*Proceeding Paper* **Framework for Energy Performance Measurement of Residential Buildings Considering Occupants' Energy Use Behavior †**

**Nida Azhar 1, Farrukh Arif 2,3,\* and Abdul Basit Khan <sup>3</sup>**

	- **\*** Correspondence: farrukh@cloud.neduet.edu.pk
	- † Presented at the 5th Conference on Sustainability in Civil Engineering (CSCE), Online, 3 August 2023.

**Abstract:** Buildings' contribution to global final energy use is about 30%, which makes them a primary focus for implementing energy-efficient measures. Building energy efficiency is an important consideration for residential buildings due to the significant environmental impact of energy consumption and the rising cost of energy. Estimating and optimizing a building's energy performance is an efficient method to reduce its environmental impact and cost. There exists a lack of accuracy in estimating the energy performance of a building due to approximations in the monitored data as well as a lack of consideration for occupants' energy use behavior. This study aimed to develop a comprehensive framework that assists in accurately estimating building energy performance considering occupants' energy use behavior. The framework proposed a scheme to collect occupant behavior data, such as occupancy patterns, appliance usage, and lighting conditions, through a living-lab setup and developing an occupants' behavior model that was utilized for more accurate building energy modeling and performance analysis.

**Keywords:** building energy performance; occupants behavior modeling; living-lab concept

#### **1. Introduction**

Buildings' contribution to global final energy use is about 30%, which makes them a primary focus for implementing energy-efficient measures [1]. A significant portion of this energy is wasted due to inappropriate building envelope design and construction. Building energy performance measurements can serve as a basis for building owners to make informed decisions for enhancing building energy efficiency. There is a growing concern in the building industry about the gap between the projected energy performance and the actual energy performance of buildings [2]. Bridging this performance gap is crucial in achieving the goal of reducing energy demand and enhancing building energy efficiency. The difference between the predicted and actual energy performance is due to approximations in the data as well as a lack of consideration for occupants' energy use behavior [3]. Therefore, a comprehensive energy performance measurement framework can help to effectively assess and quantify the building's energy efficiency.

The term occupant's behavior refers to the actions and responses exhibited by individuals within the building related to energy use and comfort, which are influenced by factors that include climate, building envelope, building energy and services systems, indoor and outdoor environments, time of the day, occupants' age and gender, and physiological, psychological, social, and economic factors [4].

**Citation:** Azhar, N.; Arif, F.; Khan, A.B. Framework for Energy Performance Measurement of Residential Buildings Considering Occupants' Energy Use Behavior. *Eng. Proc.* **2023**, *44*, 15. https:// doi.org/10.3390/engproc2023044015

Academic Editors: Majid Ali, Muhammad Ashraf Javid, Shaheed Ullah and Iqbal Ahmad

Published: 28 August 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **2. Literature Review**

To address the complex nature of occupant behavior and its impact on building energy consumption, Sun and Hong [5] proposed a framework for quantifying the influence of occupants' behavior on the energy savings achieved through energy conservation measures. Meanwhile, Wang et al. presented quantitative energy performance assessment methods specifically tailored for existing buildings, considering occupants' behavior and all other relevant factors to evaluate the buildings' energy efficiency [6]. Furthermore, Balvedi et al. [7] conducted a comprehensive review of various approaches and strategies available for gathering data on occupant behavior. They explored how these methods can be integrated into building energy simulation tools by incorporating occupant behavior models. It is widely observed that incorporating actual data for occupant behavior in energy analysis yields more accurate results as compared to energy simulations run without considering it [8].

The International Energy Agency identified six parameters that affect energy use in buildings. These parameters include climate, building envelope, building energy and services systems, indoor design criteria, building operation and maintenance, and occupant behavior. Each of these parameters plays a critical role in determining the energy efficiency of residential buildings and strategies for improving energy efficiency must consider each of these factors [9]. In addition, Chen et al. [10] in their study emphasized the need for a holistic approach to measuring the energy performance of residential buildings that considers all relevant factors and their potential impacts on energy consumption.

Laaroussi et al. [11] in their study identified the major issues and key drivers affecting occupants' behavior through an evaluation of existing approaches and methods for occupant behavior analysis. Furthermore, this study proposed and developed different methods to assess and predict the energy use behavior of occupants with better accuracy where conventional techniques such as structured and unstructured interviews, questionnaires, etc., prove to be inadequate. The study also emphasized integrating energy feedback programs into the building energy performance processes.

Incorporating an occupants' behavior model in the framework provides a more realistic evaluation of energy consumption patterns and assists in providing valuable insights into the factors affecting the energy performance of residential houses. Chen et al. [12] reviewed the impacts of occupant behavior on building energy consumption and established that the actual occupancy and the interactions with buildings are the key influencing factors determining the building energy consumption.

#### **3. Methodology**

This research study proposed a framework to measure the energy performance of residential buildings that incorporates occupants' energy use behavior with the purpose of accurately quantifying the impact of occupants' behavior on energy consumption in residential houses. The framework consists of three steps. (1) energy audit and data collection; (2) occupant behavior modeling; and (3) building energy modeling and performance analysis.

#### *3.1. Energy Audit and Data Collection*

The energy audit and data collection step begins with an assessment of the residential buildings, identifying the key influencing factors that affect energy consumption in residential buildings. For the energy audit and data collection, a hybrid method can be adopted that involves surveys, interviews, and on-site measurements using instruments to identify energy consumption and building envelope parameters in residential buildings, and installation of sensors for real-time monitoring of thermal properties of a building, indoor and outdoor environmental parameters, and occupants' energy use behavior through the living-lab concept [13].

The living-lab concept is a research methodology that focuses on the needs of endusers and stakeholders in the development of complex solutions. It involves creating a real-life test environment to sense, prototype, validate, and refine innovative solutions that address specific challenges [14].

#### *3.2. Occupant Behavior Modeling*

Occupant behavior modeling plays an essential role in understanding and predicting the energy consumption patterns of the occupants in a residential building. In a postoccupancy evaluation analysis [15], it was observed that occupant behaviors, including dissimilar presence at home, diverse occupancy levels, and differences in the occupants' thermal preferences play key roles in actual energy consumptions. Occupant behavior modeling involves developing mathematical models that incorporate various factors such as occupancy, interactions with the building systems, and occupants' preferences. The data collected in the energy audit and data collection step can be used to develop the mathematical model. Occupants' behavior is complex and diversified and has a stochastic nature rather than a deterministic one [16]. Therefore, stochastic occupant behavior model can be developed to capture the complex and diversified energy use behavior of occupants and generate synthetic occupancy schedules and occupants' energy use patterns with more precision over time. Such a model can be used to generate occupancy schedules and occupants' energy use patterns by simulating and predicting future states based on the current state and transition probabilities which can then be incorporated into the building energy simulation tools.

#### *3.3. Building Energy Modeling and Performance Analysis*

Building energy modeling and performance analysis begins with the development of a detailed energy model of the residential building, considering its physical characteristics, such as building geometry and orientation, building materials, insulation, HVAC systems, lighting, and appliances, using the data obtained from the energy audit and data collection. The real energy consumption data and the occupants' energy use behavior data such as occupancy schedules and occupants' energy use patterns can be incorporated into the energy model of the residential building. An energy simulation tool can then be used to simulate the energy performance of a residential building, with energy use intensity (EUI) serving as a metric to measure its energy consumption.

#### **4. Building Energy Performance Measurement Framework**

The framework depicted in Figure 1 comprises three main steps. The first step, energy audit and data collection, shall be carried out by collecting data related to building energy consumption and energy use; this includes; the data related to building envelope components and parameters such as building orientation, walls, roofing system, windows and glazing, doors, foundation and basement, exterior cladding, roof and window overhangs, solar heat gain coefficient (SHGC), insulation, R-Value and U-value, visual transmittance, etc., and monitoring occupants' energy use behavior through questionnaires, survey, interviews, and real-time monitoring through IOT sensors and data-logging sensors. The second step is to develop an occupant behavior model to generate synthetic occupancy schedules and energy use patterns using the data collected through occupant behavior monitoring. The data collected in the first and second step is then analyzed to perform building energy modeling and performance analysis.

**Figure 1.** Building energy performance measurement framework.

#### **5. Conclusions**

This framework integrates an occupant behavior model, which can capture the complex and diversified energy use behavior of occupants. Therefore, this framework facilitates more accurate measurements of energy performance based on real-time data of the occupants' behavior and can enable the evaluation of different energy-saving strategies and the development of more efficient building designs customized to the occupant's behavior.

**Author Contributions:** Conceptualization, N.A. and F.A.; methodology, N.A.; writing—original draft preparation, A.B.K.; writing—review and editing, F.A.; visualization, N.A. and A.B.K.; supervision, F.A.; project administration, F.A.; funding acquisition, F.A. and N.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research is a part of Sindh Higher Education Commission project-168 "Development of Energy Efficient Housing Design for Karachi using Living Lab-Virtual Reality Integration." The APC was funded through same project as well.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** All the relevant data has been included in the paper.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

### *Proceeding Paper* **The Behavior of Retrofitted GPC Columns under Eccentric Loading †**

**Shahzaib Farooq \*, Faheem Butt and Rana Muhammad Waqas**

Department of Civil Engineering, University of Engineering and Technology, Taxila 47080, Pakistan;

faheem.butt@uettaxila.edu.pk (F.B.); rana.waqas@uettaxila.edu.pk (R.M.W.) **\*** Correspondence: shahzaibfarooq010@gmail.com

† Presented at the 5th Conference on Sustainability in Civil Engineering (CSCE), Online, 3 August 2023.

**Abstract:** Geopolymer concrete (GPC) has been the subject of ongoing research as a suitable substitute for conventional concrete production because of its benefits for the environment. However, there is little research regarding retrofitting the structural part if a GPC member fails. The current study thus concentrates on the damaged GPC structural members/columns. For this purpose, twelve columns which include four CC columns, four GPC Columns, and four FRGPC columns, were retrofitted with CFRP sheets and tested in the electrohydraulic testing apparatus (5000 kN). The results showed significant improvement in the ultimate load value of all 12 columns. Axial strain in all 12 columns also increased significantly. The ductility index of the columns was also calculated using axial strain values. The axial load–displacement behavior, ductility, and loading capacity of the evaluated columns are all significantly improved by the addition of steel fibers.

**Keywords:** carbon fiber reinforced polymer (CFRP); eccentricity; fiber reinforced geopolymer concrete (FRGPC); geopolymer concrete (GPC)

#### **1. Introduction**

Geopolymer concrete (GPC) has evolved as a new option that may completely eliminate the need for cement while promoting the efficient use of waste materials. However, if a GPC structural member fails, there is little study about the retrofitting of that member. Therefore, the present study focuses on the damaged GPC structural columns. Fiber-reinforced polymer (FRP) composites, the newest modern composite materials, have recently surpassed conventional retrofitting techniques in demand. FRP jackets are the ideal material because of their high rigidity and high strength-to-weight ratio. As a result, FRP has seen significant application in retrofitting.

Numerous tests and theoretical analyses have clearly proved that wrapping FRP composites around columns is a very successful approach. Yang et al. investigated the eccentric compression loading of rectangular high-strength concrete columns restricted with carbon fiber-reinforced polymer (CFRP) [1]. Zeng et al. investigated the cyclic axial compression behavior of FRP spiral strip-confined concrete [2]. Askandar et al. examined the behavior of RC beams reinforced with FRP strips under the combined action of torsion and bending [3]. For FRP spiral strip-confined concrete, Liao et al. researched the stress–strain behavior and design-oriented model [4]. The partially FRP strengthening approach is a viable option, particularly for columns that require moderate increases in strength and deformation capacity [5].

In light of the previously mentioned, the purpose of this research is to explore the axial compressive behavior of partially FRP confined Fiber Reinforced Geopolymer Concrete (FRGC). The current investigation involved the retrofitting and testing of 12 columns using CFRP.

**Citation:** Farooq, S.; Butt, F.; Waqas, R.M. The Behavior of Retrofitted GPC Columns under Eccentric Loading. *Eng. Proc.* **2023**, *44*, 16. https:// doi.org/10.3390/engproc2023044016

Academic Editors: Majid Ali, Muhammad Ashraf Javid, Shaheed Ullah and Iqbal Ahmad

Published: 28 August 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **2. Experimental Procedure**

A total of 12 columns with cross-sections of 200 mm square and heights of 1000 mm were examined in the present study. Six deformed 12 mm diameter bars were used to brace the columns longitudinally. In all instances, transverse reinforcement was supplied as closed ties of diameter 6 mm bars spaced 100 mm from centers. Deformed steel with a yield strength of 300 MPa for D6 (6 mm) bars and 450 MPa for D12 (12 mm) bars was employed. CFRP wrapping of the columns was carried out using a unique pattern, as shown in Figure 1a. The results are compared between CC columns and GPC columns. In order to evaluate the columns, two loading scenarios were used, concentric loading and eccentric loading with varying eccentricities (eccentricity e = 15, 35, and 50 mm). Sample details are given in Table 1.

**Figure 1.** (**a**) Load mechanism for CFRP columns; (**b**) ultimate load of CC and GPC columns.


**Table 1.** Mix proportion and material quantities of mixes [6].

#### *2.1. Preparation of Specimen*

Firstly, the repairing of the specimens is carried out using geopolymer mortar having 50% fly ash and 50% slag as a binder. The specimens were then dried in atmospheric conditions for 28 days. Secondly, retrofitting of the specimens was carried out using CFRP sheets 3 mm thick having a width of 82 mm. A total of four CFRP sheets were used for wrapping each specimen, two clockwise and two anti-clockwise, at an angle of 20◦. The CFRP should be placed firmly against the GPC and CC surface in order to make good contact and remove any air pockets between it and the concrete surface.

#### *2.2. Testing*

At 28 days, unidirectional axial loading was given to each specimen. A 5000 kN capacity electrohydraulic testing equipment was used to apply the loading. Under displacement control conditions, the columns were tested to the point of failure. The load was applied at regular 1 mm/s intervals. For the concentrically loaded columns, a similar system was employed, but there was no loading pin. In order to prevent columns from failing prematurely due to overstressing, steel collars of thickness 3.2 mm having a width of 76 mm were attached at both ends of each column prior to testing. On the top and bottom sides of the columns, a thin coating of Plaster of Paris was also used to provide a level surface for the test's uniform weight distribution. A magnetic Linear Variable Differential Transformer (LVDT) of 20 mm capacity and 0.001 mm accuracy was vertically aligned with the base plate of the machine to measure the axial deformation in the specimen.

#### **3. Research Methodology**

First, the surface of the specimen was prepared, and any loose or broken material was removed. Second, a geopolymer mortar with a binder made of 50% fly ash and 50% slag is used to repair the specimens. Wrapping of CFRP strips was carried out around the column after applying the bonding agent to the finished surface of the column, make sure the CFRP strips were at a 20-degree angle with the column's horizontal axis. Use a 20 mm capacity magnetic LVDT and electrohydraulic testing apparatus (5000 kN), which can show the structural performance and behavior of the columns. The LVDT is accurately positioned and calibrated in order to precisely measure the vertical deflection of the column during the test. As we gradually added force to the column until it reached its full capacity, we set the deflection rate to 1 mm per minute. We then took the deflection data that the LVDT computer supplied to chart the behavior of the column as the load increased. We repeated the test until the column failed or started to distort visibly.

#### **4. Results**

The ultimate load of the specimens obtained from the experimental results is shown in Table 2. We can clearly see that the specimens from the GPC group showed lower ultimate load values than those from the CC group. The ultimate load of CC, GPC, and FRGPC columns was improved from previous results. The ultimate load values after retrofitting shows significant improvement. The fact that the ultimate load of the FRGPC column on concentric loading is lower than the previous value is due to the rusting of steel fibers present in the specimen. Figure 1b demonstrates how changing the value of eccentricity in the tested specimens affects load levels. In other words, as the eccentricity of the axial load increases, the ability of the column to carry loads decreases, which is linked to its eccentricity. The ductility index for all specimens was also calculated.


**Table 2.** Columns axial strength and ductility index.

#### **5. Conclusions**

The main focus of this research is to provide the proper solution for retrofitting GPC columns. From all the above research, we can now say that CFRP wrapping of GPC columns is a violable solution. This paper presents the results of twelve columns, including four reference columns, four GPC columns, and four FRGC columns. All 12 columns were wrapped with CFRP sheets in a particular manner. The ultimate strength of the CC columns, GPC columns, and FRGPC columns was compared to a previous study due to retrofitting. Considering the experimental and theoretical findings in this research, the ultimate load values of CC columns, GPC columns, and FRGPC columns increased from the values that were obtained from previous studies. The axial displacement of all the columns also significantly improved. The ductility index of all 12 columns also increased.

**Author Contributions:** Conceptualization, S.F. and F.B.; methodology, R.M.W.; software, S.F.; validation, S.F.; formal analysis, S.F.; investigation, F.B.; resources, S.F.; data curation, R.M.W.; writing—original draft preparation, S.F.; writing—review and editing, S.F.; visualization, F.B.; supervision, F.B.; project administration, F.B.; funding acquisition, S.F. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to confidentiality purposes.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
