Daylight Illumination Levels, User Preferences, and Cognitive Performance in Office Environments: Exploring an Optimal Illumination Range Using Virtual Reality DOI
Pegah Payedar-Ardakani, Yousef Gorji Mahlabani, Abdulhamid Ghanbaran

и другие.

Опубликована: Янв. 1, 2023

This study investigates the impact of varied daylight illumination levels on user preferences and cognitive performance in offices, employing a virtual reality platform HDRI 360-degree panorama images, whose level was validated using simulation. With 46 participants, task known as Stroop-test conducted under nine illuminance (66 to 1395 lux). Additionally, participants were surveyed determine their most preffered horizontal at desk height. The results uncovered distinct preferences, with majority favoring above 700 lux, specifically 1100 790 for reading work-related tasks. Notably, none opted below 300 indicating these deemed insufficient An analysis revealed significant differences between various levels. Generally, increasing an office building will increase workers’ performance. As exceeded exhibited enhanced tasks such words (RW), naming colors (NC), total (TT). 1500 lux can be considered suitable high-precision tasks, medium environments. Based findings, optimal range 900 is recommended environments, aligning both offers valuable insights architects researchers development daylighting design guidelines aimed enhancing employees' capabilities overall satisfaction.

Язык: Английский

Forecasting the strength of graphene nanoparticles-reinforced cementitious composites using ensemble learning algorithms DOI Creative Commons
Majid Khan, Roz‐Ud‐Din Nassar,

Waqar Anwar

и другие.

Results in Engineering, Год журнала: 2024, Номер 21, С. 101837 - 101837

Опубликована: Фев. 6, 2024

Contemporary infrastructure requires structural elements with enhanced mechanical strength and durability. Integrating nanomaterials into concrete is a promising solution to improve However, the intricacies of such nanoscale cementitious composites are highly complex. Traditional regression models encounter limitations in capturing these intricate compositions provide accurate reliable estimations. This study focuses on developing robust prediction for compressive (CS) graphene nanoparticle-reinforced (GrNCC) through machine learning (ML) algorithms. Three ML models, bagging regressor (BR), decision tree (DT), AdaBoost (AR), were employed predict CS based comprehensive dataset 172 experimental values. Seven input parameters, including graphite nanoparticle (GrN) diameter, water-to-cement ratio (wc), GrN content (GC), ultrasonication (US), sand (SC), curing age (CA), thickness (GT), considered. The trained 70 % data, remaining 30 data was used testing models. Statistical metrics as mean absolute error (MAE), root square (RMSE) correlation coefficient (R) assess predictive accuracy DT AR demonstrated exceptional accuracy, yielding high coefficients 0.983 0.979 training, 0.873 0.822 testing, respectively. Shapley Additive exPlanation (SHAP) analysis highlighted influential role positively impacting CS, while an increased (w/c) negatively affected CS. showcases efficacy techniques accurately predicting nanoparticle-modified concrete, offering swift cost-effective approach assessing nanomaterial impact reducing reliance time-consuming expensive experiments.

Язык: Английский

Процитировано

29
Investigating effects of indoor temperature and lighting on university students’ learning performance considering sensation, comfort, and physiological responses DOI

Surakshya Pradhan,

Youjin Jang, Hardik Chauhan

и другие.

Building and Environment, Год журнала: 2024, Номер 253, С. 111346 - 111346

Опубликована: Фев. 22, 2024

Язык: Английский

Процитировано

17
Indoor environmental quality (IEQ) in healthcare facilities: A systematic literature review and gap analysis DOI Creative Commons

Aniebietabasi Ackley,

Oludolapo Ibrahim Olanrewaju, Oluwatobi Nurudeen Oyefusi

и другие.

Journal of Building Engineering, Год журнала: 2024, Номер 86, С. 108787 - 108787

Опубликована: Фев. 15, 2024

Numerous studies have examined the connection between indoor environmental quality (IEQ) and health in various healthcare settings. However, it remains uncertain whether these findings are consistent across a wide array of environments for diverse IEQ elements such as daylighting, thermal comfort, acoustics, air quality. As result, this study aims to holistically assess impact on facilities with focus patient staff outcomes identify gaps knowledge within domain. The applied qualitative research approach, including systematic literature review from last three decades, covering four major databases (PubMed, Scopus, ScienceDirect, Web Science). collective body consistently demonstrates that favourable positively impacts recovery, reduces stress levels, shortens hospital stays, enhances effectiveness care delivery. Nevertheless, notable gap exists concerning combined effects healing outcomes, particularly purpose-built non-purpose-built facilities. To bridge gap, we propose adopting an evidence-based design approach understand relationship hospital's environment well-being both patients staff, specific architectural considerations. also proposes conceptual framework helps dynamics offer valuable insights researchers, policymakers, professionals building design, facilitating enhancement guidelines standards tailored

Язык: Английский

Процитировано

14
Application of metaheuristic algorithms for compressive strength prediction of steel fiber reinforced concrete exposed to high temperatures DOI

Muhammad Faisal Javed,

Majid Khan, Moncef L. Nehdi

и другие.

Materials Today Communications, Год журнала: 2024, Номер 39, С. 108832 - 108832

Опубликована: Апрель 6, 2024

Язык: Английский

Процитировано

12
Daylight illuminance levels, user preferences, and cognitive performance in office environments: Exploring an optimal illuminance range using virtual reality DOI
Pegah Payedar-Ardakani, Yousef Gorji Mahlabani, Abdulhamid Ghanbaran

и другие.

Building and Environment, Год журнала: 2024, Номер 258, С. 111638 - 111638

Опубликована: Май 13, 2024

Язык: Английский

Процитировано

10
Predictive modeling for durability characteristics of blended cement concrete utilizing machine learning algorithms DOI Creative Commons
Bo Fu,

Hua Lei,

Irfan Ullah

и другие.

Case Studies in Construction Materials, Год журнала: 2025, Номер unknown, С. e04209 - e04209

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

2
Lighting and thermal factors on human comfort, work performance, and sick building syndrome in the underground building environment DOI Open Access
Xinyue Hu, Nianping Li, Jiayuan Gu

и другие.

Journal of Building Engineering, Год журнала: 2023, Номер 79, С. 107878 - 107878

Опубликована: Окт. 6, 2023

Язык: Английский

Процитировано

22
Recent advancements of human-centered design in building engineering: A comprehensive review DOI
Y.Z. Zhang, Junyu Chen, Hexu Liu

и другие.

Journal of Building Engineering, Год журнала: 2024, Номер 84, С. 108529 - 108529

Опубликована: Янв. 13, 2024

Язык: Английский

Процитировано

7
Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approaches DOI Creative Commons
Muhammad Fawad, Hisham Alabduljabbar, Furqan Farooq

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июнь 20, 2024

Abstract Graphene nanoplatelets (GrNs) emerge as promising conductive fillers to significantly enhance the electrical conductivity and strength of cementitious composites, contributing development highly efficient composites advancement non-destructive structural health monitoring techniques. However, complexities involved in these nanoscale are markedly intricate. Conventional regression models encounter limitations fully understanding intricate compositions. Thus, current study employed four machine learning (ML) methods such decision tree (DT), categorical boosting (CatBoost), adaptive neuro-fuzzy inference system (ANFIS), light gradient (LightGBM) establish strong prediction for compressive (CS) graphene nanoplatelets-based materials. An extensive dataset containing 172 data points was gathered from published literature model development. The majority portion (70%) database utilized training while 30% used validating efficacy on unseen data. Different metrics were assess performance established ML models. In addition, SHapley Additve explanation (SHAP) interpretability. DT, CatBoost, LightGBM, ANFIS exhibited excellent with R-values 0.8708, 0.9999, 0.9043, 0.8662, respectively. While all suggested demonstrated acceptable accuracy predicting strength, CatBoost exceptional efficiency. Furthermore, SHAP analysis provided that thickness GrN plays a pivotal role GrNCC, influencing CS consequently exhibiting highest value + 9.39. diameter GrN, curing age, w/c ratio also prominent features estimating This research underscores accurately forecasting characteristics concrete reinforced nanoplatelets, providing swift economical substitute laborious experimental procedures. It is improve generalization study, more inputs increased datasets should be considered future studies.

Язык: Английский

Процитировано

7
The effect of classroom size and ceiling height on college students’ learning performance using virtual reality technology DOI Creative Commons
Yalin Zhang, Chao Liu, Jiaxin Li

и другие.

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Июль 3, 2024

The physical characteristics of classrooms can significantly impact the and mental health as well learning performance college students. This study investigates effects classroom size ceiling height on using virtual reality technology. Four settings were created: two small (40.5 m

Язык: Английский

Процитировано

7