images. The images were collected in a full HD resolution of 1920×1080 pixels and then center-cropped to 512 × 512 pixels. Moreover, data augmentation was carried out to create 4668 images for the training dataset. Based on the training dataset, YOLACT-based steel bar size estimation and a counting model with a Box and Mask of over 30 mAP was generated to satisfy the aim of this study. The proposed method, which is a CNN model combined with homography, can estimate the size and count the number of steel rebars in an image quickly and accurately, and the developed method can be applied to real construction sites to efficiently manage the stock of steel rebars.
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