Classification of thermal environment control indicators according to the thermal sensitivity of office occupants.

The control that have the greatest influence on comfortable in the office occupants are the heating, ventilation, and air conditioning (HVAC) system operation and the thermal environment. However, comfortable HVAC operation is difficult in the office space characterized by a recommended standard thermal environment or a centralized HVAC system. To consider the occupant's thermal comfort to the greatest possible extent, must establish a method to quantify the variables related to the occupant's thermal comfort. This study aims to group occupants in Thermal sensation vote (TSV) clusters and perform sensitivity analysis (SA) on the relationship between thermal environmental factors in an office building and each cluster's TSV to establish the typology of the control indicators for each cluster. A total of 10 field experiments were conducted in the same office. This field study was carried out 2022. The indoor thermal environmental parameters, the subjective evaluation of the thermal comfort of the resident and the operation pattern of the heating system were monitored at the same time. A total of 4,200 datasets related to indoor thermal environmental parameters and a total of 1,680 datasets related to occupants' thermal comfort were collected and analyzed. The results of this study show that people have different levels of adaptability and sensitivity to a given thermal environment. This study founded distinguishable similarities in their thermal sensation traits and grouped similar TSV values into five clusters that responded differently to the same thermal environment. Each cluster showed different TSV and Thermal comfort vote (TCV) patterns, which allowed us to classify the groups that had sensitive responses to the thermal environment and those that did not. This study was determined different control indicators and guidelines for the divided groups according to thermal sensitivity.

Developing a Prediction Model of Demolition-Waste Generation-Rate via Principal Component Analysis.

Construction and demolition waste accounts for a sizable proportion of global waste and is harmful to the environment. Its management is therefore a key challenge in the construction industry. Many researchers have utilized waste generation data for waste management, and more accurate and efficient waste management plans have recently been prepared using artificial intelligence models. Here, we developed a hybrid model to forecast the demolition-waste-generation rate in redevelopment areas in South Korea by combining principal component analysis (PCA) with decision tree, k-nearest neighbors, and linear regression algorithms. Without PCA, the decision tree model exhibited the highest predictive performance (R2 = 0.872) and the k-nearest neighbors (Chebyshev distance) model exhibited the lowest (R2 = 0.627). The hybrid PCA-k-nearest neighbors (Euclidean uniform) model exhibited significantly better predictive performance (R2 = 0.897) than the non-hybrid k-nearest neighbors (Euclidean uniform) model (R2 = 0.664) and the decision tree model. The mean of the observed values, k-nearest neighbors (Euclidean uniform) and PCA-k-nearest neighbors (Euclidean uniform) models were 987.06 (kg·m-2), 993.54 (kg·m-2) and 991.80 (kg·m-2), respectively. Based on these findings, we propose the k-nearest neighbors (Euclidean uniform) model using PCA as a machine-learning model for demolition-waste-generation rate predictions.

Fire Safety Knowledge of Firefighting Equipment among Local and Foreign University Students.

Despite the risks at university laboratories, university students are still marginalized from safety management in university laboratories. In addition, the existing studies related to the fire safety knowledge of university laboratories, do not consider the fire safety knowledge of university students with respect to firefighting equipment and the increasing number of foreign university students. To overcome this gap, we conducted a survey on 273 foreign and 144 local students and identified the differences in fire safety knowledge and those in comprehension and response related to firefighting equipment among the participants through statistical analysis. The results of the survey, where respondents were classified into four groups by gender and nationality, found significant differences in fire safety knowledge between gender and nationality. All the groups had difficulty in directly extinguishing a fire using fire extinguishing equipment. The results of this study, that is, those pertaining to the differences in fire safety knowledge depending on the gender and nationality of students and types of firefighting systems are expected to be used as basic data to establish safety education and management plans in the future.

Localization of Cracks in Concrete Structures Using an Unmanned Aerial Vehicle.

Active research on crack detection technology for structures based on unmanned aerial vehicles (UAVs) has attracted considerable attention. Most of the existing research on localization of cracks using UAVs mounted the Global Positioning System (GPS)/Inertial Measurement Unit (IMU) on the UAVs to obtain location information. When such absolute position information is used, several studies confirmed that positioning errors of the UAVs were reflected and were in the order of a few meters. To address these limitations, in this study, without using the absolute position information, localization of cracks was defined using relative position between objects in UAV-captured images to significantly reduce the error level. Through aerial photography, a total of 97 images were acquired. Using the point cloud technique, image stitching, and homography matrix algorithm, 5 cracks and 3 reference objects were defined. Importantly, the comparative analysis of estimated relative position values and ground truth values through field measurement revealed that errors in the range 24-84 mm and 8-48 mm were obtained on the x- and y-directions, respectively. Also, RMSE errors of 37.95-91.24 mm were confirmed. In the future, the proposed methodology can be utilized for supplementing and improving the conventional methods for visual inspection of infrastructures and facilities.

Development of Machine Learning Model for Prediction of Demolition Waste Generation Rate of Buildings in Redevelopment Areas.

Owing to a rapid increase in waste, waste management has become essential, for which waste generation (WG) information has been effectively utilized. Various studies have recently focused on the development of reliable predictive models by applying artificial intelligence to the construction and prediction of WG information. In this study, research was conducted on the development of machine learning (ML) models for predicting the demolition waste generation rate (DWGR) of buildings in redevelopment areas in South Korea. Various ML algorithms (i.e., artificial neural network (ANN), K-nearest neighbors (KNN), linear regression (LR), random forest (RF), and support vector machine (SVM)) were applied to the development of an optimal predictive model, and the main hyper parameters (HPs) for each algorithm were optimized. The results suggest that ANN-ReLu (coefficient of determination (R2) 0.900, the ratio of percent deviation (RPD) 3.16), SVM-polynomial (R2 0.889, RPD 3.00), and ANN-logistic (R2 0.883, RPD 2.92) are the best ML models for predicting the DWGR. They showed average errors of 7.3%, 7.4%, and 7.5%, respectively, compared to the average observed values, confirming the accurate predictive performance, and in the uncertainty analysis, the d-factor of the models appeared less than 1, showing that the presented models are reliable. Through a comparison with ML algorithms and HPs applied in previous related studies, the results herein also showed that the selection of various ML algorithms and HPs is important in developing optimal ML models for WG management.

Risk assessment of asbestos containing materials in a deteriorated dwelling area using four different methods.

The release of asbestos fibers in old buildings, during demolition, or remodeling is associated with severe public health risks to building occupants and workers. In Korea, asbestos was used in several building materials during the 20th century. Although the use of asbestos is currently banned, its widespread earlier use and the current government initiatives to revitalize dilapidated areas make it essential to accurately evaluate the location and status of asbestos-containing materials (ACMs). This study surveyed buildings in an area of deteriorated dwellings targeted for renewal and determined the status and distribution of ACMs in that area. Asbestos distribution maps were generated and asbestos characteristics were analyzed. In addition, the risk posed by the identified ACMs was assessed using four international methods (the Korean Ministry of Environment, US Environmental Protection Agency, American Society for Testing and Materials, and UK Health and Safety Executive methods), and the results were compared. Notable differences between the assessment results were identified and were found to reflect the specific characteristics of buildings in the study area. These findings suggest ACM risk assessments should be specifically tailored to the regions in which they are applied, thereby improving ACM management and promoting both worker and occupant health.

Development of a Prediction Model for Demolition Waste Generation Using a Random Forest Algorithm Based on Small DataSets.

Recently, artificial intelligence (AI) technologies have been employed to predict construction and demolition (C&D) waste generation. However, most studies have used machine learning models with continuous data input variables, applying algorithms, such as artificial neural networks, adaptive neuro-fuzzy inference systems, support vector machines, linear regression analysis, decision trees, and genetic algorithms. Therefore, machine learning algorithms may not perform as well when applied to categorical data. This article uses machine learning algorithms to predict C&D waste generation from a dataset, as a way to improve the accuracy of waste management in C&D facilities. These datasets include categorical (e.g., region, building structure, building use, wall material, and roofing material), and continuous data (particularly, gloss floor area), and a random forest (RF) algorithm was used. Results indicate that RF is an adequate machine learning algorithm for a small dataset consisting of categorical data, and even with a small dataset, an adequate prediction model can be developed. Despite the small dataset, the predictive performance according to the demolition waste (DW) type was R (Pearson's correlation coefficient) = 0.691-0.871, R2 (coefficient of determination) = 0.554-0.800, showing stable prediction performance. High prediction performance was observed using three (for mortar), five (for other DW types), or six (for concrete) input variables. This study is significant because the proposed RF model can predict DW generation using a small amount of data. Additionally, it demonstrates the possibility of applying AI to multi-purpose DW management.

Analysis of Waste Generation Characteristics during New Apartment Construction-Considering the Construction Phase.

The waste generation rate (WGR) is used to predict the generation of construction and demolition waste (C&DW) and has become a prevalent tool for efficient waste management systems. Many studies have focused on deriving the WGR, but most focused on demolition waste rather than construction waste (CW). Moreover, previous studies have used theoretical databases and thus were limited in showing changes in the generated CW during the construction period of actual sites. In this study, CW data were collected for recently completed apartment building sites through direct measurement, and the WGR was calculated by CW type for the construction period. The CW generation characteristics by type were analyzed, and the results were compared with those of previous studies. In this study, CW was classified into six types: Waste concrete, waste asphalt concrete, waste wood, waste synthetic resin, waste board, and mixed waste. The amount of CW generated was lowest at the beginning of the construction period. It slowly increased over time and then decreased again at the end. In particular, waste concrete and mixed waste were generated throughout the construction period, while other CWs were generated in the middle of the construction period or towards the end. The research method and results of this study are significant in that the construction period was considered, which has been neglected in previous studies on the WGR. These findings are expected to contribute to the development of efficient CW management systems.

Application of Crack Identification Techniques for an Aging Concrete Bridge Inspection Using an Unmanned Aerial Vehicle.

Bridge inspection using unmanned aerial vehicles (UAV) with high performance vision sensors has received considerable attention due to its safety and reliability. As bridges become obsolete, the number of bridges that need to be inspected increases, and they require much maintenance cost. Therefore, a bridge inspection method based on UAV with vision sensors is proposed as one of the promising strategies to maintain bridges. In this paper, a crack identification method by using a commercial UAV with a high resolution vision sensor is investigated in an aging concrete bridge. First, a point cloud-based background model is generated in the preliminary flight. Then, cracks on the structural surface are detected with the deep learning algorithm, and their thickness and length are calculated. In the deep learning method, region with convolutional neural networks (R-CNN)-based transfer learning is applied. As a result, a new network for the 384 collected crack images of 256 × 256 pixel resolution is generated from the pre-trained network. A field test is conducted to verify the proposed approach, and the experimental results proved that the UAV-based bridge inspection is effective at identifying and quantifying the cracks on the structures.

Reduced graphene oxide-mediated Z-scheme BiVO4/CdS nanocomposites for boosted photocatalytic decomposition of harmful organic pollutants.

The efficient photocatalytic degradation of harmful organic pollutants (isoniazid (ISN) and 1,4-dioxane (DX)) via the Z-scheme electron transfer mechanism was accomplished using a photostable composite photocatalyst consisting of BiVO4, CdS, and reduced graphene oxide (RGO). Compared to their pristine counterparts, the RGO-mediated Z-scheme CdS/BiVO4 (CdS/RGO-BiVO4) nanocomposites exhibited superior degradation activities, mainly attributed to the prolonged charge separation. RGO was found to be involved in visible-light harvesting and acted as a solid-state electron mediator at the CdS/BiVO4 interface to realize an effective Z-scheme electron transfer pathway, avoid photocatalyst self-oxidation, and lengthen the life span of charge carriers. The results of reactive species scavenging experiments, photoluminescence measurements, and transient photocurrent measurements, as well as the calculated band potentials of the synthesized photocatalysts, supported the Z-scheme electron/hole pair separation mechanism. Additionally, the intermediates formed during the degradation of ISN and DX were identified, and a possible fragmentation pattern was proposed. This systematic work aims to develop photostable Z-scheme composites as unique photocatalytic systems for the efficient removal of harmful organic pollutants.

An estimation framework for building information modeling (BIM)-based demolition waste by type.

Most existing studies on demolition waste (DW) quantification do not have an official standard to estimate the amount and type of DW. Therefore, there are limitations in the existing literature for estimating DW with a consistent classification system. Building information modeling (BIM) is a technology that can generate and manage all the information required during the life cycle of a building, from design to demolition. Nevertheless, there has been a lack of research regarding its application to the demolition stage of a building. For an effective waste management plan, the estimation of the type and volume of DW should begin from the building design stage. However, the lack of tools hinders an early estimation. This study proposes a BIM-based framework that estimates DW in the early design stages, to achieve an effective and streamlined planning, processing, and management. Specifically, the input of construction materials in the Korean construction classification system and those in the BIM library were matched. Based on this matching integration, the estimates of DW by type were calculated by applying the weight/unit volume factors and the rates of DW volume change. To verify the framework, its operation was demonstrated by means of an actual BIM modeling and by comparing its results with those available in the literature. This study is expected to contribute not only to the estimation of DW at the building level, but also to the automated estimation of DW at the district level.

The Effects of Data Collection Method and Monitoring of Workers' Behavior on the Generation of Demolition Waste.

The roles of both the data collection method (including proper classification) and the behavior of workers on the generation of demolition waste (DW) are important. By analyzing the effect of the data collection method used to estimate DW, and by investigating how workers' behavior can affect the total amount of DW generated during an actual demolition process, it was possible to identify strategies that could improve the prediction of DW. Therefore, this study surveyed demolition waste generation rates (DWGRs) for different types of building by conducting on-site surveys immediately before demolition in order to collect adequate and reliable data. In addition, the effects of DW management strategies and of monitoring the behavior of workers on the actual generation of DW were analyzed. The results showed that when monitoring was implemented, the estimates of DW obtained from the DWGRs that were surveyed immediately before demolition and the actual quantities of DW reported by the demolition contractors had an error rate of 0.63% when the results were compared. Therefore, this study has shown that the proper data collection method (i.e., data were collected immediately before demolition) applied in this paper and monitoring on the demolition site have a significant impact on waste generation.

Optimal management program for asbestos containing building materials to be available in the event of a disaster.

The safe management and disposal of asbestos is a matter of considerable importance. A large number of studies have been undertaken to quantify the issue of waste management following a disaster. Nevertheless, there have been few (if any) studies concerning asbestos waste, covering the amount generated, the cost of disposal, and the degree of hazard incurred. Thus, the current study focuses on developing a program for the management of Asbestos Containing Building Materials (ACBMs), which form the source of asbestos waste in the event of a disaster. The study will also discuss a case study undertaken in a specific region in Korea in terms of: (1) the location of ACBM-containing buildings; (2) types and quantities of ACBMs; (3) the cost of ACBM disposal; (4) the amount of asbestos fiber present during normal times and during post-disaster periods; (5) the required order in which ACBM-containing buildings should be dismantled; and (6) additional greenhouse gases generated during ACBM removal. The case study will focus on a specific building, with an area of 35.34m2, and will analyze information concerning the abovementioned points. In addition, the case study will focus on a selected area (108 buildings) and the administrative district (21,063 buildings). The significance of the program can be established by the fact that it visibly transmits information concerning ACBM management. It is a highly promising program, with a widespread application for the safe management and optimal disposal of asbestos in terms of technology, policy, and methodology.

Estimating the Additional Greenhouse Gas Emissions in Korea: Focused on Demolition of Asbestos Containing Materials in Building.

When asbestos containing materials (ACM) must be removed from the building before demolition, additional greenhouse gas (GHG) emissions are generated. However, precedent studies have not considered the removal of ACM from the building. The present study aimed to develop a model for estimating GHG emissions created by the ACM removal processes, specifically the removal of asbestos cement slates (ACS). The second objective was to use the new model to predict the total GHG emission produced by ACM removal in the entire country of Korea. First, an input-equipment inventory was established for each step of the ACS removal process. Second, an energy consumption database for each equipment type was established. Third, the total GHG emission contributed by each step of the process was calculated. The GHG emissions generated from the 1,142,688 ACS-containing buildings in Korea was estimated to total 23,778 tonCO2eq to 132,141 tonCO2eq. This study was meaningful in that the emissions generated by ACS removal have not been studied before. Furthermore, the study deals with additional problems that can be triggered by the presence of asbestos in building materials. The method provided in this study is expected to contribute greatly to the calculation of GHG emissions caused by ACM worldwide.

Predicting the mortality from asbestos-related diseases based on the amount of asbestos used and the effects of slate buildings in Korea.

Asbestos has been used since ancient times, owing to its heat-resistant, rot-proof, and insulating qualities, and its usage rapidly increased after the industrial revolution. In Korea, all slates were previously manufactured in a mixture of about 90% cement and 10% chrysotile (white asbestos). This study used a Generalized Poisson regression (GPR) model after creating databases of the mortality from asbestos-related diseases and of the amount of asbestos used in Korea as a means to predict the future mortality of asbestos-related diseases and mesothelioma in Korea. Moreover, to predict the future mortality according to the effects of slate buildings, a comparative analysis based on the result of the GPR model was conducted after creating databases of the amount of asbestos used in Korea and of the amount of asbestos used in making slates. We predicted the mortality from asbestos-related diseases by year, from 2014 to 2036, according to the amount of asbestos used. As a result, it was predicted that a total of 1942 people (maximum, 3476) will die by 2036. Moreover, based on the comparative analysis according to the influence index, it was predicted that a maximum of 555 people will die from asbestos-related diseases by 2031 as a result of the effects of asbestos-containing slate buildings, and the mortality was predicted to peak in 2021, with 53 cases. Although mesothelioma and pulmonary asbestosis were considered as asbestos-related diseases, these are not the only two diseases caused by asbestos. However the results of this study are highly important and relevant, as, for the first time in Korea, the future mortality from asbestos-related diseases was predicted. These findings are expected to contribute greatly to the Korean government's policies related to the compensation for asbestos victims.

Hierarchical weeping willow nano-tree growth and effect of branching on dye-sensitized solar cell efficiency.

In this paper we have demonstrated the simple, low cost, low temperature, hydrothermal growth of weeping willow ZnO nano-trees with very long branches to realize high efficiency dye-sensitized solar cells (DSSCs). We also discuss the effects of branching on solar cell efficiency. By introducing branched growth on the backbone ZnO nanowires (NWs), the short circuit current density and the overall light conversion efficiency of the branched ZnO NW DSSCs increased to almost four times that for vertically grown ZnO NWs. The efficiency increase is attributed to the increase in surface area for higher dye loading and light harvesting and also to reduced charge recombination through direct conduction along the crystalline ZnO branches. As the length of the branches increased, the branches became flaccid and the increase in solar cell efficiency slowed down because the effective surface area increase was hindered by branch bundling during the drying process and subsequent decrease in the dye loading.