Search Results (113)

The integration of Building Information Modeling (BIM) and Digital Twin (DT) has emerged as an innovative tool in the architecture, engineering, and construction (AEC) domain. To successfully utilize BIM and DT, it is crucial to update the 3D model in a timely and accurate manner. However, limitations remain when handling massive point clouds to reconstruct complex indoor structures with varying ceiling and floor heights. This study proposes a semi-automatic 3D model reconstruction method. First, point clouds are aligned with 3D Cartesian axes and the spatial extent of the indoor space is measured. Subsequently, the point clouds are projected onto each coordinate plane to hierarchically extract structural elements of a building component, such as boundary lines, rectangles, and cuboids. Boolean operations are then applied to the cuboids to reconstruct a 3D wireframe model. Additionally, wall points are segmented to identify openings like doors and windows. For validation, the method was applied to three typical building components with Manhattan-world structures: an office, a hallway, and a stairway. The reconstructed models were evaluated using reference points, resulting in positional accuracies of 0.033 m, 0.034 m, and 0.030 m, respectively. Finally, the resulting wireframe model served as a reference to build an as-built BIM model. Full article

This study investigates the acoustic conditions of a design studio (Studio 130) in the Department of Interior Architecture and Environmental Design at Afyon Kocatepe University by integrating 14 weeks of continuous noise measurements with perception data collected from 192 students. Noise measurements were conducted in accordance with ISO 3382-3:2022 guidelines at three locations—window front, door side, and studio midpoint—during morning, noon, and evening periods, with 10 min recordings at each session. The results indicate that when students were present, the equivalent continuous noise level (Leq) reached an average of 65.5 dB(A), with peak levels rising to 72.3 dB(A) during jury sessions. These values substantially exceed the recommended 35 dB(A) classroom threshold by the World Health Organization and the 35–45 dB(A) limits specified in national regulations for indoor educational spaces. Survey findings reveal that 88% of students experienced loss of concentration, 72% reported decreased productivity, 60% had difficulty communicating, and 52% reported fatigue due to noise exposure. Pearson correlation analysis demonstrated a strong relationship between measured noise levels and reported negative effects (r = 0.966). Moreover, independent samples t-test results confirmed that student presence significantly increased studio noise levels (t = 4.98, p < 0.001). The novelty of this research lies in its combined use of longitudinal objective measurements and subjective perception data, addressing the unique open-plan, collaborative, and critique-based pedagogical structure of design studios. The findings highlight that acoustic comfort is a critical component of learning quality in studio-based education. Based on the results, the study proposes several design and material interventions—including spatial dividers, acoustic ceiling panels, fabric-wrapped absorbers, and impact-reducing flooring—to enhance auditory comfort. Overall, the study emphasizes the necessity of integrating acoustic design strategies into studio pedagogy to support concentration, communication, and learning performance. Full article

Stack effects in high-rise buildings cause noise, drafts, and elevator door malfunctions during cold weather yet remain difficult to control. Because vertical shafts couple pressures between floors, local fixes at a single lobby can unintentionally disturb the pressure field elsewhere. To analyze these interactions, we developed a measurement-calibrated CONTAM multizone model of a 43-story office building and evaluated representative local countermeasures. Under base winter conditions, the pressure difference across the problematic first-floor high-rise elevator doors is 56 Pa, driving approximately 1300 CMH of airflow through the door line. First-floor depressurization reduces this to 34 Pa (about 30% lower airflow) but simultaneously increases the pressure at the main entrance doors from 19 to 39 Pa. Additional first-floor partitions slightly reduce pressures on upper high-rise floors, whereas opening exterior windows in the high-rise zone increases shaft airflow by 7.7% and further amplifies elevator door pressures. We show that neutral pressure level (NPL) shifts into vertical shafts are a key mechanism limiting the effectiveness of purely local interventions. These results demonstrate that effective countermeasures must be designed at the whole-building scale, jointly controlling pressure redistribution and neutral-pressure-level movement while directing unavoidable pressure transfer toward the exterior envelope and away from sensitive interior spaces. Full article

Three-dimensional (3D) building models are essential for urban planning, spatial analysis, and virtual simulations. However, most reconstruction methods based on Airborne LiDAR Scanning (ALS) rely primarily on rooftop information, often resulting in distorted footprints and the omission of façade semantics such as windows and doors. To address these limitations, this study proposes an automatic 3D building reconstruction method driven by façade geometry. The proposed method introduces three key contributions: (1) a façade-guided footprint generation strategy that eliminates geometric distortions associated with roof projection methods; (2) robust detection and reconstruction of façade openings, enabling reliable identification of windows and doors even under sparse ALS conditions; and (3) an integrated volumetric modeling pipeline that produces watertight models with embedded façade details, ensuring both structural accuracy and semantic completeness. Experimental results show that the proposed method achieves geometric deviations at the decimeter level and feature recognition accuracy exceeding 97%. On average, the reconstruction time of a single building is 91 s, demonstrating reliable reconstruction accuracy and satisfactory computational performance. These findings highlight the potential of the method as a robust and scalable solution for large-scale ALS-based urban modeling, offering substantial improvements in both structural precision and semantic richness compared with conventional roof-based approaches. Full article

The automatic reconstruction of existing buildings has gained momentum through the integration of Building Information Modeling (BIM) into architecture, engineering, and construction (AEC) workflows. This study presents a hybrid methodology that combines deep learning with surface-based techniques to automate the generation of 3D models and 2D floor plans from unstructured indoor point clouds. The approach begins with point cloud preprocessing using voxel-based downsampling and robust statistical outlier removal. Room partitions are extracted via DBSCAN applied in the 2D space, followed by structural segmentation using the RandLA-Net deep learning model to classify key building components such as walls, floors, ceilings, columns, doors, and windows. To enhance segmentation fidelity, a density-based filtering technique is employed, and RANSAC is utilized to detect and fit planar primitives representing major surfaces. Wall-surface openings such as doors and windows are identified through local histogram analysis and interpolation in wall-aligned coordinate systems. The method supports complex indoor environments including Manhattan and non-Manhattan layouts, variable ceiling heights, and cluttered scenes with occlusions. The approach was validated using six datasets with varying architectural characteristics, and evaluated using completeness, correctness, and accuracy metrics. Results show a minimum completeness of 86.6%, correctness of 84.8%, and a maximum geometric error of 9.6 cm, demonstrating the robustness and generalizability of the proposed pipeline for automated as-built BIM reconstruction. Full article

Windborne debris generated during tornadoes and hurricanes plays a critical role in building damage. This damage occurs either through direct impact on structural and nonstructural components or indirectly by increasing internal pressure when debris penetrates openings (e.g., windows and doors) or creates new ones. These breaches can significantly raise internal pressure, even at lower wind speeds compared to debris-free conditions. Current provisions in ASCE 7, the nationally adopted standard for wind load calculations in the United States, account for factors such as building geometry, location, and exposure category. However, they do not consider the effects of windborne debris on internal pressure coefficients. This study proposes an enhancement to ASCE 7 by incorporating debris effects through the use of a more conservative enclosure classification. Real-world damage observations from three tornado-impacted residential buildings are presented, followed by a failure mechanism analysis, supporting analytical fragility data, and numerical simulations of debris effects on building damage. The findings suggest that treating buildings as Partially Enclosed under ASCE 7 can more accurately reflect debris-induced internal pressures and improve building resilience under extreme wind events. Full article

A major cause of cancer-related mortality globally is colorectal cancer, which emphasises the critical need for state-of-the-art diagnostic tools for early identification and categorisation. We use deep learning methodology to classify colorectal cancer histology images into eight different categories automatically. To improve classification accuracy and maximise feature extraction, we create a DeepFocusNet architecture with attention approaches using a dataset of 5000 high-resolution (150 × 150) histological images. To improve model generalisation, we combine data augmentation, fine-tuning, and freezing early layers into our progressive training approach. Additionally, we create full-scale images using heatmaps and multi-class overlays after breaking up large-scale histology images (5000 × 5000) into smaller windows for classification using a special tiling technique. Attention mechanisms are added to improve the model’s performance and interpretability, as they are proven to focus on the most important histopathological traits. The model provides pathologists with high-resolution probability maps that aid in precise and speedy patient identification. The robustness of our methodology is demonstrated by empirical findings, opening the door for clinical applications of AI-driven histopathological investigation. Pathologists can receive precise and efficient diagnostic support from the final system thanks to its high-resolution probability maps and 97% classification accuracy. Empirical results provide evidence of our methodology’s robustness and show its potential for real-world clinical applications in AI-assisted histopathology. Full article

This study investigates the impact of teacher decisions and other contextual factors on indoor air quality (IAQ) in mechanically ventilated elementary school classrooms using low-cost air quality sensors. Four classrooms at a K–8 school in San Jose, California, were monitored for airborne particulate matter (PM), carbon dioxide (CO₂), temperature, and humidity over seven weeks. Each classroom was equipped with an HVAC system and a portable air cleaner (PAC), with teachers having full autonomy over PAC usage and ventilation practices. Results revealed that teacher behaviors, such as the frequency of door/window opening and PAC operation, significantly influenced both PM and CO₂ levels. Classrooms with more active ventilation had lower CO₂ but occasionally higher PM_2.5 due to outdoor air exchange, while classrooms with minimal ventilation showed the opposite pattern. An analysis of PAC filter material and PM morphology indicated distinct differences between indoor and outdoor particle sources, with indoor air showing higher fiber content from clothing and carpets. This study highlights the critical role of teacher behavior in shaping IAQ, even in mechanically ventilated environments, and underscores the potential of low-cost sensors to support informed decision-making for healthier classroom environments. Full article

This study focuses on thermal comfort in residential buildings within the Iron Triangle area of South Australia, examining how indoor conditions influence residents’ comfort and adaptive behaviours. Conducted from June 2023 to February 2024 across 30 homes in Port Pirie, Port Augusta, and Whyalla, the research gathered data from 38 residents, who reported indoor comfort levels in living rooms and bedrooms. A total of 3540 responses were obtained. At the same time, the measurement of indoor conditions in the buildings was performed using a small HOBO MX1104 device. Using the Mean Thermal Sensation Vote (MTSV) concept, it was possible to determine the neutral operative temperature and temperature ranges for thermal comfort categories. According to the defined linear regression formula, the neutral temperature was 23.9 °C. In living rooms, it was slightly lower, at 23.7 °C, and in bedrooms, slightly higher, at 24.4 °C. For comparison, the neutral temperature was calculated based on the average Predicted Mean Vote (MPMV) and equal to 24.3 °C. Comparison of the regression curves showed that in terms of slope, the MPMV curve is steeper (slope 0.282) than the MTSV curve (slope 0.1726), and lies above it. Regarding the residents’ behaviour, a strong correlation was found between the operative temperature T_o and the degree of clothing I_cl in living rooms. Use of ceiling fans was also studied. A clear trend was also observed regarding window and door opening. The findings of the research can be used to inform the design and operation of residential buildings with a view to enhancing thermal comfort and energy efficiency. Full article

This study aimed to improve indoor air quality (IAQ) in an existing college building in London by addressing two key pollutants: PM_2.5 particles (from indoor and outdoor sources) and SARS-CoV-2 as a biological contaminant. Various mitigation strategies were assessed, including hybrid ventilation that combined CIBSE-recommended rates with partial window and door opening. The effectiveness of HEPA-based air purifiers (APs) and upper-room ultraviolet germicidal irradiation (UVGI) systems with different intensities was also evaluated for reducing viral transmission and the basic reproduction number (R₀). To manage PM_2.5 in the kitchen, HEPA and in-duct MERV13 filters were integrated into the ventilation system. Results showed that hybrid ventilation outperformed mechanical systems by achieving greater reductions in infection probability (P_I) and maintained higher performance as the number of infectors increased, showing only a 2.5–16% drop, compared to 35% with mechanical ventilation. An R₀ analysis indicated that UVGI is more suitable in high-risk settings, while APs combined with hybrid ventilation are effective in lower-risk scenarios. The findings also emphasize that combining Supply–Exhaust ventilation with APs or MERV13 filters is crucial for maintaining safe IAQ in kitchens, aligning with the WHO’s short- and long-term exposure limits. Full article

This study proposes sustainable natural ventilation strategies using the periodic opening and closing of windows and doors to maintain acceptable indoor air quality in a small office space during the winter season. Field experiments were conducted in a 26.8 m² university office room in Seoul, Korea, measuring the indoor and outdoor temperature, humidity, wind speed, carbon dioxide concentration, and fine dust levels. A simulation model based on a first-order differential equation was developed using EES software (version 9) to predict indoor CO₂ concentrations at one-minute intervals. The simulation results showed good agreement with the experimental data, validating the accuracy of the modeling approach. Based on the validated model, practical ventilation durations and intervals were derived according to the occupant number and room volume, ensuring that indoor CO₂ concentrations remained below the recommended 1000 ppm threshold. The results demonstrate that simple, periodic natural ventilation is effective in maintaining acceptable indoor air quality. As a passive strategy requiring no electrical energy, it offers a sustainable and low-cost solution for creating a healthy indoor environment. Full article

The Façade Opening Rate (FOR) reflects a building’s capacity to withstand seismic loads, serving as a crucial foundation for seismic risk assessment and management. However, FOR data are often outdated or nonexistent in rural areas, which are particularly vulnerable to earthquake damage. This paper proposes a practical framework for estimating FORs from real-scene 3D models derived from UAV photogrammetry. The framework begins by extracting individual buildings from 3D models using annotated roof outlines. The known edges of the roof outline are then utilized to sample and generate orthogonally projected front-view images for each building façade, enabling undistorted area measurements. Next, a modified convolutional neural network is employed to automatically extract opening areas (windows and doors) from the front-view façade images. To enhance the accuracy of opening area extraction, a vanishing point correction method is applied to open-source street-view samples, aligning their style with the front-view images and leveraging street-view-labeled samples. Finally, the FOR is estimated for each building by extracting the façade wall area through simple spatial analysis. Results on two test datasets show that the proposed method achieves high accuracy in FOR estimation. Regarding the mean relative error (MRE), a critical evaluation metric which measures the relative difference between the estimated FOR and its ground truth, the proposed method outperforms the closest baseline by 5%. Moreover, on the façade images we generated, the MRE of our method was improve by 1% and 2% compared to state-of-the-art segmentation methods. These results demonstrate the effectiveness of our framework in accurately estimating FORs and highlight its potential for improving seismic risk assessment in rural areas. Full article

Traditional construction of timber houses in Tanzania has been prevalent for years; however, inhabiting these structures has been a challenge due to the instability of the buildings under various loadings. This instability, despite its lightweight, is mainly controlled by mechanical joints within timber members. Parametric Python scripts were developed in Abaqus (version 6.13) to have a reliable joint between timber volume modules and assess their response when subjected to wind forces. Two timber volume modules, each with a height of 3.0 m, were subjected to a horizontal displacement of 10 mm. Results show that the screwed fasteners between the modules result in high shear resistance due to the embedded fastener’s threads in timber members increasing the rope effect. Additionally, with weak fastener stiffness, the openings in the longitudinal wall had no effect on resisting shear compared to strong joints between modules. Longitudinal walls with doors and window openings showed a decrease in shear force to 21.95 kN, which is 44% less than the 39 kN of walls without openings. In addition, for a single door in the wall, the shear force decreased to 17.9%, indicating that major shear forces in the wall are affected by the window opening due to its large size and proximity to the point of load application. Furthermore, the stresses were concentrated in the corners of the openings, subjecting the structure to failure during its in-service life and demanding the use of cross-diagonal timber members between the corners to redistribute corner stresses. It is recommended that these types of houses be adopted due to less slip deformation (less than 10 mm) caused by wind speed of 24 km/h. Full article

In the context of global climate warming, the issue of building energy consumption has become increasingly prominent, with a particular focus on energy management in educational buildings. This study investigates the impact of window usage behaviors in primary school classrooms on building energy consumption, aiming to reveal the dynamic relationship between window-opening behaviors and energy consumption, as well as to propose optimization strategies. A case study was conducted at a primary school, where data on door and window behaviors were collected using wireless smart sensors. Combined with indoor and outdoor environmental monitoring and CFD simulations, this study quantified the impact of window-opening behaviors on building energy consumption. The findings revealed that, in summer, window-opening behaviors exhibited a negative correlation with both indoor and outdoor temperature and humidity. Under high-temperature conditions, individuals tend to close windows to reduce heat entry. In contrast, winter window-opening behaviors showed a positive correlation with indoor and outdoor temperatures, although the probability of opening windows decreased once the temperature exceeded a certain threshold. This study also found that during the winter heating period, energy losses caused by opening external windows were substantial, with daily energy losses amounting to 12.83 kWh. Based on the PMV model, this study proposed an optimization strategy for opening specific windows during winter to maintain thermal comfort. This research provides a scientific basis for the energy-saving design of primary school buildings, helping to reduce energy waste while ensuring indoor comfort and promoting the development of low-carbon campuses. Full article

Modular construction is a modern and efficient type of construction that has gained wide recognition in the construction industry. Limited research has been conducted on how large door openings affect the stress state of modular blocks. The present study aims to investigate the features of the stressed state of modular blocks with large door openings and the effect of size and place of the doors on the openings on the overall structural behavior of the building. Four full-scale (room-sized) modular blocks of the “lying cup” type were tested to failure under vertical loading with eccentricity simulating wind effects. The varied parameters of the specimens included concrete strength and the size of the window openings. Experimental results revealed that crack opening characteristics, main load-bearing wall deformations, horizontal deflections, and failure patterns under vertical loads are directly influenced by the small thickness and increased flexibility of the blocks. The effects of size and the placement of openings on the overall structural behavior of the building were analyzed. Tests revealed the distribution of compressive stresses in the main load-bearing walls of the “lying cup” blocks with an embedded reinforced concrete panel, considering vertical load eccentricity. Maximum compressive stresses in the longitudinal walls reached 70–80% of concrete strength, while in end walls and panel walls, they were 50–60%. Additionally, non-uniform deformations were observed in the supports of main load-bearing walls near the conjunction with the end walls and the edges of door openings. Average compressive strains in these walls were in the range of 470–500 × 10⁻⁶, which corresponds to 22–29% of the cylindrical compressive strength of concrete. Partial factors accounting for loading conditions were introduced, allowing for further processing and the evaluation of the experimental data along with developing methods of analysis of buildings constructed with modular blocks. Full article