Accurately estimating a building's lifespan is crucial for assessing its asset value and determining its economic and environmental feasibility, which is key for decision-making in the construction industry. However, because it's nearly impossible to precisely estimate the lifespan of each building due to the wide range of influencing factors, most studies have used uniform lifespans based on the building's primary structural type. To address this limitation, 1,812,700 records were analyzed of buildings constructed and demolished in Korea to predict each building's lifespan with greater accuracy. Based on the previous study, a prediction model was developed using both deep learning and traditional machine learning methods. This study evaluated whether the building lifespan prediction model experienced overfitting based on the data period used to create the model. A performance evaluation was also conducted, comparing models using only key factors to those using a broader set of factors. The results showed that among the machine learning models, the artificial neural network model, a nonlinear approach, maintained high predictive accuracy without overfitting, regardless of the data period used. The model that used all available factors performed better than those based on just a few key factors. This research demonstrates the viability of using big data and AI for building lifespan prediction, providing a more reliable method for estimating building lifespan tailored to each building's unique characteristics. This approach meets a growing societal demand for more accurate building lifespan predictions.

The real estate market faced a downturn with declining sales, exacerbated by increased construction costs due to conflicts in Russia and Ukraine. Additionally, interest rates on PF loans surged following the Bank of Korea's base interest rate hike. Given these challenges, ongoing real estate development lacks feasibility as sales dwindle and business expenses soar. These risks extend beyond development firms, affecting mid-sized construction companies, second-tier financial institutions, and trust companies, posing broader risks to society. To mitigate these risks, PF risk factors were identified through credible literature and expert opinions. Using ANOVA variance analysis, key PF factors considered by each organization were examined and high-risk items were pinpointed by assessing the correlation between risk factors.

The Building Management Act, enacted on May 1, 2020, emerged in response to the escalating challenges posed by unmanaged and aging buildings, prompting a separation from the Building Code. The legislation sought to institute comprehensive building management plans, addressing the entire life cycle of a building, including maintenance, safety inspections, and post-construction energy conservation. This study, conducted three years post-enforcement, scrutinized the efficacy of the recently introduced building management plan. Findings unveiled that approximately 40% of plans were deemed unsatisfactory, necessitating thorough review. Key contributors to this low fidelity encompassed the absence of system establishment, fostering a limited understanding between builders and officials. Furthermore, issues such as the system's unclear establishment points and the submission of handwritten, non-digitized PDF documents posed substantial hurdles for verification and future utility. To rectify these challenges, proposed solutions include educating authors and government officials, implementing an active follow-up system, and enhancing the system. This study marks the inaugural investigation into the status of building management plans under the Building Management Act, aiming to pinpoint problems and propose effective improvement strategies. The aspiration is that these measures will foster the development and robust establishment of an efficient system.

Many universities aim to collaborate on education, research, and knowledge valorization, making it crucial to create physical spaces that encourage interdisciplinary work. The purpose of this study is to examine the convergence majors offered at the university. It aims to identify which majors emphasize convergence education, to understand the layout and spatial characteristics of the campus, and to see how convergence-focused majors are distributed across the campus. Additionally, it explores the proximity and spatial arrangement of the key buildings related to these majors. A network analysis of convergence majors was performed using the NodeXL program. Once the convergence majors were defined, the spatial characteristics among them were explored with the Depthmap X program. Axial analysis focused on connectivity, integration, choice and mean depth in various building areas across the campus. The administration building, student lounge, and library were all centrally located on campus. Among key convergence majors such as the medical, engineering, and business schools, the business school tended to be at the campus's center, close to other major buildings. Given the campus layout, the College of Business Administration building is a flexible and collaborative space, conveniently located near central areas and other important buildings.

This study explores AI-based prompt design methods and their impact on architectural design process and designers, examining how these approaches alter traditional design processes and redefine architects' roles. By synthesizing the insights of six design practitioners, this study contrasts prompt design with conventional methods, exploring their practical applications and potential future directions. It also delves into the challenges and limitations posed by AI technologies in this evolving context. This study reveals that AI-based prompt design represents a new design paradigm, where prompted keywords play a central role in streamlining workflow, boosting creativity, and facilitating the translation of concepts into architectural schematics. This paradigm shift transforms architects from solitary designers into AI collaborators, necessitating a broader skill set in design literacy. Prompt design, driven by designer-suggested keywords or prompts, enables a more direct and precise process, allowing architects to clearly define their goals and scope. The evolving role of architects reflects this collaborative approach, with AI handling visualization tasks while designers focus on design decision-making. This shift underscores the ongoing relevance and expertise of architects, highlighting their new role as diversified engineering designers in a dynamic AI-driven environment.

The AEC industry faces persistent challenges such as an aging workforce and low productivity. Off-Site Construction (OSC) and Design for Manufacture and Assembly (DfMA) are emerging as solutions to these issues, receiving significant attention and application in the United States, Singapore, Hong Kong, and recently in Korea. DfMA, a design method that incorporates front loading at the design stage, involves analyzing and disassembling all aspects of a building to design modules optimized for transport and installation. Recognized for its potential to enhance safety, reduce construction time, and increase productivity, DfMA addresses the limitations of OSC construction, particularly errors in the complex design and fabrication stages. To enhance OSC's effectiveness and productivity, this study proposes the development of a BIM-based generative design algorithm. This algorithm aims to rapidly generate numerous combinations of building components using domestic OSC materials, applying DfMA principles at the design stage. The research methodology includes a review of DfMA concepts, analysis of case studies and prior research, investigation and classification of domestic OSC building components, and development of a generative design algorithm for building component combinations. By applying this algorithm, the study seeks to identify and verify the optimal combination of components, considering budget and selected types. Despite DfMA's advantages, issues such as design complexity, uniformity, and assembly errors have been extensively studied. The algorithm introduced in this research could contribute significantly to DfMA-based architectural design, improving fabrication and assembly efficiency early in the design process.

The study purpose to analyze parking demand characteristics based on industry type and company size by assessing parking availability within and outside factory facilities in industrial zones. Manufacturing industry averages 238.8m²/car for parking space. Parking demand correlates with factory floor area, notably low for machine and electronics industries with less than 2,000m² and 2,000-3,000m² respectively. However, the metal industry with 5,000-10,000m² floor area shows a 118% increase in parking space per unit. Parking demand varies by company size, with larger floor areas leading to increased parking needs. Small-scale enterprises exhibit a 63% increase, small enterprises 69%, and medium-sized enterprises 75% increase compared to current standards, indicating differing parking demands across company sizes. For companies with less than 2,000m² floor areas across industries, parking space per unit is at 47% of the current standard. Electronics and chemical industries consistently show lower parking space per unit across company sizes. Conversely, small-scale enterprises in metal and machinery industries (3,000-5,000m²) and small enterprises in the metal industry (5,000-10,000m²) exceed current standards for parking space per unit. This analysis underscores the need for tailored parking solutions based on industry and company size within industrial complexes.

Closed schools present a significant challenge as they are often neglected and disconnected from their local communities, constituting 65.6% of the 3,896 closed schools. This abandonment not only entails the loss of a valuable facilities but also eradicates collective memory and historical significance associated with these institutions, thereby impacting the local living environment. Consequently, this study aims to comprehensively analyze the issues surrounding the utilization of closed schools, examining their current state and proposing effective strategies for improvement. Drawing from the background of closed school occurrences, three regions anticipating an increase in closed schools and facing social issues were selected for analysis. The examination of closed school usage progress in three aspects: the process of practical use, utilization patters, and policy and financial assistance. Insights from the practices in foreign countries underscore the importance of guidelines for repurposing closed schools, obtaining permissions for remodeling, exploring diverse utilization opportunities, and fostering collaborations with organizations beyond the Office of Education. By implementing these findings, closed schools can potentially transform into significant spaces within their communities, enriching residents' lives and serving as multifunctional community hubs.

Since the Korea National Housing Corporation (KNHC) constructed the first model house in 1969 to experiment with the pre-sale system for apartment housing, this concept has attracted attention across various disciplines in Korea. However, a comprehensive analysis of trends in model-house research has been lacking until now. This study aims to explore the trends of model houses through a systematic literature review. A total of 95 relevant papers published in Korean academic journals from 1994 to 2023 were examined, utilizing quantitative analysis of sources searched from databases like RISS, DBPIA, and AURIC. The research was then categorized into four groups using content and frequency analysis based on the perception of model houses: apartment interior units, independent facilities, virtual technology, and others such as concepts reflecting housing sales, cultural, philosophical, and urban phenomena of Korea. The frequency analysis revealed that the majority of research focuses on apartment interior units at 45.3%, with significant contributions from the other groups at 20.0%, 21.3%, and 13.7%, respectively. These results not only deepen our understanding of model house research but also substantially broaden perspectives, providing a solid foundation for future studies and enhancing the efficiency of literature reviews for new researchers. This is particularly useful for distinguishing between prominently addressed and less emphasized topics in model house research.

This study explores Le Corbusier's early urban planning ideas as presented in his unpublished work, La Construction des Villes 1910-1911. It contradicts the common view that Le Corbusier was exclusively a proponent of rigid geometric city designs during the 1920s. Instead, La Construction des Villes indicates that he was influenced by Camillo Sitte's more organic approach to city planning. This stands in contrast to Le Corbusier's later and better-known geometric vision, the Radiant City. While discussing his ideal urban block, Le Corbusier shows a preference for layouts with landscaped courtyards, creating safe, green areas for residents. The study also examines his views on streets and squares. Although he often promoted curved streets for their visual appeal and ability to navigate complex terrain, he also recognized the practicality of straight streets in major cities like Paris. Regarding public squares, Le Corbusier shared Sitte's criticism of 19th-century designs but still saw value in certain geometric Baroque squares depending on the context. A key concept in Le Corbusier's approach is la corporalite, which focuses on creating outdoor spaces that function like rooms, blending aesthetics with utility. Through this analysis of La Construction des Villes, this study offers fresh insights into Le Corbusier's urban planning philosophy, challenging conventional interpretations and encouraging a reexamination of his approach to the relationship between city planning and architecture.

This research aims to examine the architectural system and spatial hierarchies maintaining the universality of central government office architecture. It analyzes the types and procedures of attendance rituals (坐起) performed at the central government office around Yukjo Street, based on the legal codes and gwanseoji in the Joseon Dynasty. The legal codes from Gyeonggukdaejeon to Daejeonhoetong record the positions and actions of officials greeting and facing each other. Additionally, Gwanseo literature from the late Joseon Period documents regulations for each government office, applying them to the attendance rituals, which are rituals for superiors to go to work. The spatial elements such as the main gate (大門), middle gate (中門), courtyard (庭), stylobate (中階), exterior column (楹), and wall (壁), along with the hierarchical order according to the level of members, are significant factors that contribute to the universality of the architectural system of the central hall, which varies in function and condition.

This study examines the factors contributing to traffic accidents within apartment complexes. Seoul's vibrant apartment market offers a rich source of data, capturing a variety of complex types over time. Data from a sample of 288 complexes with over 1,200 households in Seoul were analyzed to understand traffic accident trends from 2019 to 2022. Using Poisson regression analysis, this study aims to identify key factors influencing accident rates. Independent variables such as complex size, traffic flow, and neighborhood characteristics were collected and analyzed. The analysis showed that a greater number of households in a complex raised the likelihood of traffic accidents and that CCTV installations also had a significant effect on accident probability. The type of surrounding roads even played a role, with two-way parallel roads presenting the highest risk, followed by two-way corner roads, three-way roads, and four-way roads. This study found that more parking spaces per household tended to lower accident rates, while multi-way intersections increased risk. Additionally, the presence of nearby educational facilities was associated with fewer accidents, and complexes with on-site or adjacent commercial areas experienced fewer accidents. These findings highlight the importance of managing vehicle speed and promoting pedestrian traffic to improve safety awareness among drivers. This study's insights can guide future planning and maintenance strategies for apartment complexes, aiming to create safer environments for both residents and visitors.

This study aims to evaluate the performance of outside air and particle infiltration reduction with application of weather strips across different air gap lengths. To evaluate the performance, an experimental chamber was developed to simulate indoor air, outdoor air, and an air gap. By using the chamber, reduction of air and particle infiltration was evaluated when considering 6 types of weather strips and 3 air gap lengths. The findings indicated that at a pressure difference of 10 Pa, the penetration coefficient increased with longer air gap lengths when using the door sweep types (S1 and S2) and the adhesive-backed tape type. However, there was irregularity in the penetration coefficient with longer air gap lengths when using the door sweep type (S3), the tubular rubber type (S4) and the door shoe type (S6). At a 4 Pa pressure difference, excluding one type (S6), the penetration coefficient increased with longer air gap lengths. Furthermore, the penetration coefficient was low when S5 was attached at a 0 mm air gap length, shifting to S6 at 3 mm, confirming the differing particle infiltration reduction performance of weather strip types at each air gap lengths. Types S1, S2, and S5 showed a strong correlation between the effective leakage area and the penetration coefficient, so it is judged that the infiltration of outdoor air and particle can be reduced at the same time when using the weather strip of the corresponding types. These findings can serve as a guideline for selecting and installing weather strips, and inform the development of weather strips for simultaneous reduction of outdoor air and particle infiltration.

This study aims to analyze the carbon sink aspect of greenhouse gas items in environmental impact assessment reports for urban development projects and propose improvement measures accordingly. To achieve this goal, a quantitative analysis of the change in greenhouse gas emissions before and after the completion of 63 urban development projects spanning form 2013 to 2023 was conducted, with a focus on the carbon sink. The findings revealed that, in many instances, greenhouse gas storage and absorption via the carbon sink have decreased compared to the pre-implementation phase. Furthermore, issues such as uniform prediction of reduction measures were identified. Therefore, recommendations are provided for urban precise predictions and evaluations of impact in relevant areas during project execution, establishing measures. It is expected that these recommendations will enhance the effectiveness and reliability of environmental impact assessment in future project implementations.

This study explores the design and energy assessment of liquid-based cooling systems to meet the growing demands and high power densities of IT equipment in data centers. It creates a baseline model for cooling systems to handle a 200 kW ITE equipment load and sets objective criteria for evaluating cooling energy efficiency. This study identified seven distinct data center workloads and points out the limitations of traditional air-cooling methods for IT power densities exceeding 50 kW/rack, suggesting a shift to liquid-based alternatives. Liquid-based cooling systems were classified into three main types: RDEX, plate cooling, and immersion cooling, with specific operational characteristics depending on the system configuration. To gauge system efficiency, this study introduces the TUE metric, correlating the total energy input to the data center with the energy used by computing components. An analysis of energy efficiency showed a significant reduction in the Power Usage Effectiveness (PUE), with immersion cooling achieving an outstanding PUE of 1.05. The TUE metric also revealed considerable efficiency gains across all liquid-based cooling systems compared to traditional air cooling. This study anticipates a rise in the adoption of liquid-based cooling systems and stresses the need for universal design criteria, taking into account ITE power density and energy efficiency.

This study explored whether classifying elementary schools by size could improve the accuracy of predicting energy consumption. Analyzing energy use across 18,539 data points from various elementary schools, it became apparent that annual energy consumption patterns differed between schools with fewer than 10 classes and those with more than 10 classes. Consequently, prediction accuracy improved when schools were categorized into two groups based on size. Among the models proposed, multi-linear regression, which used the number of classes and students as explanatory variables and annual energy consumption as the response variable, provided the most accurate predictions. A simpler model that employed linear regression with only the number of classes also showed comparable accuracy. In contrast, a model using energy consumption per student had the lowest predictive power. A limitation of this study was the limited access to various explanatory variables, which could have contributed to the lower accuracy in some cases. Future research aims to address this by incorporating a broader range of explanatory variables to refine the prediction model.

This research aims to propose the best placement for outriggers in tall buildings equipped with a two-outrigger system along the building's height direction. To achieve this, a schematic structural design was conducted on a 75-story building. Key factors considered include outrigger stiffness, frame stiffness, shear wall stiffness, outrigger positioning in height, and spacing between upper and lower outriggers. The analysis considered the lateral displacement at the top floor and examined how frame stiffness influences the optimal placement of two outriggers. Previous studies on this topic were also reviewed. This study introduces a more accurate and reasonable method for calculating optimal outrigger positions compared to existing approaches. Additionally, these findings are expected to contribute valuable insights into the optimum location of two outriggers in high-rise buildings.

This study conducted cyclic loading tests where composite beams were linked to assembled concrete-filled steel tube (CFT) columns to examine how the connections behaved mechanically. The key factors included H-shaped steel PC composite beams with stress concentrated in the center, and concrete-filled steel tube SC composite beams with stress concentrated at the edges. Three samples were made, adding another factor: the thickness of the external diaphragm of the same PC composite beam. For the PC composite beam connection sample, stress was centered at the column's core. Conversely, for the SC composite beam connection sample, stress was focused towards the edges. Concerning the external diaphragm, the PC sample showed high stress at both edges, while the SC sample exhibited the opposite behavior. Comparing samples with different external diaphragm thicknesses, those designed within current codes exceeded nominal strength. However, samples designed outside of current codes didn't meet expectations, displaying variations in the connections' internal forces. In terms of stiffness degradation and energy dissipation capacity, samples designed within current codes and connected with PC composite beams performed better. The assessment of the seismic performance of the assembled CFT columns and their connections with the two types of composite beams suggests that they fulfill the requirements for special moment frames (SMF).

Accurate analysis and prediction of Quantity Takeoff (QTO) is crucial for construction project success, enabling efficient resource management, cost and time savings, quality improvement, and project continuity. However, current QTO methods rely on manual labor, posing challenges in productivity and accuracy due to the dependence on workers' experience and skills. This study utilizes an open-source machine learning-based data analysis tool to analyze major QTO components such as reinforcement, concrete, and formwork in a previously executed apartment project. By integrating fundamental project information applicable in the early stages, a predictive model capable of estimating major quantities based on various situational variable combinations was proposed and its reliability was validated.

According to historical records from the Joseon Dynasty, Korean traditional admixtures were made by boiling a mixture of grain powder and water. While the Standard Specification for Cultural Heritage Repair recommends using these traditional admixtures to enhance the workability and adhesion of mortar, it doesn't specify the exact mixing ratio or manufacturing process. This lack of clarity results in inconsistent quality of the admixtures. Without intervention, the techniques for making traditional admixtures could change or disappear altogether. To address this issue, this study aims to develop a method for consistently producing traditional admixtures. Experts were consulted to quantitatively document the manufacturing techniques of four types of admixtures known as Gyomal, Jinmal, Jukmi, and Taemal-based. Analysis of these techniques revealed three factors contributing to quality variation: water content of raw materials, tools used, and dilution method. Three methods were then proposed to mitigate these variations, which were tested through experiments. Results confirmed that each factor indeed influenced quality variation, and implementing all proposed methods simultaneously eliminated errors completely. In conclusion, this study identified quality variation issues with traditional admixtures and presented effective solutions to address them.

This study examines the relationship between the flexural and compressive strengths of early strength portland cement (ESPC) mortars containing limestone powder (LP). Nineteen different mortar mixtures were classified into three groups based on the blaine of the LP. The addition ratio (R_LP) of LP for the partial replacement of ESPC varied from 0% to 30% in 5% increment for each group. Test results revealed that both the 28-day compressive strength and flexural strength of ESPC mortars decreased with an increase in R_LP, with steeper decreases for mixtures with higher blaine of LP. This pattern was insignificantly affected by the curing condition. Flexural strength of ESPC mortars could be evaluated as the square root of the 28-day compressive strength. Based on regression analysis of the test data, empirical equations were proposed to assess the flexural strength gain of ESPC mortars containing LP.

To address COVID-19 concerns, governments have implemented Non-Pharmacological Interventions (NPIs) and treatment-focused policies, particularly in densely populated areas like schools. These measures, including partial closures and time restrictions, raise concerns about socioeconomic impacts. This investigation aims to identify infection factors in academic settings, develop a pedestrian traffic simulation model, establish risk thresholds for disease spread, and conduct policy experiments and impact analyses. Various intervention methods were assessed, such as classroom zoning and movement restrictions, finding that spatial compartmentalization alone is ineffective. However, limiting free movement reduces contact time and infection risk. Nonetheless, intervention effectiveness varies based on student population and density, highlighting the need for further research to recommend optimal strategies based on school size and enrollment. This study aims to support schools and institutions in improving infection control policies and offer practical guidance for decision-making in this area.

This study aims to identify issues within the existing education and training system for construction engineers in construction companies, CM companies, and design offices. It also seeks to determine key personal competencies required in various segments of the construction industry, ultimately suggesting measures to enhance training and education to bolster personal competency in the future. The study revealed that most construction engineers believe personal competency training is essential, with many expressing that current legal education and training hours are inadequate. Among the key personal competencies identified for construction engineers across the entire industry were understanding and responsibility. Additionally, construction company engineers highlighted drive, CM company engineers emphasized communication skills and design office engineers noted analytical skills as crucial. Further analysis showed that critical competencies in urgent need of education improvement include understanding, responsibility, drive, communication, and analytical skills. Furthermore, current competency levels such as planning ability, creativity, and leadership were observed to be relatively low across the industry. Based on this research, the feedback from construction engineers provided recommendations to improve educational content, duration, methods, and costs. These insights aim to guide the construction industry toward more effective training and educational frameworks for enhancing personal competency.