Visual Comfort Assessment Research Articles

Pseudo-global strategy-based visual comfort assessment considering attention mechanism

Pseudo-global strategy-based visual comfort assessment considering attention mechanism

A glare predictive control strategy for split-pane electrochromic windows: Visual comfort and energy-saving assessment

A glare predictive control strategy for split-pane electrochromic windows: Visual comfort and energy-saving assessment

Ensemble machine learning framework for daylight modelling of various building layouts

AbstractThe application of machine learning (ML) modelling in daylight prediction has been a promising approach for reliable and effective visual comfort assessment. Although many advancements have been made, no standardized ML modelling framework exists in daylight assessment. In this study, 625 different building layouts were generated to model useful daylight illuminance (UDI). Two state-of-the-art ML algorithms, eXtreme Gradient Boosting (XGBoost) and random forest (RF), were employed to analyze UDI in four categories: UDI-f (fell short), UDI-s (supplementary), UDI-a (autonomous), and UDI-e (exceeded). A feature (internal finish) was introduced to the framework to better reflect real-world representation. The results show that XGBoost models predict UDI with a maximum accuracy of R2 = 0.992. Compared to RF, the XGBoost ML models can significantly reduce prediction errors. Future research directions have been specified to advance the proposed framework by introducing new features and exploring new ML architectures to standardize ML applications in daylight prediction.

Probability-based visual comfort assessment and optimization in national fitness halls under sports behavior uncertainty

Sports behavior uncertainty is a main cause of mismatch between simulated and actual visual comfort in sports spaces, which has not been properly addressed in existing studies. This study proposes a multi-phase and multi-objective optimization approach to improve visual comfort in national fitness halls by better considering sports behavior uncertainty. Specifically, a surrogate-based multi-objective optimization workflow is developed for fast optimization of visual comfort. Then, a probabilistic behavior model is created to predict player's occupancy probability in all possible view scenes during sports, and based on which the probabilistic visual comfort is introduced to assess player's realistic visual comfort taking account of the occupancy probability. The sport of tennis in a parametric national fitness hall is selected as the case study scenario. Results indicated that the proposed response surface model outperformed artificial neural network model in predicting visual comfort, while this approach saved 35.43% of total computational time versus the traditional simulation-based multi-objective optimization approach in this case study; 296 Pareto solutions out of 1758 design alternatives were quickly identified, among which the final optimum solution indicated an approximately 200% better visual comfort against the worst one; Sports behavior uncertainty was found to affect player's visual comfort assessment in tennis sports, presenting variances on different court locations under two court layouts. This study contributes new insights of probabilistic visual comfort assessment under sports scenarios and explores the potential of court location and layout arrangement for multi-purpose usage consideration in national fitness halls.

An occupant-centric approach for spatio-temporal visual comfort assessment and optimization in daylit sports spaces

Most visual comfort analysis is based on glare detections in limited fixed view scenes, but sports spaces present challenges given occupants’ spatial and view directional variations in sports-related visual tasks. This paper proposes an occupant-centric approach for spatio-temporal visual comfort assessment and optimization (ST-VCAO) in daylit sports spaces by better considering the occupants’ movement behaviour. It introduces the movable view scenes (MVS) in glare simulations and further accelerates the ST-VCAO process using the GPU-based parallel simulation combining response surface methodology. The proposed approach was applied to a real case building in Harbin, China. Annual daylight and glare simulations in two tennis courts with a total of 500 MVS were performed using Perez all-weather sky model with Chinese standard weather data. Results indicate that the proposed approach could sufficiently clarify the spatial variance and temporal variation of visual comfort across all MVS, which is appropriate for sports spaces. Meanwhile, using the developed workflow, the total computational time could be saved by 27.08% (Case 1) or 71.25% (Case 2). Amongst all Pareto solutions, the optimum alternative upon the worst one observed 200% (Case 1) or 50.7% (Case 2) higher visual comfort.

An intelligent optimization method of exercisers' visual comfort assessment in gymnasium

Exercisers' visual comfort is an essential factor in successful gymnasium design. Existing research has identified viable indicators of visual comfort and the explained the interaction between humans and the light environment. However, it remains difficult to accurately quantify the impact of the daylight environment on human perception. Given the particularity of exercisers' behavior and activities in gymnasiums, the current general assessment model for exercisers' visual perception is lacking. Taking a university gymnasium in Harbin as a case, this study aimed to establish a computational method for assessing visual comfort from the human-centric perspective via mutual authentication between questionnaire and physiological indices and luminance. An analysis of the questionnaire responses revealed that the synthetical visual evaluation (SVE) was an appropriate visual evaluation index. Machine learning was applied to quantify the correlation between various luminance levels and human perception to assess exercisers' level of visual comfort. Multilayer Perceptron models with the best-fit optimization were selected by artificial neural networks (ANNs) to determine the most optimized visual comfort assessment model. Based on the ANNs, the correlation coefficient between luminance, SVE, and physiological indicator ranged from 85% to 90%. According to the genetic algorithm, the average luminance of the entire field of view (Lfov) was 55–135 cd/m2, the average luminance of the target area (Lt) was 82–375 cd/m2, and the average luminance of the window area (Lw) was 960–1950 cd/m2, for a comfortable visualization.

A Novel Dynamic Approach for Determining Real-Time Interior Visual Comfort Exploiting Machine Learning Techniques

The accurate assessment of visual comfort in indoor spaces is crucial for creating environments that enhance occupant well-being, productivity, and overall satisfaction. This paper presents a groundbreaking contribution to the field of visual comfort assessment in occupied buildings, addressing the existing research gap in methods for evaluating visual comfort once a building is in use while ensuring compliance with design specifications. The primary aim of this study was to introduce a pioneering approach for estimating visual comfort in indoor environments that is non-intrusive, practical, and can deliver accurate results without compromising accuracy. By incorporating mathematical visual comfort estimation into a regression model, the proposed method was evaluated and compared using real-life scenario. The experimental results demonstrated that the suggested model surpassed the mathematical model with an impressive performance improvement of 99%, requiring fewer computational resources and exhibiting a remarkable 95% faster processing time.

Visual Comfort Assessment of Hospital Patient Rooms with Climate Responsive Facades

As advanced technologies become prevalent, they are being used more widely in numerous fields. The building sector is not an exception. One of these cutting-edge technologies is responsive facades, which are used in buildings and have an undeniable effect on daylighting. However, they have not been adequately evaluated for improving visual comfort in hospitals. This study investigates visual comfort in a standard patient room, based on applying four responsive facades. Simulations were conducted using HoneybeePlus, a plugin in the Grasshopper. Simulation-based results of annual indicators, including Annual Sunlight Exposure (ASE) and spatial Daylight Autonomy (sDA), showed that different facades could result in several optimal modes. Furthermore, a more comprehensive investigation should consider factors such as Daylight Glare Probability (DGP) and Daylight Glare Index (DGI). Glare indicators revealed that facade directly affects patient visual comfort and can even have an adverse effect. When the optimal responsive facade is chosen, it enhances users' visual comfort throughout the year, yet there will be still glare probability in some cases. Based on the results, this probability decreases as patient distance increases, and Window to Wall Ratio (WWR) is not particularly effective in reducing glare. Nevertheless, when it comes to daylight availability, WWR cannot be ignored, and the first façade with WWR 60% showed the best overall performance.

The Impact of Evaluated Daylight to the Total Light Ratio on the Comfort Level in Office Buildings

One of the main challenges in visual comfort assessment is controlling daylight in indoor spaces. The effect of daylight’s contribution to total light is one of the variables influencing how people perceive illumination in an indoor environment. This study investigates the optimal day-light-to-total light ratio that delivers the most satisfaction with the lighting environment. Therefore, a subjective survey of 509 questionnaires and field measurements in six office buildings in Tehran with a total of 257 rooms was conducted to assess lighting quality (daylight and artificial light). Furthermore, the effects of building characteristics and seasons on the acceptable range of daylight ratio are investigated. The results reveal that occupants prefer daylight to total light ratio ranging between 0.56 and 0.8. In contrast, occupants reported that a ratio less than 0.4 was unacceptable. It was also found that the optimum daylight-to-total light ratio is influenced by the season and the building characteristics.

Sporters' visual comfort assessment in gymnasium based on subjective evaluation & objective physiological response

Visual comfort is affected by the light environment of interior spaces. However, relatively few studies have combined subjective and objective indicators of visual comfort in the assessment of indoor sport facilities. Thus, the purpose of this was to determine the factors influencing visual comfort and establish corresponding thresholds by combining subjective evaluation, objective physiological indicators, and photometric light environment assessment. The experiment was conducted at a university gymnasium in Harbin, located in the northeast of China, during summer, autumn, and winter. The visual comfort of the participants was assessed via subjective evaluation via questionnaires, and objective physiological indicators were recorded using biosensors. Additionally, high-dynamic-range images were used to assess the light environment. Subjective evaluation revealed that visual comfort was affected by the glare intensity, target clarity, and emotional state. Objective physiological indicators showed that emotional arousal and alertness were higher in autumn and winter than in summer. Furthermore, the physiological indicators and subjective evaluation of visual comfort were highly consistent with each other. In summer, acceptable ranges of the luminance ratio of the window area to the field of view were less than 5 and greater than 10, and comfortable ranges were less than 3 and greater than 12. In winter, the upper acceptable and comfortable thresholds were 42 and 13, respectively. This study provides a reference point for optimizing gymnasium light environment design.

MACHINE LEARNING MODEL FOR GLARE PREDICTION IN OFFICES WITH SIMPLE ARCHITECTURAL FEATURES

ABSTRACT Daylight glare index (DGI), daylight glare probability (DGP) and glare-sensation (GS) predictive models are the widely used glare indices for the assessment of occupant visual comfort in daylit spaces. This paper presents the development and implementation of Machine Learning models to predict these glare indices. The training and validation data sets were collected from sensors incorporated in the test room with motorized Venetian Blinds and dimmable LED luminaires. Predictor and response data were obtained from conventional sensors, digital cameras, and the EVALGLARE Software. The regression models predict DGI and DGP, whereas the classification model predicts GS. In addition to standard statistical error evaluation metrics, the hypothesis test assesses the performance of regression/classification models. The results reveal that Ensemble Tree (ET) models are highly accurate at predicting glare indices. The proposed technique attempts to simplify the existing traditional Glare Index(GI) estimation method. The combination of real-time daylight glare prediction and suitable window shading control increases occupant visual comfort. A high dynamic image-based system is employed to verify the measurements made using traditional sensors.

Pupil diameter variation related to visual comfort of hue‐asymmetric stereoscopic images

AbstractColor asymmetry of the left and right views is a common phenomenon in the stereoscopic three‐dimensional (S3D) display, which can lead to visual discomfort. Due to visual discomfort is a subjective sensation, we hypothesize that variations of pupil diameter while viewing S3D images are related to experienced visual comfort. To test this hypothesis, we conducted eye‐tracking experiments on humans viewing hue‐asymmetric stereoscopic images while simultaneously collecting their judgment scores of experienced visual discomfort. From the collected eye‐tracking data, pupil diameter variations were extracted by a normalization formula. Results show that change in hue asymmetry levels causes a significant change in pupil diameter variations. There was a strong negative correlation between the pupil diameter variation and the visual comfort assessment (VCA) score, and the Pearson's r = −0.8936. We conclude that as the hue asymmetric level of stereoscopic image increases, the pupil diameter of the participant becomes smaller and the visual comfort decreases. A visual comfort predication model was fitted well using pupil diameter variation. It predicts that when the pupil diameter variation exceeds 20.57%, the brain feels visual discomfort.

Assessment of visual comfort in the lighting environments of subway cabins in China

ABSTRACT This paper presents a multi-dimensional evaluation and comparison of the visual comfort of the cabin lighting environments of five subways lines in Chengdu. The study proposes a visual comfort evaluation procedure that integrates objective and subjective methods combined with existing visual comfort measurement methods. Based on this evaluation procedure, the lighting conditions (illuminance and glare) in each cabin were measured using an illuminance metre and high-dynamic image technology, and an online questionnaire was used to understand passengers’ subjective experiences of cabin lighting. The results show that: seating layout has no significant influence on the visual performance of mobile phones or on spatial brightness; horizontal illuminance in the lighting environments of subway cabins has no significant influence on task performance of passengers using mobile phones; the overall perception of subway cabin spatial brightness increases with vertical illuminance; and passengers have no obvious preference for a cabin correlated colour temperature.

Visual comfort assessment of OLED lighting in an indoor office environment

Visual comfort is one of the significant criteria that influence good-quality lighting in an indoor environment. There are various technologies for delivering illumination in indoor installations; three significant milestones are incandescent, fluorescent, and LED lighting. Alternative technologies, such as OLED (organic light-emitting diode) lighting, cannot be disregarded by a thorough examination. Few studies have investigated the influence of OLED lighting on visual comfort in terms of merely illuminance level. To this end, this study investigates the influence of OLED lighting conditions on visual comfort, including general comfort parameters—overall comfort, illuminance, brightness ratio, veiling reflections, colour, flicker effect, overall satisfaction—in an indoor office environment. 12 members from the Faculty of Architecture, Çankaya University, voluntarily participated in the study. A full-scale indoor office environment was designed for the test environment, which was illuminated by OLED lighting (2900 K). An Office Lighting Survey was adopted to assess the general visual comfort of OLED lighting conditions in an indoor office environment. In contrast to earlier lighting studies, this study did not measure any task performance under the lighting condition and instead focused solely on participants’ visual comfort evaluations. At the same time, they were exposed to OLED lighting during their everyday routines in an indoor office environment.

Current Trajectories and New Challenges for Visual Comfort Assessment in Building Design and Operation: A Critical Review

Visual comfort can affect building occupants’ behaviour, productivity and health. It is highly dependent on the occupant and how they perform a task indoors. In that regard, an occupant centred approach is more suitable for evaluating the lighting perception of the indoor environment. Nevertheless, the process of rating and estimating the visual comfort makes a limited distinction between physiological differences (e.g., ageing eye, light sensitivity), field of view, and personal preferences, which have been proven to influence the occupants’ lighting needs to complete their tasks. Such features were not considered while establishing the visually comfortable conditions; perhaps due to the challenge of coupling the assumptions made during building design to the performance indicators monitored during building operation. This work focuses on reviewing literature findings on how the common design approach deviates from real building performance, particularly failing to prevent visual disturbances that can trigger the inefficient operation of building systems. Additionally, it is highlighted that redesigned visual comfort assessment methods and metrics are required to bridge the gap between the lighting environment ratings computed and surveyed. One possibility is to consider such physiological features that induce lighting experiences. Finally, it was deducted that it is important to target the occupants’ eye response to calibrate limit thresholds, propose occupant profiling, and that it is convenient to continuously monitor the occupants’ perception of indoor lighting conditions.

Development of a Machine-Learning Framework for Overall Daylight and Visual Comfort Assessment in Early Design Stages

Application of machine learning methods as an alternative for building simulation software has been progressive in recent years. This research is mainly focused on the assessment of machine learning algorithms in prediction of daylight and visual comfort metrics in the early design stages and providing a framework for the required analyses. A dataset was primarily derived from 2880 simulations developed from Honeybee for Grasshopper. The simulations were conducted for a side-lit shoebox model. The alternatives emerged from different physical features, including room dimensions, interior surfaces’ reflectance factor, window dimensions, room orientations, number of windows, and shading states. Five metrics were applied for daylight evaluations, including useful daylight illuminance, spatial daylight autonomy, mean daylight autonomy, annual sunlit exposure, and spatial visual discomfort. Moreover, view quality was analyzed via a grasshopper-based algorithm, developed from the LEED v4 evaluation framework. The dataset was further analyzed with an artificial neural network algorithm. The proposed predictive model had an architecture with a single hidden layer consisting of 40 neurons. The predictive model learns through a trial and error method with the aid of loss functions of mean absolute error and mean square error. The model was further analyzed with a new set of data for the validation process. The accuracy of the predictions was estimated at 97% on average. The View range metric in the quality view assessment, mean daylight autonomy and useful daylight illuminance had the best prediction accuracy among others respectively. The developed model which is presented as a framework could be used in early design stage analyses without the requirement of time-consuming simulations.

Subjective assessment of visual comfort in a daylit workplace with an electrochromic glazed façade

Electrochromic (EC) glazing is increasingly employed in building façades to achieve better visual comfort for the occupants. EC glazing can modulate the light entering through the façade by varying the solar transmittance of the glass and therefore can work as a shading strategy to minimize solar heat gains or glare. However, it also alters the spectrum and distribution of light entering through the façade, which influences certain visual attributes associated with a space. This user assessment study evaluates some of these attributes including the colour perception of the elements inside and outside the room, the uniformity of daylight distribution, the clarity of the view through the glazing and the perception of glare when the sun is in the field of view. Results indicate a visual transmittance (τv) of 0.6% is sufficient to control glare when the sun is in the peripheral field of view (FOV) while τv,n-n of 0.14% is required to control glare when the sun is close to the central FOV. Most of the participants did not perceive the colours of outdoor environment as natural when seen through EC glazed window. The majority of participants also desired to change the glazing configuration by adding an additional shading device.

Management and Enhancement of Livable Urban Streetscape through the Development of the 3D Spatial Multimedia System

Maintaining the continuity of a valuable and livable visual experience is very important for the quality of urban streetscape planning and design. One of the impacts of the rapid development of urban streetscape is unsatisfying pedestrian space for spatial and visual comfort. In line with establishing good governance, it is necessary to evaluate the quality of urban visuals by involving community participation through online participation. The case study on the five provincial streets In Malang, Indonesia, has become a strategic route for the uncontrolled rapid growth of commercial districts alongside these streetscapes. This study promoted spatial multimedia development as a decision support system in the planning process as one of the recent developments in design planning activity by developing a multimedia 3D spatial system based on public preferences in the local community. A combination of three sequential stages was conducted, starting with investigating public preferences that produced spatial and visual comfort assessment through the Semantic Differential method and regression model analysis, followed by the 3D streetscape modeling ends with the development of a Spatial Multimedia System. This study resulted in several findings. The investigation of public preferences determined dominant variables and significant variables that affect the streetscape's spatial and visual comfort aspects. This outcome guided the development scenario of 3D modeling construction and 3D simulation of each streetscape and the development of a decision-making system in the 3D spatial multimedia system. This finding also revealed the significance of the leveling strategy of user interactive advancement in the system.

Towards nearly zero energy building concept – visual comfort and energy efficiency assessments in a classroom

PurposeThis paper aims to raise awareness on how a simple action by the occupant can significantly influence building energy efficiency, cost and CO2 emissions to the environment. Classrooms in schools are the primary energy consumers (45.4%) due to the use of artificial lighting, despite Malaysia's tropical climate being ideal for daylight exploitation. This paper focuses on assessing the workplane daylight distribution quality and quantity in baseline and existing conditions of a typical pre-school classroom in Kuala Lumpur as a model-based exploration strategy towards nearly Zero Energy Buildings.Design/methodology/approachThe adopted method is based on the calculation of average daylight factor (DF), daylight illuminance level (IL) and uniformity ratio (UR) parameters affected by the internal fixed drapes through computational and in situ measurements according to the requirements of the law and respective standards comprising the MS1525:2019, GBI and BREEAM.FindingsThe results show how user behaviour can turn a well-daylit area (Net Lettable Area>90%) into a poor-daylit area (NLA<5%) by sacrificing natural daylight. All the parameters' values were significantly decreased from 10% (UR) up to 88% (ADF). Full dependency on artificial lighting has imposed a total of RM18858.90 and CO2 emissions of 25,362 kg for all pre-schools' classrooms in the country per day.Social implicationsThe paper develops the occupants' awareness on their contribution to climate change and global warming through the information and transparency provided.Originality/valueThe evidence indicates that a simple action by the occupant can significantly influence visual comfort, EE, cost and CO2 emissions to the environment.

Visual Comfort Aware-Reinforcement Learning for Depth Adjustment of Stereoscopic 3D Images

Depth adjustment aims to enhance the visual experience of stereoscopic 3D (S3D) images, which accompanied with improving visual comfort and depth perception. For a human expert, the depth adjustment procedure is a sequence of iterative decision making. The human expert iteratively adjusted the depth until he is satisfied with the both levels of visual comfort and the perceived depth. In this work, we present a novel deep reinforcement learning (DRL)-based approach for depth adjustment named VCA-RL (Visual Comfort Aware Reinforcement Learning) to explicitly model human sequential decision making in depth editing operations. We formulate the depth adjustment process as a Markov decision process where actions are defined as camera movement operations to control the distance between the left and right cameras. Our agent is trained based on the guidance of an objective visual comfort assessment metric to learn the optimal sequence of camera movement actions in terms of perceptual aspects in stereoscopic viewing. With extensive experiments and user studies, we show the effectiveness of our VCA-RL model on three different S3D databases.