Exploring Heritage: An In-Depth Performance Evaluation of Kenadsa’s Office Building through User Perceptions and Behaviors

Abstract

This research focuses on the evaluation of a heritage office building in the town of Kenadsa in the Southwest of Algeria (a famous oasis in the arid regions of the country). Emphasizing user’s perception as a metric key for performance assessment, this study aims to investigate the users’ perception and behaviors of a heritage office building on an oasis settlement. The research was conducted in 2023, and employing a multidimensional approach, both quantitative and qualitative methodologies, along with agent-based modeling, were integrated. One qualitative methodology is based on a series of field surveys and the other quantitative methodology relies on in situ measurements of the physical dimensions of the environment. The quantitative aspect involves an agent-based modeling framework, simulating user interactions by incorporating physical dimensions, spatial layout, historical context, and user behavior. The main findings of this study, examining perceptions and behaviors under varying luminous and thermal environments, show that this integrated approach provides insights into user satisfaction, spatial utilization, and the identification of user behaviors and productivity in each office.

1. Introduction

Oasis settlements, often synonymous with resilience in the face of arid conditions, hold a distinctive position in the vast deserts and semi-desert regions [1]. Traditionally envisioned as lush landscapes adorned with water features and palm trees against the backdrop of sandy deserts [2], oases represent a harmonious intersection of human habitation and cultural identity within challenging environments. However, despite their significance, there exists a noticeable void in the architectural literature, particularly within the African context, with a glaring gap in research focused on North African oases [3] characterized by arid climates, cold winters, and scorching summers [4]. This is particularly true for the Saharan region of Algeria, where semi-arid conditions prevail, characterized by clear skies and abundant sunlight throughout the year [5,6]. While these oasis regions in the African Sahara, notably in Algeria, are undergoing rapid urbanization, the expansion is concentrated within the verdant palm groves rather than the surrounding desert expanse. This urbanization strategy poses environmental challenges, including the imminent impact of climate change, which threatens to render these settlements excessively hot and uninhabitable in the coming decades. Furthermore, the unchecked urbanization approach risks compromising the serene and tranquil essence that has defined Algerian oases [7]. Aligning with the United Nations Sustainability Goal 11, which aspires to create inclusive, safe, resilient, and sustainable human settlements, this study recognizes the crucial role of sensory perception in shaping the environment. Against the backdrop of these challenges, there is an imperative to explore the built environments of these oases, delving into both architectural and urban spaces. This investigation gains significance as comprehending the intricate interplay between human perception and the physical oasis environment remains a challenge, further compounded by the dearth of the scholarly literature on the subject [8,9,10].

The physical environment of a building is intricate, comprising numerous elements that collectively shape its ambiance. This ambiance encompasses various sensory perceptions “visual, thermal, auditory, and olfactory” that individuals experience within the space at any given moment [11]. From this perspective, the perception of space emerges as a multifaceted process, wherein individuals navigate their behavior within the environment, guided by sensory stimuli [12]. The collective multisensory interplay among these stimuli and its impact on users’ perception and utilization of space remains inadequately understood, particularly concerning individuals working within these architectural environments [13]. This presents a critical concern if we aim to comprehend how occupants in architectural settings might respond to the myriad sensory inputs predominantly influenced by the physical surroundings. Over the past three decades, researchers from diverse disciplines have increasingly focused on ambiances shaped by sensory experiences, underscoring the significance of this area of inquiry. This growing focus underscores the significance of comprehending the impact of sensory experiences on individuals working within architectural settings [14]. As users engage in their professional activities within the built environment of oasis settlements in North Africa, the interplay between their work-related tasks and the surrounding sensory stimuli becomes a pivotal aspect in shaping their perceptions and experiences [15], including those interested in building space evaluation based on users’ perceptions and behaviors. In a notable effort, studies have endeavored to investigate the sensory experiences of users within office environments, particularly focusing on indoor air quality and comfort [16,17], while other research aims to conduct an environmental quality assessment of office buildings and study the impact of glass façade on internal and external users [18,19]. On the other hand, few studies have focused on the perception evaluation approach in oasis environments through the user perception experience in indoor spaces [20]. Moreover, no research has yet addressed the issue of the performance building evaluation based on the perception and behaviors of users working in indoor heritage office buildings.

In the context of the Saoura region (an Oasis in Berber) of Bechar, more precisely in Kenadsa city, which is considered one of the most famous Oases in Algeria [21,22], Kenadsa’s administrative heritage office building is considered one of the most important institutions that participate in improving the institutional services for its inhabitants in this Saharan city. Therefore, our study focuses on evaluating Kenadsa’s heritage office building, and the main objectives of this study are:

• To evaluate the physical and perceptual dimensions of the indoor space, thermal and luminous environment of Kenadsa’s heritage office building located in the oasis of Bechar.
• To evaluate our case study offices’ performance based on the perception and behaviors of their users and to review if there are correlations between the physical dimensions
• Agent-based modeling simulation as a method of evaluation, in order to confirm our objective and subjective approaches.

2. Methodological Approach

As energy efficiency studies increasingly prioritize occupants, researchers have underscored the significance of comprehending the underlying motivations driving occupant behaviors from a behavioral science standpoint, advocating for its integration with technical aspects [23,24,25,26,27,28,29]. Consequently, there is a burgeoning body of research dedicated to exploring how various behavioral theories (e.g., psychological, social, logical, and economic) elucidate occupants’ behaviors and their interactions with diverse building systems [30]. To gain insights into the current landscape of this field, this study conducted a comprehensive review of the existing research, aiming to evaluate the applicability of behavioral theories in understanding the relationships between users’ perceptions and behaviors, building systems, and energy consumption. Notably, this paper represents the first attempt to explicitly analyze the correlations between behavioral and perceptual dimensions and their interaction with physical ones, employing agent-based modeling simulation as an evaluation method, specifically focusing on a heritage office building.

In order to evaluate and analyze the multisensory perception and behavior of office users in their offices of the heritage administrative building of Kenadsa which is located in the Kenadsa oasis settlement, the first study is based on an objective approach using several in situ measurements of the physical dimensions of the indoor environment in different offices, recorded during different users work. The second is based on a subjective approach, based on in situ questionnaire investigations carried out after each user’s experience. In this study, two surveys are conducted in the laboratory, carried out in two stages subsequent to the completion of several short-term, in situ assessments regarding the temperature and lighting that are used in winter and summer in different offices. We investigated the user’s perception at first and then their behaviors as a result of their perception, investigating the relationship between them using an agent-based modeling method [31,32,33].

Figure 1 illustrates the systematic process employed in this research endeavor. The initial phase involves an exhaustive data collection effort focusing on three important work packages. First, one was about the architectural heritage office building in Kenadsa (Figure 2). The goal was to gather comprehensive information on various facets, including the constructive system and materials employed in its construction, the building’s form and layout, orientation, and the specific functions of each office within. Mobility patterns between offices and the overall number of users were also scrutinized. This data retrieval process is vital for initiating the subsequent fieldwork and experimentation. Given the unique nature of the heritage building, the bulk of the information was sourced from archival sections dedicated to heritage buildings, the relevant literature, and historical communications. The richness of these archives provided valuable insights into the distinctive features of the building [34]. This first set of work served as a crucial preparatory step and a more detailed basis of investigation in the later stages of the research.

The second work package of data collection focuses on the quantitative aspects of data collection, specifically in the realm of the multisensory environment within the selected case study of Kenadsa’s architectural heritage office building. The critical factors under scrutiny included thermal comfort, indoor air quality, luminous comfort, and daylighting. This phase employs a meticulous approach to gather precise measurements, employing red circles to denote measurement stations strategically positioned within the building.

The third work package of the research focuses on qualitative subjective data collection, specifically targeting users of the heritage office building in Kenadsa. This qualitative survey aims to delve into the intricate relationship between users’ perceptions and subsequent behaviors within the multisensory environment of the building [35].

In the next phase of our research, we utilize ArchiCAD software 22 to create a detailed two-dimensional floor plan for the case study [6]. This floor plan is then refined and subdivided into thermal and lighting zones through the application of the Climate Studio Grasshopper plug-in [36]. Subsequently, the volumetric representation of the building is exported to the Rhinoceros 7 software, facilitating a seamless transition into three-dimensional modeling. One notable aspect of our modeling process is the consistency in internal input scenarios. This uniformity is maintained across both the Quelia Agents Grasshopper plug-in for agent-based modeling and the Climate Studio plug-in for thermal and lighting medializations. The occupancy scenario is standardized, assuming a two- to four-person occupancy for each office and meeting room. Key parameters, including infiltration rate, occupants’ metabolic energy, and thermal resistance of clothing, were fixed at specific values (0.8 vol/h, 1.2 met, and 1.5 clo, respectively) [37]. To enhance the accuracy of our simulations, we considered the scenario of window closure to mitigate the impact of air currents on thermal dynamics. Internal gains from equipment, such as computers, are also factored into the simulations. Lighting power is adjusted to simulate the presence of one to two lamps, contributing to a more realistic representation of the building’s lighting conditions.

2.1. Stage One: Physical Context and Case Study

This research took place in Kenadsa, a town located 20 km from the main town of Bechar. An Algerian city in western Algeria, it serves as a real border zone between the north and the great south [38]. Kenadsa inherited colonial urbanization, which since French colonization in Algeria, has given birth to a sufficient number of public or administrative buildings, including one of the most important, “the office building”, made by the architect Charles Henri Montaland in 1941 [34], which will be our case study. As we moved forward with our research (Figure 3), we planned to explore various aspects related to the architecture and history of the administrative building.

In the construction of the specified building, a comprehensive integration of building materials was employed (see

Table 1. Envelope materials and their thermal characteristics [6].

Building Element	Material Used	Thermal Conductivity (W/mK)	Thickness (m)
Exterior Walls	Cement plaster	1.4	0.025
	Hollow brick	0.5	0.15
	Air blade	0.31	0.5
	Hollow brick	0.5	0.15
	Gypsum plaster	0.35	0.025
Interior Walls	Gypsum plaster	0.35	0.025
	Hollow brick	0.5	0.1
	Gypsum plaster	0.35	0.025
Low and intermediate Floors	Gypsum plaster	0.35	0.025
	Solid slab	1.45	0.25
	Mortar	1.4	0.5
	Flooring	2.1	0.8
Terrace floor	Gypsum plaster	0.35	0.025
	Hollow body + compression slab	1.45	0.25
	Isolation	0.1	0.05
	Slope shape	1.15	0.05
	Water tightness	0.04	0.03

) [6]. External walls are designed as double walls using hollow bricks, consisting of a 15 cm layer towards the outside and another 15 cm layer towards the inside, separated by a 5 cm air gap. The external side is coated with cement plaster, while the internal side receives a 2.5 cm plaster layer. Interior walls are constructed using a single brick wall, employing 15 cm thick brick walls. Ceilings and floors are implemented as hollow slabs. Weather data from the selected city, Bechar, specifically Kenadsa, were integrated into the construction process, utilizing information exported from the Climate Studio location plug-in. Furthermore, a statistical study is conducted using SPSS software 25, emphasizing a comprehensive analysis of data, possibly related to the building’s performance or environmental impact.

Figure 4 illustrates the monthly distribution of air velocity, temperature, and relative humidity for the city. These data reveal that for the majority of the year, conditions fall outside the comfort zone, with exceptions during certain periods in September, October, March, and April. Another period, spanning November to February, typically necessitates heating to maintain occupant comfort. From May to September, there is a distinct hot season characterized by high insolation, exceeding 3500 h per year, and direct solar radiation peaking at 800 W/m² on horizontal surfaces. During summer, temperatures in the shade surpass 40 °C, with a day–night temperature amplitude of approximately 15 °C, while relative humidity remains low at around 27% [39,40].

2.2. Stage Two: Case Study Analysis

Following the nature of the heritage office building, two indoor spaces are considered in this research: the user’s office and public spaces in the building where they receive public clients. The two spaces that have been studied in the Kenadsa’s heritage office building are presented in (Figure 5b). It shows that the public space starts with an open space on two sides (yellow), followed by a long corridor on both sides, and then another space with a reception for public use on one side. The private director’s office (green), located between access and semi-private route, and considered part of the gallery space, passes through the quarter circle shape formed by two juxtaposed administrative staff offices and very close office users (red). The whole building morphology is also in a quarter circle shape but with a slightly greater distance between offices than in the first shape. The transition space between offices represents a covered passage that leads to a large reception hall that gives access to the public, which is located in the middle of the building, creating a symmetry relationship. The adaptive reuse of the heritage office building is divided into three important areas (a municipal treasury area, a tax revenue area and the administrative building area). It has two semi-private access routes from one side. Including the sanitary areas, there are two semi-private areas and a private one. It is interesting that the building has four elevations and one public access in the middle. The form of the building was originally created because of the shape of the opposite roundabout transition (Figure 5a). We notice that it has the same shape as the opposite building (the Museum of Kenadsa), and it was designed by the same architect, which has a welcoming and positive effect on visitors.

2.3. Stage Three: Objective Approach and Materials Used

The multisensory approach adopted in this data collection phase underscores the commitment to comprehensively understanding the environmental factors that contribute to user comfort and well-being. By employing a quantitative methodology, the research aims to derive precise measurements that serve as foundational data for subsequent analyses and simulations. This data-driven approach is pivotal in informing the broader objectives of optimizing energy consumption and fostering environmentally friendly behaviors within the heritage office building “our case study”. As the red circles denote the measurement stations, they symbolize the strategic points where data are gathered to paint a detailed picture of the building’s environmental characteristics.

In assessing the physical dimensions of the luminous environment at various indoor stations, the study employs in situ measurements of luminance, a crucial parameter in evaluating the luminous environment. These measurements are conducted using a “luxmeter kit” compatible with the “Testo 480 multifunctional meter” [41]. Additionally, the multifunction meter (Testo 480) is utilized to gauge the thermal environment dimensions, encompassing ambient air temperature (Ta) and relative humidity (RH). The purpose of these physical measurements is to capture the current conditions within the building, shedding light on the actual state of the environment. The objective is to explore potential correlations between perceptual aspects (behaviors) and the measured physical dimensions, providing valuable insights into the interplay between users’ experiences and the tangible characteristics of the indoor environment (Figure 6).

2.4. Stage Four: Subjective Approach and Used Questionnaire

The second study adopts a subjective approach, focusing on the thermo-visual experiences of individuals to assess the multisensory perceptual dimensions within each indoor environment of the investigated heritage office building and to extract the behaviors of each user and office. To accomplish the objectives of this phase, two essential tasks are outlined: Firstly, the research necessitated a comprehensive data collection process to inform the creation of the surveys. This involved gathering diverse indicators to construct surveys that capture the nuanced aspects of users’ experiences within the case study. These indicators encompass elements related to environmental comfort, historical significance, and energy consumption.

The second task involves a meticulous study of the survey’s objectives, ensuring that the questions are crafted with precision. The surveys, designed for users’ perceptions and behaviors, serve as instruments to uncover the impact of multisensory physical dimensions on their experiences. This qualitative approach allowed for a nuanced exploration, going beyond quantitative measurements to understand the qualitative aspects of users’ interactions with the built environment. Within these surveys, attention is devoted to identifying behaviors that help to reduce the building’s energy consumption and to emphasizing actions that contribute to sustainability with minimal impact and respect for the building’s heritage value [42]. Simultaneously, the surveys seek to identify unfriendly behaviors, pinpointing actions that may have a more significant environmental footprint and higher energy consumption. By embarking on this qualitative journey, the research aims to unravel the human dimension of the heritage building, acknowledging that user perceptions and behaviors play a pivotal role in the overall environmental impact [43].

The questionnaire designed for this study comprised 25 attributes, as outlined in Figure 7. The initial nine attributes pertain to the evaluation of the luminous environment, addressing aspects such as uniformity, brightness, contrast, glare, concentration, pleasantness, and overall satisfaction and comfort with the environment. The inspiration for the concentration function and attention–distraction attributes is drawn from the contrast variables presented in the works of Demers et al. [44,45]. Attributes 10, 11 and 14, focusing on the thermal environment evaluation, are adapted from previous studies, including the hot–cold and humid–dry scales used in thermal sensation votes (TSV) and humidity sensation votes (HSV). Additionally, attributes 13 and 16 gauge the sensation of temperature stability during the day and the movement of indoor air. Attributes 12 and 15 measure satisfaction and comfort concerning the thermal environment. User behavior attributes (17 to 20) are directed at thermal environment behaviors, while attributes 21 and 22 focus on luminous environment behaviors. The remaining attributes (23 to 25) explore the time use of heating systems, air conditioners, and artificial lights. The multisensory evaluation proposed a rating scale from 3 to −3 for questionnaire attributes. Data analysis is conducted using IBM-SPSS software 25 to examine the semantic differences in multisensory evaluation for the heritage office building [43].

2.5. Participants

In this study, the participants who had the (thermo-visual work time) experiences were fifty-one (51) users of the heritage office building of Kenadsa whose age categories ranged from 26 to 46 years. They are grouped into two groups, corresponding to each workspace. The first group, which came from the administrative staff, comprised 83% females and 17% males. The second one, which consisted of the employees, comprised 43.8% females and 56.3% males.

3. Results

3.1. Stage 1: Distribution of the Physical Dimensions of the Environment during the Work Hours in the Heritage Administrative Building of Kenadsa

The initial stage of this research is centered on mapping the distribution of physical environmental dimensions throughout the indoor heritage office building during working hours. The objective is to gain insights into the real-time thermal and luminous conditions experienced within the workspace. This involves a comprehensive assessment of the spatial variations in thermal and luminous parameters, providing a detailed understanding of the actual environmental conditions during the operational hours of the heritage office building.

The distribution of illuminance, “floor, walls and air temperature” and relative humidity as a function of space from the work experience in the different office spaces in our case study (municipal treasury area, tax revenue area, administrative building area) is presented in Figure 8. Regarding the dimensions of the luminous environment, it is observed in Figure 7a that the work offices are characterized by illumination levels that vary between 0.19 lux and 3.57 lux, with an average value of 1.963 lux between the different workspace zones.

In Figure 8, the thermal conditions of various indoor office spaces are assessed, presenting ambient air temperature, floor and wall temperature, and relative humidity, along with the corresponding office locations and numbers. Figure 8a reveals that the recorded ambient air temperature in indoor workplaces averages 35.4 °C (±1.04 °C), with an average relative humidity of 7.15% (±2.47%). The ambient floor temperature in the administrative building area averages 35.5 °C, while the wall temperature was recorded at 36.5 °C. Throughout the indoor workspaces, the thermal comfort stress level for occupants ranged from neutral (no thermal stress) to slightly warm (slight thermal stress). User discomfort is primarily attributed to direct solar radiation exposure, particularly evident in offices 01, 02 (tax avenue area) and offices 03, 04, 05, 06, 07, and 09, 10 (administrative building area with a southerly orientation). Conversely, in offices shaded by trees and other buildings, a significant temperature difference of 7.79 °C was observed, particularly in the office building’s northern-oriented offices. Temperatures in this area range from 32.2 °C to 38.9 °C, averaging 35.55 °C. Figure 8a illustrates that temperatures in south-oriented offices are higher than those in the North. The corridor route, located in the middle of the building, recorded an average ambient air temperature (Ta) of 30.75 °C, with a standard deviation of 5.56 °C, and a mean relative humidity of 12.6%.

Figure 8b provides a visual representation of the location of each office within the heritage office building and its respective areas, enhancing the understanding of spatial variations in thermal conditions. These findings contribute to a comprehensive overview of the thermal atmosphere, emphasizing the impact of solar exposure and spatial orientation on indoor office spaces.

3.2. Stage 2: Correlations between the Dimensions of the Physical Environment

Figure 9 presents the correlations among the different physical dimensions gathered from the three office areas within our building. These dimensions aim to examine the relationship between various variables, particularly focusing on those that characterize the luminous environment, such as illuminance and the thermal environment (Ta, Tf, Tw and RH). Figure 9 shows a significant correlation between the physical dimensions. Concerning the association between the luminous and thermal environment dimensions, it is found that illuminance has strong correlations with relative humidity, with correlation coefficients of −0.212, respectively. The relative humidity has a significant correlation with the air temperature, floor temperature, and wall temperature, with a correlation coefficient of −0.864, −0.886, and −0.881 (p-value = 0.000), respectively. Moreover, it is also seen that illuminance has a significant correlation with the thermal dimensions (wall temperature with correlation coefficients −0.290, floor temperature with correlation coefficients −0.313, air temperature with correlation coefficients −0.354).

3.3. Stage 3: Perceptual Data Analysis: On-Site Questionnaire with Users

Following the completion of the qualitative analysis and the identification of user office and space behaviors within the building, we organize the findings into three classification tables. Perception and behaviors table: This table encompasses the perception and behaviors of users during each period (Winter/Summer) under the current thermal and light conditions. Office information table: This second table provides details on offices, including the number of users, office location within the building (north, south, east, west), behavior type (Winter/Summer), and the duration of each behavior. Behavior code table: this third table catalogs each behavior along with its corresponding code, facilitating modeling within the software. For instance, thermal behavior 1 is represented as TB1.

Observations are utilized to identify recurring behaviors among offices, resulting in the identification of 12 distinct behaviors within the building. The subsequent phase involves initiating agent-based modeling using the QUELIA AGENT plug-in within the Rhinoceros 7 software [46,47].

Each behavior is meticulously modeled in every office, considering the precise number of users in each location. The software’s plugging strengths are adjusted to align with the behaviors in the real thermal and light environment, based on quantitative measurements such as temperature and brightness in each office. For instance, offices with lower temperatures are modeled to induce discomfort, leading to slower user execution speed and prolonged work duration compared to users in more comfortable offices.

3.4. Stage 4: Identification of the Perceptions and Behaviors of Each User Office

The second step of this research is based on the identification and analysis of the user’s perception and behaviors for each office and space in the heritage office building and analysis of the sensory perception and behavior evaluation through the evaluation of users that correspond to each office.

Table 2. The perceptual data average results analysis of the physical environment [43].

Offices	Indices	Perception Average Results
Global Analysis	From 01 to 16	Uniform (60.5%)	Non-uniform (39.5%).
		Bright1 (81.3%)	Dark1 (18.7%).
		Contrast (62.5%)	Low contrast (37.5%).
		Glaring (66.3%)	No glare (33.5%).
		Attention (56.3%)	Distraction (34.7%).
		Pleasant (57.3%)	Unpleasant (42.7%).
		Bright2 (18.5%)	Dark2 (60.5%).
		Satisfied1 (81.3%)	Dissatisfied (81.5%).
		Comfortable1 (56.2%)	Uncomfortable1 (43.8%).
		Hot1 (38%)	Cold1 (62%).
		Humid (12.7%)	Dry (87.3%).
		Satisfied2 (84.8%)	Dissatisfied2 (15.2%).
		Different (75%)	Similar (25%).
		Hot2 (23.3%)	Cold2 (76.3%).
		Comfortable2 (66.1%)	Uncomfortable2 (33.9%).
		Strong (52.5%)	Weak (47.5%).

presents the perception average results analyses with each index used in this study shown in our questionnaire corresponding to the evaluation of multisensory physical environments of the offices of our case study, respectively.

Table 2 illustrates that the average results for the initial perception primarily correlate with the thermal perception function of the space, encompassing factors such as humidity–dryness, pleasantness–unpleasantness, satisfaction–dissatisfaction, hotness–coldness, diversity–similarity, and comfort–discomfort. These factors are associated with the sensation of humidity, pleasantness, satisfaction, temperature disparities, and thermal comfort. Meanwhile, the average results for the secondary perception are predominantly associated with the visual perception of the luminous environment, including aspects such as contrast–low contrast, brightness–darkness, pleasantness–unpleasantness, and brightness2–darkness2. These aspects are linked to the perception of contrast, brightness, and pleasantness. Lastly, the average results for the tertiary perception are also connected to dimensions of visual environment perception, such as uniformity–non-uniformity, glare–no glare, and attention–distraction. These factors are related to uniformity, glare, and concentration. From this analysis, Table 2 provides a summary of perception average results for different indices related to the indoor office spaces, categorized globally across all offices (indexed from 01 to 16). Each index within the study denotes a distinct facet of the environment, with the accompanying percentages reflecting the proportion of respondents or participants who perceived that aspect in a particular manner. For uniformity, 60.5% perceived the environment as uniform, while 39.5% perceived it as non-uniform. brightness (bright1 and dark1) was perceived as bright by 81.3%, with 18.7% perceiving it as dark. Contrast yielded a split perception, with 62.5% perceiving it as contrasted and 37.5% perceiving it as low in contrast. Glare was reported by 66.3% of participants, whereas 33.5% did not perceive any glare. Attention and distraction were reported by 56.3% and 34.7% of participants, respectively. Regarding pleasantness, 57.3% found the environment pleasant, while 42.7% found it unpleasant. Brightness (bright2 and dark2) was perceived as bright by 18.5%, contrasted with 60.5% perceiving it as dark. Satisfaction1 was experienced by 81.3%, while 18.5% were dissatisfied. Comfortable1 was reported by 56.2%, while 43.8% found it uncomfortable. Hot1 and cold1 were perceived by 38% and 62% of participants, respectively. Humidity was perceived as humid by 12.7% and dry by 87.3%. Satisfaction2 was experienced by 84.8%, with 15.2% expressing dissatisfaction. Difference and similarity were perceived by 75% and 25% of participants, respectively. Hot2 and cold2 were perceived by 23.3% and 76.3% of participants, respectively. Comfortable2 was reported by 66.1%, while 33.9% found it uncomfortable. Lastly, Strength was perceived by 52.5%, with 47.5% perceiving it as weak. These findings offer valuable insights into user perceptions of various aspects of indoor office spaces, shedding light on environmental conditions and user experiences [43].

Table 3. The identification of user’s behaviors and its duration in each office (w.winter, s.summer) [43].

Office	Location	Number of Users	Behaviors	Duration of the Behavior
01	Northern side	02	20 (2, 1), 17 (0,−1), 18 (3)w, 19 (1,−1), 18 (1,0,−1)s, 22 (1w,−1w, 1s,−1s), 21 (0).	23 (−2), 24 (0), 25 (−1).
02	Northern side	02	20 (−2), 17 (0), 18 (1)w, 19 (1,−2), 18 (0,−1)s, 22 (−1w,−1s), 21 (0).	23 (−2), 24 (−2), 25 (−2).
03	Northern side	03	20 (2, 1), 17 (−1), 18 (3)w, 19 (−2), 18 (2,1)s, 22 (1w,−1w, 1s,−1s).	23 (−1), 24 (−1), 25 (−2).
04	Northern side	01	20 (−2), 17 (2), 18 (3)w, 19 (−2), 18 (−1)s, 22 (1w,−1w,−1s), 21 (0).	23 (−2), 24 (−1), 25 (−1).
05	Northern side	02	20 (2), 17 (2), 18 (3)w, 19 (0,−1), 18 (−1)s, 22 (1w,−1w,−1s), 21 (0).	23 (0), 24 (−1), 25 (−1).
06	Northern side	03	20 (2, 1), 17 (2,0), 18 (3)w, 19 (2,1,−1,−2), 18 (−1)s, 22 (1w,−1w,−1s).	23 (−1), 24 (−1), 25 (−1).
07	Northern side	02	20 (2, 1), 17 (2), 18 (3)w, 19 (−2), 18 (−1,−2)s, 22 (1w,−1w), 21 (0).	23 (−1), 24 (−1), 25 (−1).
08	East side	02	20 (2, 1), 17 (−1), 18 (−1)w, 19 (1,−2), 18 (−1,−1)s, 22 (1w,−1w, 1s,−1s), 21 (0).	23 (−1), 24 (−1), 25 (−1).
09	Southern-east side	01	20 (1, −2), 17 (−1), 18 (−1)w, 19(−2), 18 (0,−1)s, 22 (−1w,−1s), 21 (0).	23 (0), 24 (−1), 25 (0).
10	Southern-east side	02	20 (2, 1), 17 (0,−1), 18 (3)w, 19 (0,−1), 18 (−1)s, 22 (1w, 1s), 21 (0).	23 (−1), 24 (−1), 25 (0).
11	Southern-west side	02	20 (2, 1), 17 (2,0), 18 (3)w, 19 (1,−1), 18 (−1)s, 22 (1w, 1s), 21 (0).	23 (−1), 24 (−1), 25 (0).
12	Southern side	04	20 (2, 1), 17 (0), 18 (1)w, 19 (1,0), 18 (1,−1)s, 22 (1w,−1w, 1s,−1s), 21 (0).	23 (−1), 24 (−2), 25 (−2).
13	Middle side	04	20 (2, 1), 17 (0), 18 (1)w, 19 (1,0), 22 (1w,−1w,−1s), 21 (0).	23 (0), 24 (−2), 25 (−1).
14	Middle side	01	20 (2, 1), 17 (0,−1), 18 (3)w, 19 (1,−1), 18 (1,−1)s, 22 (1w,−1w, 1s), 21 (0).	23 (0), 24 (−1), 25 (−1).
15	Southern-west side	01	20 (2, 1), 17 (1), 18 (1,−1)w, 19 (1,0), 18 (1,−1)s, 22 (1w,−1w,−1s), 21 (0).	23 (−1), 24 (−2), 25 (−2).
16	Southern-west side	02	20 (2, 1), 17 (0), 18 (3)w, 19 (1,−1), 18 (2,1,−1)s, 22 (1w,−1w, 1s,−1s), 21 (0).	23 (−2), 24 (−1), 25 (−1).
17	Southern-west side	02	20 (2, 1), 17 (0), 18 (3)w, 19 (1,−1), 18 (2,1,−1)s, 22 (1w,−1w, 1s,−1s), 21 (0).	23 (−2), 24 (−1), 25 (−1).
18	Southern-west side	01	20 (2, 1), 17 (0), 18 (3)w, 19 (1,−1), 18 (2,1,−1)s, 22 (1w,−1w, 1s,−1s), 21 (0).	23 (−2), 24 (−1), 25 (−1).
19	Northern side	02	20 (2, 1), 17 (1), 18 (1,−1)w, 19 (1,0), 18 (1,−1)s, 22 (1w,−1w,−1s), 21 (0).	23 (−1), 24 (−2), 25 (−2).
20	Northern and southern side	04	20 (2, 1), 17 (1), 18 (1,−1)w, 19 (1,0), 18 (1,−1)s, 22 (1w,−1w,−1s), 21 (0).	23 (−1), 24 (−2), 25 (−2).
21	Northern side	03	20 (2, 1), 17 (2,0), 18 (3)w, 18 (1,−1), 18 (−1)s, 22 (1w, 1s), 21 (0).	23 (−1), 24 (−1), 25 (0).

presents comprehensive information about various offices within Kenadsa’s heritage office building, including their location, the number of users, observed behaviors, and the duration of these behaviors. Each office is identified by a unique number and is situated on different sides of the building, such as the northern side, eastern side, southeastern side, southwestern side, southern side, and middle side. The user count for each office is specified, providing an understanding of the occupancy in different sections. The focal point is the behaviors observed in each office, denoted numerically and detailed with sub-behaviors in parentheses. For instance, behavior 20 may have sub-behaviors like 2 and 1. The duration or intensity of each behavior is also outlined, such as the duration of behavior 23 with an intensity of −2 [43].

This information is crucial for comprehending the behavioral patterns of occupants across diverse offices within the building. Notably, offices in the same location or side exhibit similar behaviors and durations. A noteworthy observation is the disparity in behavior duration between northern and southern offices, indicating varying energy consumption patterns in summer and winter. Additionally, offices utilizing solar protection measures like blinds tend to use artificial lights more frequently. The analysis underscores the importance of considering user behavior for optimizing energy use and environmental comfort within the building, shedding light on tendencies related to lighting, heating, and cooling systems. The acknowledgment of the corridor as a relatively dark area by most users further adds valuable insights into the occupants’ perception of the built environment [43].

The behavior code in

Table 4. The codification of user behaviors and their duration in each office for the agent-based modeling.

User’s Behaviors	Behavior’s Type	Codes (Modeling Agent-Based Modeling)
Air conditionner use 4 h–6 h/6 h–8 h/8 h–10 h	Thermal behavior	TB1, TB2, TB3
heating system time use 4 h–6 h/6 h–8 h/8 h–10 h	Thermal behavior	TB4,TB5,TB6
Ceiling Lamps light time use 4 h–6 h/6 h–8 h/8 h–10 h	Luminous behavior	LB1,LB2,LB3
Turn off artificial light and microns during lunch break	Luminous behavior	LB4
Use shutters/blinds	Thermal and luminous behavior	TLB
Opening windows for ventilation	Ventilation behavior	VB

, the third classification table, systematically catalogs each behavior and assigns a unique code to facilitate modeling within the software. This structured approach simplifies the representation of behaviors in the simulation. For example, if the first thermal behavior is labeled as TB1 (presumably representing “thermal behavior 1” or a similar designation), this code becomes a shorthand reference that the software can use to efficiently incorporate and differentiate various behaviors. These codes serve as a practical means of identifying, organizing, and implementing behaviors within the modeling environment. By adopting a standardized coding system, you enhance the software’s ability to manage and simulate diverse behaviors across different scenarios and user interactions. This streamlined representation contributes to the clarity and efficiency of the modeling process, allowing for a more effective analysis of user responses to the building’s thermal and light conditions.

3.5. Stage 4: Behavior’s Agent-Based Modeling Simulation Study

The third stage consists of starting agent-based modeling in the Rhino 7 software using the Quelia Agent plug-in. In addition, the weather data of our selected city (Kenadsa, Bechar), which are exported from the Climate Studio software, were integrated. We have finished modeling each behavior in each office with the exact number of users in each location by adapting the plugging strengths of the software with the behaviors in the real or current thermal and light environment (measurement results for the “quantitative approach”, temperature and brightness in each office).

Figure 10 includes a representation of the behavior’s agent-based modeling simulation study’s process in our building case study. Figure 10a–c represent the modeling process of our agents while Figure 10d–f represent the simulation of our agent’s speed and lifespan in each office under the physical environment conditions (the actual and real thermal, luminous environment and the performance of our building). Thus in the software, the duration of work is represented as the lifespan of the agents (users) in the simulation. The user’s performance speed is represented by the movement speed of the agents in the simulation.

From our agent-based modeling simulation observation, we conducted two stages of simulation (the presence of users’ behaviors/the absence of users’ behaviors) in order to know the optimal thermal and luminous office that has the best user productivity in our case study. Firstly, we found three types of offices: the first ones are the offices that have an optimal luminous and thermal environment, and where their users declared their comfortable state in their offices without any of the energy-wasting behaviors (01,02,07,09,21) in Figure 7; their agents disappear quickly and move so fast (short job execution time, the speed of user performance). In contrast, the second ones have an optimal luminous and thermal environment, and their users declare their comfortable state in their offices with the presence of the energy-wasting behaviors (03,04,05,06,08,13,14,16) in Figure 8, and they have the same results as the first ones. The third ones are those who have the worst luminous and thermal environments, and where the users declare their uncomfortable state in their offices with the presence of the energy-wasting behaviors (10,11,12,15,17,18,20) in Figure 8. It essentially gives us a low speed of execution of the user and a long duration of execution of the work (low speed of movement and a longer lifespan of agents) (Figure 11).

4. Discussion

4.1. Synthesizing the Findings

The detailed analysis reveals critical insights into the thermal and luminous conditions, user perceptions, and behaviors within Kenadsa’s heritage office building. Here is a synthesis of the key findings:

The findings from the first stage of this study about the thermal and luminous environment that there is a significant temperature difference between offices exposed to direct solar radiation and those which are shaded, indicating the impact of orientation. Offices facing south experience discomfort due to direct solar radiation, while shaded areas show more comfort and ambient temperature, average ambient air temperature is 35.4 °C, with notable variations across different office zones. User discomfort is linked to solar radiation exposure, particularly in south-oriented offices. North-oriented offices show a temperature range of 32.2 °C to 38.9 °C, with an average of 35.55 °C, highlighting the impact of orientation. The corridor, located centrally, records an average ambient air temperature of 30.75 °C and 12.6% relative humidity. From our second stage, the findings suggest that illuminance has a significant correlation with the thermal dimensions (wall temperature with correlation coefficients −0.290, floor temperature with correlation coefficients −0.313, air temperature with correlation coefficients −0.354).

The findings from the third stage of this study about user perceptions suggest that regarding thermal perception, users generally perceive the environment as slightly warm, with variations in humidity, satisfaction, comfort, and perceived temperature, and luminous perception, brightness, contrast, and glare perceptions vary, influencing users’ attention, distraction, and overall pleasantness. Finally, yet importantly, the visual environment perception, users’ perceptions of uniformity, glare, and attention vary, affecting their concentration. Furthermore, the findings of user behaviors suggest that every office has its own specific behaviors and durations. This also suggests that offices in the same locations or on the same sides exhibit similar behaviors and durations, emphasizing the influence of environmental conditions and disparities in behavior duration between northern and southern side offices, suggesting varying energy consumption patterns in different seasons. However, offices using solar protection measures like blinds tend to use artificial lights more frequently. In addition, users perceive the corridor as a relatively dark area, affecting their overall experience.

The findings from the fourth stage of this study are about agent-based modeling. They suggest that the simulation distinguishes between offices with optimal conditions and those with uncomfortable conditions, considering the presence or absence of energy-wasting behaviors or overconsumption behaviors. However, we found that offices with optimal conditions and minimal energy-wasting behaviors exhibit faster agent disappearance and movement speed, suggesting higher user productivity.

Finally, the findings offer valuable insights for optimizing the design of heritage office buildings, emphasizing the importance of environmental conditions on user comfort and productivity. In addition, the agent-based modeling approach provided a dynamic representation of user behaviors and their impact on performance, contributing to a deeper understanding of the building’s functionality.

4.2. Comparison, Strengths and Limitations of the Study

Many of these studies have centered on developing novel approaches and tools for capturing occupant behavior patterns and preferences within indoor settings, with the aim of optimizing the operation of building systems accordingly [48,49]. These investigations often delve into aspects such as occupancy dynamics [50,51], thermal, visual, and acoustic comfort [52,53], as well as interactions with various control interfaces, both mechanical and digital. These interfaces include lighting control, window/door operations, thermostats, and appliances [27,28,54]. Additionally, there is a subset of research dedicated to energy feedback systems aimed at implementing behavioral interventions for enhancing energy efficiency in buildings [29].

The strength of this research is mainly due to its comprehensive analysis and its objective and subjective approaches. The study provides a thorough examination of indoor environmental conditions, user perceptions, and behaviors within a heritage office building. It covers aspects such as ambient temperature, humidity, luminosity, and visual environment, offering a holistic view. By integrating user perceptions and behaviors, the study adopts a user-centric perspective. This approach recognizes the importance of users’ experiences and preferences in building design and operation. None of the previously published articles have investigated the use of agent-based modeling in a heritage office building. This advanced simulation technique allows for the dynamic representation of user behaviors, providing insights into the impact of various conditions on user productivity. Moreover, the detailed classification of behaviors in different offices, including their location, user count, and duration, adds granularity to the analysis. This classification enables a nuanced understanding of energy consumption patterns and user preferences. The study goes beyond theoretical analysis by proposing practical applications, such as the use of blinds for solar protection and the consideration of user behaviors in building design. This makes the findings more actionable for architects and designers. Finally, yet importantly, the integration of weather data from the selected city enhances the study’s applicability to real-world conditions, providing a more accurate representation of the building’s performance.

Our findings are limited to the study of a single case study: The study focuses on a specific heritage office building in Kenadsa, Bechar. While this provides in-depth insights into a particular context, the findings may not be universally applicable, limiting the generalizability of the results. Moreover, the number of offices analyzed and the corresponding user responses might be limited. A larger sample size could enhance the statistical robustness of the findings and provide a more representative picture of user experiences. In addition, user perceptions are inherently subjective and can vary based on individual preferences. The study acknowledges this subjectivity but may face challenges in precisely capturing the diverse range of user experiences. Finally, while agent-based modeling is a powerful tool, there are inherent simplifications in any simulation. The model’s accuracy relies on the assumptions made during the modeling process, and real-world complexities may not be fully captured.

4.3. Implications for Practice and Future Research

The study’s implications for practice are substantial, offering valuable insights for designers and architects to prioritize user experience in building design. The findings suggest a user-centered design approach, emphasizing the importance of understanding occupant behaviors, preferences, and the impact of environmental conditions. Practical recommendations, such as employing solar protection strategies with blinds, optimizing energy-efficient lighting, and fostering an adaptive design that considers both seasonal variations and user behaviors, can inform the creation of more user-friendly and efficient spaces. Additionally, the study highlights the need for education and awareness among building users about sustainable practices and their role in energy consumption. Ongoing building performance monitoring is recommended for continuous optimization. In terms of future research, the study encourages multi-case studies, longitudinal investigations, and the integration of additional environmental parameters for a more comprehensive understanding of indoor environmental quality. The validation of assumed impacts on productivity, exploration of technological solutions, cross-disciplinary collaboration, and consideration of cultural and contextual factors are emphasized. Furthermore, the study suggests exploring occupant feedback mechanisms and replicating agent-based models in different simulation environments to enhance robustness and consistency in findings. The focus on life cycle assessment (LCA) results adds an important dimension to guide sustainable building practices. Collectively, these implications provide a roadmap for advancing knowledge in building design, sustainability, and user-centric approaches to indoor environmental quality.