Quantitative approach for evaluating the building design features impact on cooling energy consumption in hot climates

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Quantitative Approach for Evaluating the Building Design Features Impact on Cooling Energy Consumption in Hot Climates Abdullah Al-Saggaf , Hassan Nasir , Mahmoud Taha PII: DOI: Reference:

S0378-7788(19)30659-0 https://doi.org/10.1016/j.enbuild.2020.109802 ENB 109802

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Energy & Buildings

Received date: Revised date: Accepted date:

1 March 2019 3 December 2019 18 January 2020

Please cite this article as: Abdullah Al-Saggaf , Hassan Nasir , Mahmoud Taha , Quantitative Approach for Evaluating the Building Design Features Impact on Cooling Energy Consumption in Hot Climates, Energy & Buildings (2020), doi: https://doi.org/10.1016/j.enbuild.2020.109802

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Highlights     

Evaluation of seven architectural design features impact on cooling loads consumption Smart energy scoring system is developed to use at the early design stage in hot climate regions Three design alternatives BIM models are analyzed in terms of cooling loads in hot climates Quantitative analysis of the alternatives heat breakdown, consumption, and cost-effective BIM integration to the scoring system proves the ability to optimize and reduce cooling loads

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Quantitative Approach for Evaluating the Building Design Features Impact on Cooling Energy Consumption in Hot Climates Abdullah Al-Saggaf, M.Sc.1, Hassan Nasir, Ph.D.2, and Mahmoud Taha, Ph.D.3 Abstract Architectural building design practice in hot climate regions has high-priority in the design process. Thus, selecting appropriate Architectural Design Features (ADFs) at the early design stage provides significant opportunities to control heat flows, maximize building energy performance, reduce excessive energy consumption cost, and preserve a comfortable temperature for occupants. In this research, an ArchitecturalBased Energy Impact Scoring System (AEISS) was developed based on inputs from 10 expert architects, and it consists of seven ADFs and 40 multiple design options. The proposed system provides a simple decision scoring system for designers to evaluate design alternatives and optimize the energy consumption of buildings in the early design phase. A simple user-friendly program for the AEISS was developed to assist the designers in generating the design alternatives scores. Energy simulation analyses were performed to validate the applicability of the proposed AEISS by integrating 3-D BIM models of three different design alternatives of a residential building in Saudi Arabia with Ecotect analysis software. A detailed analysis was performed to evaluate the energy impact of the selected ADFs and to compare the alternatives in terms of energy consumptions and cost-efficiency. The simulation results proved that the proposed AEISS gave responsive results and is capable of providing stakeholders with the optimum decision to select the design alternative with high energy performance and low consumption cost. Keywords: Cooling consumption; Building design; Design features; Scoring system, Sustainability; Building Information Modeling (BIM); Energy simulation; Hot Climates 1

Research Associate, Dept. of Civil Eng., King Abdulaziz University, Jeddah, Saudi Arabia, (corresponding author), Email: [email protected] 2 Associate Professor, Dept. of Civil Eng., CECOS University of IT & Emerging Sciences, Peshawar, Pakistan. Email: [email protected] 3 Associate Professor, Dept. of Civil Eng., King Abdulaziz University, Jeddah, Saudi Arabia. Email: [email protected]

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1. Introduction The energy consumption of the building sector is continuously growing and is responsible for about 40% of total global energy consumption [1]. A significant proportion of 37% of the total energy used for Heating, Ventilation, and Air Conditioning (HVAC) systems in the building sector in the U.S. [2] and 30% of the total energy is used for heating and cooling loads in Canada [3]. For this reason, measures for energy-efficient are being increasingly implemented, particularly in the building sector. According to the Saudi Energy Efficiency Center (SEEC) [4], the three-building sectors of residential, governmental, and commercial consume more than 75% of the total electrical power, with approximately 7% annual growth rate. Around 50% of electric power is consumed in the operation of residential buildings [5]. In addition, SEEC also reported that more than 70% of Saudi residential buildings lack proper thermal insulation and architectural solutions that can reduce energy consumption in cooling devices. It also estimated that proper architectural design solutions can save around 250 million barrels of oil equivalent over a five-year period. In Saudi Arabia (as a special case of hot climate region), where the extremely hot climate characteristic is a combination of high temperature, high humidity, and high rainfall, a large amount of cooling loads energy demand in buildings is increased excessively to maintain occupants' thermal comfort level. As a result, architectural designers use a few design solutions and ignore many others. For example, most buildings use a few small windows to avoid the heat, which is not sufficient to provide the needed sunlight to the space users. On the other hand, some architects who design buildings with fancy glazing facades need to consider the cost of additional HVAC load in their designs. Energy consumption is one of the most important issues surrounding green buildings and one that can significantly impact reductions in operating costs. In hot climate regions, minimizing energy consumption due to the ever-increasing scarcity of the natural resources and rising energy costs, have become increasingly pressing challenges to A/E/C industry. Thus, multiple researchers have concluded that the demand for reducing energy consumption appears to be the most important aspect of the early buildings design stage [5-12]. However, estimating the energy consumption of a building early in the

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design phase is often challenging due to the high level of subjectivity by architects and engineers in this phase [13]. For example, finding suitable multi-objective design solutions that lead to reducing the energy consumption of a certain building is not an easy task due to the stakeholders‟ conflicting objectives. Thus, the conceptual design phase of a building provides significant opportunities for designers to reduce subjectivity in the design decision making by selecting the optimum design alternative that would lead to obtaining a building with high energy efficiency [14]. During this stage, providing possible building design solutions that could optimize Architectural Design Features (ADFs) (such as building envelope, storey height, building materials, plan shape, and windows) are possible to primality minimize energy demand by appropriate building design details which do not involve high cost. In order to improve the early building design process, integrating promising innovative tools used in digital environments of data can provide great potential for finding optimal solutions for the thermal load's energy trade-off. Building Information Modeling (BIM) simulation tools can be used to predict the energy performance by creating building models, developing different design alternatives, analyzing energy performance for these alternatives [15]. In addition, BIM tools can support the access and extraction process of the required ADFs regarding the thermal properties from a BIM model for accelerating the evaluation of design alternatives, and provide an accurate result. The main objective of this research is to develop an Architectural-Based Energy Impact Scoring System (AEISS) used to evaluate the design alternatives to reduce related-energy subjectivity in decisions at the early architectural design process. A simple user-friendly program for the proposed AEISS has been developed to assist stakeholders in building projects to evaluate architectural design alternatives in terms of energy performance and to make it easier for users to perform evaluation process. To develop the AEISS, producing and assessing a set of ADFs categories and design options are employed based on 10 expert architects‟ consultations. In addition, a further ADFs analysis was performed to rank the key ADFs based on energy impact and then to demonstrate the optimum parameters of design options in hot climate regions. Further, a real case study of three design alternatives models was established to be evaluated by the proposed AEISS using BIM Autodesk Revit Architecture software. Then, the three models were 4

exported to Ecotect energy simulation tool for further HVAC energy consumption analysis to validate the results of AEISS. This research work is aiming to support the architects in identifying the design features with the highest effect on energy performance while considering all design features at the early stage. Therefore, the AEISS provides support to architects in making informed decisions about the alternative design they chose. As such, although a gap between the architects‟ preference (owner requirements) and the actual building energy performance may exist, a balanced design has the smallest gap. The proposed AEISS is novel in the sense that the decision problem is full of subjectivity and bias, and none of the previous research addressed a quantitative assessment of the combined impact of the identified ADFs and their design options on energy building performance in hot climates. This research presents the first scoring system that ranks design alternatives considering multiple design features with using BIM simulation as validation. The systematic approach of this paper supports the difficult evaluation process that is primarily one criterion at a time, and arrives at best design, considering all ADFs options impact simultaneously. The novelty of the research approach stems from: (1) it is designed for hot climates which have excessive cooling energy use due to the lack of utilization of better design features (2) it incorporates a comprehensive list of ADFs that represent a wide range of design options reported individually in different studies but not integrated in any previous work; (3) it evaluates design through the proposed AEISS based on energy performance with multiple ADFs represent different perspectives, thus avoiding the common tendency to overlook some ADFs.

2. Literature Review 2.1 Factors Influencing Energy Consumption in Buildings There are various factors that can influence the energy consumption of a building. Many of these factors can be managed to improve building energy efficiency. For example, the American Society of Heating, Refrigerating, and Air- Conditioning Engineers (ASHRAE 90.1) [16] determined five main factors that

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affect energy consumption as follows: (1) building envelope; (2) mechanical systems; (3) water heating; (4) power generation systems and (5) lighting systems. The study by Yu et al. [17] stated that the factors influencing building energy consumption are divided into seven main categories: (1) climate characteristics; (2) building characteristics; (3) user characteristics; (4) services and operating systems; (5) occupants‟ behavior and activities; (6) social and economic factors; and (7) indoor environmental quality required. According to the International Energy Agency [18], the energy performance of the building envelope and its components (external walls, roofs, material insulation, windows etc.) has a critical energy impact and determine how much energy is required inside the building. The building envelope, which is responsible for 25% of the building energy, generally influences the heat exchange between the building and its environment by regulating inflow and outflow of heat control, air, moisture, and sound [9]. Reducing heat transfer through the building envelope leads to minimize energy use and make a significant reduction on the utility bills as well the environmental costs of fossil fuel use. A building envelope is a physical separator between the building internal and external environments. Studies have suggested that energy consumption can be reduced due to improving the envelope insulation material [19]. A proper choice of building envelope materials and design can also reduce cooling and heating loads [20]. For example, the envelope insulation material using efficient building external walls can reduce energy consumption by at least 33%, rising to 60% [21]. Bojic et al. [22] analyzed the thermal behavior of residential buildings with different characteristics envelope. The study found that improving the thermal insulation of the internal partitions separating air-conditioned and non-air-conditioned spaces was the most effective way of reducing cooling load. Similarly, increasing the insulation of the building envelope provides a better result in energy saving for heating than merely the impact of human behavior [23]. In addition, the building envelope shape can affect the annual energy consumption and performance of a building. For example, Tuhus-Dubrow and Krarti [24] used a simulation-optimization tool to optimize building envelope shape using alternatives shapes that included a rectangle, L-shape, T-shape, U-shape, H-shape, cross and trapezoid. Yoshino et al. [25] addressed that the envelope shape is one of 6

the most technical and physical factors that impact energy use. Ferrara et al. [26] developed a simple single-objective optimization model to reduce energy demand by using features that related to the building envelope (insulation thickness, glass type, window dimensions and solar shading devices). Natephra et al. [27] proposed a decision support system for designers used to select the proper envelope material that achieves optimum overall thermal transfer value for optimizing the energy-efficient design. The plane shape and size of a building have a significant impact on energy consumption [19, 28, 29]. Ibrahim [30] stated that the plan shape and the design complexity have a direct impact on the total operational and construction cost. The uses of narrow and complex layout shape of a building plan limit the direct gains of energy and consequently decrease the energy consumption. In addition, the orientation of a building properly is one of the vital energy factors since the building can receive a large solar heat [31]. Building orientation can influence energy consumption with respect to the use of heating/cooling, lighting systems, and glazing percentage amount [10, 29, 32, 33]. Therefore, in hot climates, south-facing windows must be designed to allow transmitting light and reflecting heat while in cold climates windows must allow the sun‟s heat and insulate against the cold [34]. Aksoy and Inalli [35] suggested that the proper optimization of both building orientation and shape may potentially lead to energy savings of 36% in buildings. While Spanos et al. [36] reported that proper building orientation and landscaping changes can reduce the energy requirements of a building by 20% through increasing the amount of daylight entering the internal space. A new study by Abanda and Byers [10] found that a well-oriented building can save a considerable amount of energy consumption throughout its life-cycle. The study confirmed that the best orientation of the building to reduce heat is (+180°) and the worst is (+45°). Windows glazing is responsible for a large amount of energy consumption in buildings due to its remarkably higher U-values compared to the other building envelope components [37]. According to Jelle et al. [38], windows are responsible for about 60% of total energy consumption in buildings. The glazing façade percentage and shape also affect energy consumption. Glazing has an essential construction material to construct low energy buildings and become critical to improving a building‟s sustainability. 7

For example, since the glazing facades allow maximum natural daylight into a building, the amount of solar heat gains or losses could be controlled depending on the glazing areas, shapes and specifications. In most circumstances, additional design approaches can be taken to offset the additional energy loss from the excessive amount of glazing. One of these design approaches is to set the total percentage area of wall, roof and doors windows to not exceed 25 % of the floor area to conserve building energy [39]. In addition, the double-glazed facades can reduce energy consumption to 50% of that of an equivalent building [40]. The double-glazed facade has a great ability to allow natural light, decrees the need for artificial light and the energy requirement for heating, cooling and ventilation. Furthermore, the utilization of well-designed sun-breakers as external shading devices in buildings could block the direct solar rays during hot summer months to reduce cooling load, while passes then during the cold winter months to decrease heating load [41]. El Zafarany et al. [41] suggested that the good utilization of sun-breakers has the ability to reduce building energy consumption by up to 31%. However, occupants' behavior such as occupants' activities and indoor environmental quality is also a factor that increases the level of energy required for the space cooling or heating in buildings [4244]. For example, the use of cooling/heating systems, space size and appliances will differ significantly between occupants with dissimilar behavior [45]. Santin et al. [46] investigated that occupants‟ behaviors significantly affect energy use in building by 4.2% while the building characteristics determine a large part of the energy use by 42%. Table 1 lists some recent studies that addressed several factors that affect the energy consumption of buildings in different countries and climates.

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Table 1. Studies addressed factors that affect the energy consumption of buildings Study

Location

Bazjanac (2010) [47]

United State

Ekici and Aksoy (2011) [48]

Turkey

Fokaides et al. (2011) [49]

Cyprus

Molin et al.( 2011) [50]

Sweden

Zhang et al. (2011) [51]

United Kingdom

Zhao and Magoulès (2011) [52]

France

El-Zafarany et al. (2012) [41]

Egypt

Menezes et al. (2012) [53]

United Kingdom

Pacheco et al. (2012) [32]

Spain

O‟Donnell et al. (2013) [54] Shrestha and Kulkarni (2013) [34]

United State United State

Aldossary et al. (2014) [5]

Saudi Arabia

De Wilde (2014) [55]

United Kingdom

Anderson et al. (2015) [56]

Germany

Fallahtafti and Mahdavinejad (2015) [29] Abanda and Byers (2016) [10]

United Kingdom

Shiel and West (2016) [57]

United State

Yoshino et al. (2017) [25]

Austria, Belgium, China, and Japan

D‟Orca et al. (2018) [58]

United State

Shiel et al.( 2018) [44]

United State

Iran

Energy Consumption Factors Space boundaries, building geometry Building form factor, transparency ratio, orientation, thermophysical properties of materials, distance to nearest buildings Occupant behaviour, heating and cooling loads, plant operation schedule Infiltration, internal heat gains, orientation Occupant behaviour, nighttime plug loads, appliances, control technologies, occupancy patterns Outside temperature, relative humidity, solar radiation, wind speed, appliance loads, space heating Windows sun-breakers Occupant patterns and behaviour, envelope thermal performance, plant/services control, facilities management Building orientation, shape, envelope system, passive heating and cooling mechanisms, shading, glazing Geometry, parameters required for automated BIM to BEM Total gross area, building occupants, windows type and position Building envelope, building location, occupant behaviour, local environment features Outdoor temperature (dependent on time, climate, and building use; gap is larger during colder periods) Form/shape, materials, structural systems, shared walls, area of heat transfer (thermal mass and thermal bridging) Building shape, building orientation Building orientation Lagged external temperature, HVAC plant operating efficiency, energy efficiency programs Building envelope, climate, building services, operations and maintenance, social factors, occupant activity, indoor environment Operations and maintenance, occupant activity, indoor environment Geometry, materials, glazing system, HVAC system, lighting system, equipment system, occupancy, adjacencies, weather

2.2 BIM Energy Simulation Tools BIM tools have a great ability which helps designers to select better design alternatives at the early stage as well as transfer efficiently and quickly the design information to energy and simulation tools for further process of validation and analysis [14]. One of the key benefits of BIM is that it integrates the 3-D model of a building with many analysis tools. BIM software tools can be used for modelling physical building elements and perform energy analysis to empower optimization techniques. Energy BIM modelling tools facilitate analysis and comparison of energy use through design configurations and can act as a decisionmaking tool for stakeholders to evaluate building life-cycle performance [59]. Energy analysis is affected

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by the building location, orientation, weather data, building usage, insulation, window glazing, Mechanical, Electrical and Plumbing (MEP) types, and user‟s consumption. A variety of energy simulation BIM software used in conjunction with energy simulation software allows the opportunity for sustainability measures and criteria performance analysis to be performed throughout the design process [60]. The BIM building model data is able to be integrated with several simulation software used for quantifying the energy of building performance such as Green Building Studio, eQUEST, Ecotect, Project Vasari, TRACETM 700, DOE2, and VE. This type of integration has the ability to enables these tools to be powerful decision-making tools used to design a high energy performance building [61]. However, Stumpf et al. [61] mad a multi-aspects comparison between some of these energy analysis tools as shown in Table 2.

Industry Foundation Class (IFC) Compliant

Energy Standard for Buildings

Required Outputs

Green Building Studio Energy Plus TRACETM 700 eQUEST EcotectTM VE-Ware DesignBuilder Carrier HAP DProfiler

Supports Early Design Analysis

Energy Analysis Tools

3D-CAD/BIM

Table 2. Comparison of building energy analysis tools (Stumpf et al. [61])

x

x

x

x x x x

x x

x x

x

x x

x x

x x x x x x x x x

x

For example, a study in Canada by Jrade and Jalaei [31] used Revit, Athena Impact Estimator, and Excel tools to estimate the full range of life cycle assessments parameters available through Athena Impact Estimator and energy LEED performance. While Lee et al. [62] utilized the Revit BIM model to obtain the environmental impact of building materials in Korean building life-cycle. In addition, Peng

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[63] integrated Revit with Ecotect to estimate CO 2 emissions for the whole life-cycle, while Shin and Cho [64] integrated Revit with ArchiCAD tool for the same purpose. In a recent study, Abanda et al. [65] simulated embodied energy and carbon emissions through BIM Model, Excel Templates, ICE Database, Navisworks manipulations, by using the tools of Revit, Navisworks, Excel, and Revit API. However, none of the previous researches studied the impact of key ADFs interacting on energy cooling consumption in hot climates which is addressed in this study. Autodesk® Ecotect® Analysis [66] is an energy simulation tool that is used to perform a wide range of comprehensive preliminary building energy performance simulations and it is highly compatible with 3-D BIM software, such as Autodesk Revit Architecture [67]. Ecotect allows the user to easily import building 3-D BIM model properties and related rooms information along with the raw geometry from Revit using Green Building XML schema (gbXML) format. In Ecotect, the user can perform three types of analysis including thermal, lighting and acoustic, which include hourly and monthly thermal space loads, natural and artificial lighting levels, reverberation time, and environmental impact [68, 69]. To confirm this, many studies have demonstrated the ability of Ecotect analysis to generate highly accurate results [70-72]. For example, Ecotect was used to simulate the thermal loads (heating and cooling) and CO2 emissions for the building life-cycle during the building operational stage, under its defined geometry, local weather conditions, floor plate depth, massing, material properties to ensure the accuracy of the results [63, 73, 74]. Since Ecotect has high competence to simulate building energy, in this research, Ecotect was used in to perform a comprehensive thermal loads energy simulation to compare between the design alternatives consumption loads and validate the proposed AEISS results.

2.3 Energy Simulation Modelling Energy modelling is the process of computerized representation of a building and its parameters that are used to perform energy simulation, while energy simulation is the process of predicting building energy performance through using software analysis [75].

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Energy simulation is a complex process that

comprises many computations affected by building parameters such as thermal properties, orientation, geometries, and envelope. Traditionally, most building energy simulation was performed by designers who used a variety of techniques to quantify and evaluate building performance in the design stage [76, 77]. The early design is the most critical phase to make decisions on design features that impact energy performance [78, 79]. Not using building performance analysis during the design process may lead to less efficient designs in terms of desired energy consumption. Therefore, performing energy analysis early by the design team is essential since it enables to identify energy-saving improvements [61]. Making different building configuration models to identify energy-saving alternatives does not typically occur due to the difficulty, expense, and the time needed [61]. In addition, preparing simulation models (i.e. input files) often results in numerous coding errors since it consists of manual or semi-manual translation from architectural model data to simulation data [80, 82]. To overcome this complexity, a piecemeal approach that involved simplifying assumptions and the omission of certain system parameters are involved [76]. BIM technology gives building owners and design teams affordable access to a full range of interactive building design decision-making tools used for enhanced energy conservation; thereby leveraging the benefits of BIM to construct sustainable and green buildings [9]. Recently, the A/E/C industry has an increasing interest in using the technology of building information modelling (BIM) in conjunction with energy simulation in the design phase. As BIM is capable of storing, updating and extracting data that can be analyzed during the design phase to improve the decision- making process [8386], it also has the powerful ability to leverage the information stored in these models to perform energy simulations. BIM can provide an effective way to enable the integrated model design and energy simulation assessment over the building‟s life-cycle process [87, 90]. Furthermore, BIM-based models enable designers to produce different energy-saving alternatives in early design while avoiding the time-consuming process of re-entering building parameters and analysis information [61]. As well, these BIM-based models help to compare the building performance implications alternatives for building forms, designs, materials, and mechanical systems. Integrating a 12

BIM model into a decision-making tool helps to enable useful decisions in the early project design stage and allows detailed sustainability analysis by referring to real project data [91]. Such an approach also enables designers to promote energy design alternatives by incorporating cost-effective decision-making which impacts overall building energy costs.

3. Research Methodology To develop a simple scoring system that facilitates architects on selecting an energy-related optimum design, this research approach is consists of two main parts: (1) developing the proposed AEISS model to be used by designers at early design phase; and (2) performing energy BIM simulation to justify the output of AEISS. The main seven steps were conducted as follows (detailed steps are shown in Fig.1): (1) Identify the key ADFs that suit buildings in hot climate regions which have a high impact on energy consumption; considering external and internal building characteristics; (2) Develop a set of categories and further design options for each ADF to be fitted with hot climates features and suit with different architects‟ trends. Accordingly, crate each design option 3-D model for illustration purpose; (3) Develop the proposed scoring system by assessing the impact of each design options on energy performance based on the expert architects inputs which are very familiar with the requirements in that hot climates projects; (4) Create and evaluate different design alternatives using a real case study of a residential building in Saudi Arabia generate overall scores; (5) Construct 3-D BIM models for the design alternatives using Revit software to take building information, both geometric and non-geometric such as physical and material properties, from the BIM model and translates it into a gbXML format. This step facilitates the process of transferring the model to the Autodesk Ecotect Analysis software for thermal load analysis.

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(6) Perform several thermal load analyses to the design alternatives by Ecotect BM simulation and then evaluate design energy efficiency of the design alternatives based on its combination. This information can demonstrate the effects of changing ADFs on the estimated building cooling energy; and (7) Compare the simulation results with AEISS outputs, the cooling consumption loads, and expected monthly cost-efficiency. The importance of this step is to verify the results of the proposed system and compare them with the energy results generated from the BIM.

Developing the AIESS Model

Step 1

1. Perform a literate to investigate ADFs and their impact 2. Interview with domain experts to identify key ADFs

Identifying Key ADFs Influence on Energy

Step 2

1. Develop different ADFs categories/perspectives 2. Develop several design options for each ADF 3. Create 3-D models of the design options

Develop AIESS Categories and Options

Step 3

1. Assess the impact of design options on cooling loads 2. Set each design option (+/-) impact values 3. Evaluate ADFs by calculating importance indices

Quantify ADFs Energy Cooling Loads Impact

Step 4

1. Create different design alternatives using a case study 2. Calculate each alternative total score by AIESS

Create and Evaluate Designs Alternatives

Performing Energy BIM Simulation

Step 5 Construct Alternatives BIM Models by Revit

Revit

Step 6 Perform Energy Simulation by Ecotect

Step 7 Evaluate and Compare Alternatives Energy

Ecotect

1. Construct the BIM models of design alternatives 2. Export Revit-based models by gbXML format 3.Import the models to Ecotect for the simulation process

1. Set design alternatives thermal loads properties 2. Perform losses/gains and cooling degree days analysis 3. Calculate cooling loads needed by HVAC

1. Specify heat losses/gains sources and amounts 2. Compare the alternatives monthly cooling loads 3. Calculate and compare monthly costs for cooling loads

Fig. ‎1. Research methodology steps

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4. Architectural-Based Energy Impact Scoring System (AEISS) 4.1 Proposed ADFs, Categories, and Design Options At the early design phase to evaluate the energy efficiency of buildings, this research identified seven ADFs that have a significant impact on cooling loads consumption in hot climates; based on the literature and structured interviews with 10 domain experts from different 7 companies in Saudi Arabia. The selected experts have different education levels and wide experience ranged from 10 to 25 years in green buildings design and construction for both public and privet sectors. During the interviews, the experts were asked to select the key ADFs that have the highest impact on energy performance. The experts indicted 7 ADFs out of given 12 ADFs. These 7 ADFs are (1) Building orientation; (2) building envelope; (3) plan shape and complexity; (4) storey and height; (5) windows and glazing; (6) floor spans; and (7) circulation space. Furthermore, each design feature has several categories/perspectives (all considered in a design, not mutually exclusive), and each perspective has possible options (mutually exclusive). The ADFs, perspectives, options, and descriptions are shown in Table 3. The proposed AEISS has a total of 40 design features options which fit all possible design considerations and orientations and developed for three building types: (1) residential buildings (2) commercial buildings, and (3) educational buildings. To create and evaluate a complete design alternative by AEISS, the designer/engineer has to select only 14 design parameters out of the 40 design options, as shown in Table 3. For visualization purpose, Fig. 2 Parts (a) and (b) illustrate the 3-D models of the ADFs options to help the user to select the design parameters for each design being evaluated.

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Table 3. Architectural Design Features (ADFs) categories and descriptions ADFs Combinations 1. Building Orientation a. Related to Wind and Sun

Design Options Descriptions The building site sitting orientation to face the likely and unlikely winds

- Preferred (North Western) - Not Preferred (South Eastern)

- Building oriented to likely wind side that receives a small amount of sun radiation - Building oriented to unlikely wind side that receives a large amount of sun radiation

b. Related to View

The building site sitting orientation to have natural or not natural views

- Good Natural View - Bad Natural View

- Building spaces have a wide good natural view (Garden-Sea-Main Street, etc.) - Building spaces have a bad or no natural view (Neighborhood- Small Street, etc.)

2. Building Envelope a. Building Massing Form

The degree of how different building masses interlocking to the whole form

- Simple (Regular) - Normal (Moderate) - Complex (Sharp)

- Simple surface masses interlocking, straight roofs - Normal masses interlocking, straight roofs - Sharp masses interlocking, inclined roofs

b. Facade Material

The exterior wall materials used to enclose the building façade and form

- Precast Concrete - Block (Concrete) - Brick (Stone)

-12" (305 mm), Precast concrete, U = 0.55 -12" (305 mm), Concrete block, solid, U = 0.36 -12" (305 mm), Brick stone, U = 0.31

c. Glazing Percentage (G/W Ra)

The ratio of façade glazing area to the same façade of wall area

- Small (<20%) - Medium (20-50%) - Large (>50%)

- Small glazing façade area with G/W Ratio = <20% - Average glazing façade area with G/W Ratio =20-50% - Complete glazing façade area or, G/W Ratio = >50%

3. Plan shape and Complexity a. Plan Efficiency (W/F Ratio)

The ratio of building exterior walls area to the building Gross Floors Area (GFA)

- Not-Efficient (<70%) - Acceptable (70-90%) - Efficient (>90%)

- Less sitting and space efficiency, Less external walls, More economical design - Normal sitting and space efficiency, Normal external walls, Normal economical design - More sitting and space efficiency, More external walls, Less economical design

b. Plan Shape Complexity

The proportion degree of building plan dimensions and its setting out

- Simple (Regular) - Normal ( Moderate) - Complex (Irregular)

- Symmetrical shape : Square, Rectangular, Circular, Hexagonal, Octagonal, etc. - Combined shape: Regular shape interlocking with irregular, narrow, or curvy shapes - Complex shape: Irregular shape interlocking with irregular, narrow, curvy shapes

4. Storey and Height a. Number of Storey

The number of storeys that building contains to the same floors area

- Low-Rise (1-2 Storeys) - Medium-Rise (3-4 Storeys) - High-Rise (>4 Storeys)

- 1-2 Storeys: Ground and First floors, or only one floor - 3-4 Storeys: Ground, First, Second, and Third floors, or only three floors - >4 Storeys : Ground, First, Second, Third, Fourth, and more floors

b. Average Storey Height

The different range of the storeys heights that give the average building height

- Low (<3.00m) - Normal (3.00-4.00m) - High (>4.00m)

- Average storeys heights of the building are less than average range - Average storeys heights of the building are within average range - Average storeys heights of the building are more than average range

5. Windows and Glazing a. Glazing Shape

The outline configuration shape of different building façade windows

- Regular Shape - Semi-Regular Shape - Irregular Shape

- Simple Shapes, Single Arranged, Determined Size - Overlapping Shapes, Multiple Arranged, Determined Size - Overlapping Shapes, Multiple Not-Arranged, Undetermined Size

b. Glazing Efficiency

The glazing elements features (Panel number, Reflectivity, Thermal Break, U-value)

- Low Efficiency - Medium Efficiency - High Efficiency

- Single sheet aluminum glazing, Not reflective (<30%), No thermal break, U = (>0.80) - Double sheets aluminum glazing, Semi-Reflective (30-70%), Thermal break, U = (0.50-80) - Triple sheets low-e coating aluminum glazing, Reflective (>70%), Thermal break, U = (<0.50)

c. Sun-Breakers Geometry

The sun-breakers panels configuration and shading areas

- Simple Shape (1-Panle) - Normal Shape (2- Panels) - Complex Shape (3- Panels)

- One panel: vertical or horizontal panel, one-side shading area - Two panels: vertical and horizontal panels, two-sides shading areas - Three panels: two vertical and one horizontal panels, complete shading areas

6. Floor Spans a. Span Dimension

The longest distance of usable area between exterior wall and fixed interior element

- Short (<4.50m) - Medium (4.50-6.50m) - Long (>6.50m)

- Less span efficiency, Normal economical design, Less esthetics - More span efficiency, More economical design, Normal esthetics - Normal span efficiency, Less economical design, More esthetics

7. Circulation Space a. Circulation Area (C/F Ratio)

The ratio of building circulation space area to the building Gross Floors Area (GFA)

- Low (<15%) - Normal (15-25%) - High (>25%)

Entrance halls, Corridors, Stairways, and Lift wells areas are less than average range Entrance halls, Corridors, Stairways, and Lift wells areas are within average range Entrance halls, Corridors, Stairways, and Lift wells areas are more than average range

16

b. Related to View Not Preferred (South Eastern)

a. Building Form

Preferred (North Western)

Bad Natural View (Neighborhood)

Normal (Moderate)

Block (Concrete)

Small (<20%)

Medium (20-50%)

Large (>50%)

18%

31%

54%

Not-Efficient (<70%)

Acceptable (70-90%)

62%

84%

Simple

Brick (Stone)

Efficient (>90%) 105%

Medium Complexity

Complex

c. Glazing Percentage (G/W)

Precast Concrete

b. Façade Material

Complex (Sharp)

a. Plan Efficiency (W/F)

3. Plane Complexity

2. Building Envelope

Simple (Regular)

b. Plan Shape

1. Building Orientation

Good Natural View (Main Street)

a. Related to Wind and Sun

Part (a): 3-D models of the building orientation, building envelope, and plane complexity

17

4.50 m 2.92 m

Normal (3.00-4.00m)

b. Average a. Number of Storey Height Storey

High-Rise (>4 Storeys)

3.67 3.67 m m

Low (<3.00m)

High (>4.00m)

Regular Shape

Semi-Regular Shape

Irregular Shape

Single sheet Reflectivity = <30% U = >0.80

Double sheets Reflectivity = 30-70% U = 0.50-0.80

Triple sheets Reflectivity = >70% U = <0.50

Low Efficiency

Medium Efficiency

High Efficiency

c. Sun-Breakers Geometry

5. Windows and Glazing

Medium-Rise (3-4 Storeys)

a. Window Shape

Low-Rise (1-2 Storeys)

4 Floors

b. Glazing Efficiency

4. Storey and Height

6 Floors 2 Floors

Simple (1-Panel) Normal (2-Panels)

3.55 m

7.30 m

6.50 m

Long (>6.50m)

Normal (15-25%)

High (>25%)

28% 28%

Medium (4.50-6.50m)

20.8% 20.8%

Low (<15%)

Short (<4.50m)

13.6% 13.6%

7. Circulation 6. Floor Spans Space (C/F)

Complex (3-Panels)

Part (b): 3-D models of the storey and height, windows glazing, floor spans, and circulation space

Fig. 2. ADFs 3-D models for the developed design options 18

4.2 Quantifying the Impact of ADFs on Energy Consumption To determine the contribution of each ADF options in terms of energy consumption, a five-point scale is used as shown in Table 4. The scale allows for both positive and negative impact to be specified, as well as the degree of impact both in letters (VH, H, M, L, and VL) and also in equivalent numbers. For example, Very High positive impact, means an impact value of +0.90, while a Very High negative impact means an impact value of -0.90. Table 4. Five-point assessment scale No. 1 2 3 4 5

Scale Very High (VH) High (H) Medium (M) Low (L) Very Low (VL)

Positive Impact (+VH) (+H) (+M) (+L) (+VL)

Discerption Extremely Important /Reliable/Severe Very Important/Reliable/Severe Important/Reliable/Severe Somewhat Important/Reliable/Severe Not Important/Reliable/Severe

Value +0.90 +0.70 +0.50 +0.30 +0.10

Negative Impact (-VH) (-H) (-M) (-L) (-VL)

Value -0.90 -0.70 -0.50 -0.30 -0.10

Importance Value 5 4 3 2 1

Using this scale, the 10 expert architects provided the assessment values shown in Table 5 for the ADF options. The assessments made based on the characteristics of the hot climates. In the table, the average values for the experts‟ assessments are calculated and adjusted to the nearest point in the scale to determine the final assessments for ADF options. The adjustment is done based on the judgment of the researchers. Based on the assessments values made in Table 5, the energy importance index for each design feature is calculated using equation (1). 5

Importance Index

5

(∑

⁄5 ∑

1

1

) *100

Where, = 1 to 5 (scale); importance value; = frequency of assessment at scales i, as follows: frequency of “Very Low” assessments; =1; frequency of “Low” assessments; = 2; frequency of “Medium” assessments; = 3; frequency of “High” assessments; =4; frequency of “Very High” assessments; =5

19

Table 5. The expert architects assessment for the ADFs impact on energy ADF/Expert

E.1

E.2

E.3

E.4

E.5

E.6

E.7

E.8

E.9

E.10

Average Value

Adjusted Value

+0.90 -0.90

+0.90 -0.70

+0.70 -0.90

+0.90 -0.70

+0.90 -0.90

+0.90 -0.90

+0.90 -0.90

+0.70 -0.90

+0.50 -0.70

+0.90 -0.90

+0.82 -0.84

+0.90 -0.90

-0.30 +0.70

-0.30 +0.50

-0.50 +0.70

-0.30 +0.70

-0.50 +0.70

-0.30 +0.70

-0.50 +0.70

-0.50 +0.50

-0.50 +0.70

-0.70 +0.70

-0.44 +0.66

-0.50 +0.70

-0.30 -0.10 +0.70

-0.30 -0.30 +0.50

-0.50 -0.30 +0.50

-0.90 -0.50 +0.90

-0.50 -0.30 +0.70

-0.50 -0.10 +0.50

-0.90 -0.30 +0.70

-0.50 -0.50 +0.70

-0.50 -0.30 +0.50

-0.70 -0.50 +0.70

-0.56 -0.32 +0.64

-0.50 -0.30 +0.70

-0.70 +0.30 +0.90

-0.70 +0.10 +0.90

-0.50 +0.70 +0.90

-0.50 +0.30 +0.50

-0.70 +0.30 +0.70

-0.50 +0.50 +0.70

-0.50 +0.70 +0.90

-0.50 +0.30 +0.90

-0.70 +0.70 +0.90

-0.70 +0.70 +0.90

-0.60 +0.46 +0.82

-0.50 +0.50 +0.90

+0.90 -0.70 -0.90

+0.70 -0.70 -0.90

+0.90 -0.50 -0.90

+0.90 -0.70 -0.90

+0.90 -0.50 -0.90

+0.70 -0.50 -0.70

+0.90 -0.70 -0.90

+0.90 -0.50 -0.90

+0.90 -0.70 -0.90

+0.90 -0.70 -0.90

+0.86 -0.62 -0.88

+0.90 -0.70 -0.90

-0.30 +0.50 +0.70

-0.70 +0.70 0.90

-0.50 +0.50 +0.70

-0.30 +0.50 +0.70

-0.30 +0.70 +0.90

-0.50 +0.70 +0.90

-0.70 +0.50 +0.90

-0.50 +0.70 +0.90

-0.50 +0.70 +0.90

-0.50 +0.50 +0.90

-0.48 +0.60 +0.84

-0.50 +0.70 +0.90

-0.90 +0.50 +0.90

-0.70 +0.50 +0.70

-0.90 +0.50 +0.90

-0.90 +0.70 +0.90

-0.90 +0.50 +0.90

-0.70 +0.70 +0.90

-0.90 +0.50 +0.70

-0.90 +0.50 +0.90

-0.70 +0.50 +0.90

-0.90 +0.30 +0.90

-0.84 +0.52 +0.86

-0.90 +0.50 +0.90

+0.90 -0.30 -0.70

+0.70 -0.50 -0.70

+0.90 -0.50 -0.70

+0.50 -0.70 -0.90

+0.70 -0.50 -0.70

+0.90 -0.70 -0.90

+0.90 -0.70 -0.90

+0.70 -0.50 -0.70

+0.70 -0.30 -0.90

+0.90 -0.50 -0.70

+0.78 -0.52 -0.78

+0.70 -0.50 -0.70

+0.70 -0.50 -0.70

+0.70 -0.50 -0.90

+0.70 -0.70 -0.90

+0.70 -0.50 -0.70

+0.50 -0.70 -0.90

+0.70 -0.30 -0.50

+0.90 -0.50 -0.70

+0.70 -0.30 -0.50

+0.50 -0.50 -0.70

+0.50 -0.50 -0.90

+0.66 -0.50 -0.74

+0.70 -0.50 -0.70

-0.50 +0.50 +0.70

-0.70 +0.70 +0.90

-0.50 +0.30 +0.50

-0.70 +0.50 +0.70

-0.90 +0.30 +0.50

-0.70 +0.30 +0.50

-0.70 +0.50 +0.70

-0.70 +0.50 +0.70

-0.90 +0.50 +0.70

-0.70 +0.50 +0.70

-0.70 +0.46 +0.66

-0.70 +0.50 +0.70

-0.90 +0.70 +0.90

-0.90 +0.50 +0.90

-0.90 +0.70 +0.90

-0.90 +0.70 +0.90

-0.90 +0.70 +0.90

-0.70 +0.70 +0.90

-0.90 +0.50 +0.90

-0.90 +0.70 +0.90

-0.90 +0.70 +0.90

-0.90 +0.70 +0.90

-0.88 +0.66 +0.90

-0.90 +0.70 +0.90

+0.30 +0.50 +0.70

+0.30 +0.50 +0.90

+0.50 +0.70 +0.90

+0.50 +0.70 +0.90

+0.50 +0.70 +0.90

+0.30 +0.70 +0.90

+0.30 +0.70 +0.90

+0.30 +0.50 +0.70

+0.50 +0.70 +0.90

+0.50 +0.70 +0.90

+0.40 +0.64 +0.86

+0.50 +0.70 +0.90

+0.70 +0.50 -0.50

+0.70 +0.50 -0.70

+0.30 +0.50 -0.50

+0.70 +0.30 -0.50

+0.50 +0.50 -0.70

+0.70 +0.30 -0.70

+0.50 +0.30 -0.90

+0.70 +0.50 -0.70

+0.70 +0.50 -0.70

+0.50 +0.30 -0.50

+0.60 +0.42 -0.64

+0.70 +0.50 -0.70

+0.70 -0.50 -0.90

+0.50 -0.50 -0.90

+0.90 -0.50 -0.70

+0.70 -0.50 -0.90

+0.70 -0.30 -0.70

+0.90 -0.50 -0.70

+0.50 -0.70 -0.90

+0.90 -0.50 -0.70

+0.70 -0.30 -0.90

+0.50 -0.70 -0.90

+0.70 -0.50 -0.82

+0.70 -0.50 -0.90

1. Building Orientation a. Related to Wind and Sun - Preferred (N-W) - Not Preferred (S-E)

b. Related to View - Good Natural View - Bad Natural View

2. Building Envelope a. Building Massing Form - Simple (Regular) - Normal (Moderate) - Complex (Sharp)

b. Facade Material - Precast Concrete - Block (Concrete) - Brick (Stone)

c. Glazing Percentage (G/W) - Small (<20%) - Medium (20-50%) - Large (>50%)

3. Plan Shape & Complexity a. Plan Efficiency (W/F) - Not-Efficient (<70%) - Acceptable (70-90%) - Efficient (>90%)

b. Shape Complexity - Simple (Regular) - Normal (Moderate) - Complex (Irregular) 4. Storey and Height

a. Number of Storey - Low-Rise (1-2 Storeys) - Medium-Rise (3-4 Storeys) - High-Rise (>4 Storeys)

b. Average Storey Height - Low (<3.00m) - Normal (3.00-4.00m) - High (>4.00m)

5. Windows and Glazing a. Glazing Shape - Regular Shape - Semi-Regular Shape - Irregular Shape

b. Glazing Efficiency - Low Efficiency - Medium Efficiency - High Efficiency

c. Sun-Breakers Geometry - Simple Shape (1-Panle) - Normal Shape (2-Panels) - Complex Shape (3- Panels)

6. Floor Spans a. Span Dimension - Short (<4.50m) - Medium (4.50-6.50m) - Long (>6.50m)

7. Circulation Space a. Circulation Area (C/F) - Low (<15%) - Normal (15-25%) - High (>25%)

20

The results of the ADFs evaluation are shown in Table 6. Based on these results, “building orientation” has the most significant impact on energy consumption, with 85% importance index. This is because the proper positioning of a building with respect to the sun, especially in hot regions, increases the energy efficiency by reducing the required cooling loads. On the contrary, “floor spans” and “storey and height” are the design features with the lowest impact, with 73.33% importance index. Typically, for example, increasing floor spans or the space area is only a functional use consideration and it does not have a high relation with energy consumption. The close values of the importance of indices are also an indication that they represent significant options for the decision-maker to choose from.

Table 6. Summary of the importance indices for the ADFs based on Energy Impact No.

ADF

1 2 3 4 5 6 7

Building Orientation Building Envelope Plan shape and Complexity Storey and Height Window Glazing Floor Spans Circulation Space

Number of Assessments 4 9 6 6 9 3 3

Importance Index (%)

Rank

85.00 75.56 83.33 73.33 82.22 73.33 80.00

1 5 2 6 3 6 4

4.3 Optimum Energy Design Parameters Further analysis is conducted in order to select the design combination that can optimize certain energy performance measures. The AEISS optimum energy of the 14 design parameters that achieve the highest positives scores of the building energy performance is weighted with an overall score of 11.20 as illustrated in Table 7. In most cases, optimum energy design features cannot be selected because of various practical constraints.

For instance, factors such as site location and shape, owner‟s requirements, space

functionality, owner‟s budget, have a strong influence on the selection of the architectural design parameters. Thus, the architects‟ role is to try to select the ADFs options that enhance energy efficiency through the combinations of these options.

21

Table 7. Optimum ADFs parameters based on energy efficiency and consumption ADFs and Categories 1. Building Orientation a. Related to Wind b. Related to View 2. Building Envelope a. Building Form b. Facade Material c. Glazing Percentage (G/W RA) 3. Plan Shape and Complexity a. Plan Efficiency (W/F RA) b. Shape Complexity 4. Storey and Height a. Number of Storeys b. Storey Height 5. Windows and Glazing a. Glazing Shape b. Glazing Efficiency c. Sun-Breakers Geometry 6. Floor Spans a. Span Dimension 7. Circulation Space a. Circulation Area (C/F RA)

Optimum Design Parameters

Score

Likely Wind (N-W) Bad Natural View

+0.90 +0.70

Complex (Sharp) Brick/Stone Small (<20%)

+0.70 +0.90 +0.90

Efficient (>90%) Complex (Irregular)

+0.90 +0.90

Low-Rise (1-2 Storeys) Low (<3.00m)

+0.70 +0.70

Irregular Shape High Efficiency Complex (3-Panels)

+0.70 +0.90 +0.90

Short (<4.50m)

+0.70

Low (<15%) Overall Weighted Score

+0.70 11.20

5. AEISS Implementation and Validation 5.1 Case Study through the AEISS A simple user-friendly program for the proposed AEISS was developed using C sharp programing language and has three main functions. The first function of this program is “Designs Evaluation”, the second one is “Best Sub-Criteria”, and the third one is “Setting Values” as shown in Fig. 3. The main objective of this program is to facilitate the using of the AIESS by automatically generating the assessment values of the design alternatives. In Fig.4, the user must first add the number of design alternatives to be evaluated (maximum of 10 alternatives) and then the program will automatically create the evaluation table for these alternatives. Afterwards, the program would enable the user/decision-maker to evaluate the design alternatives he/she made through only inserting the 14 parameters/characteristics (out of the 40 design options) of each design by using the drop-down menu or design options 3-D models as shown in Fig.4.

22

Fig.3. User interface of the AEISS program

Fig.4. Selection of each design alternatives parameters 23

To implement and validate the proposed AEISS, firstly, a real case study project of three design alternatives of residential villa building located in Jeddah city (Saudi Arabia) is selected. The site area of the project is 4,400 m2 and surrounded by three buildings with having one elevation facing the main street. Secondly, 3-D BIM models of the alternatives were created by Revit software and then integrated with Ecotect analysis to perform three types of simulations: (1) heat breakdown losses and gains, (2) cooling degree days, and (3) HVAC cooling loads. The results of these simulations were compared with AEISS system to test its applicability as a scoring system for optimizing energy cooling loads consumption in hot climate regions. The main advantage of the developed AEISS is that it can be utilized in two different ways: (1) before preparing the design, to create low consumption design alternatives; (2) after preparing the design alternatives, to be included with the other criteria when comparing between the alternatives. Based on the client functional requirements, three different design alternatives (A, B, and C) of the villa building were proposed to be evaluated in terms of energy consumption. Each alternative consists of 2,547 m² total gross floor area, 3-floor plans, 103 spaces, and 18 person occupancy. The 14 design parameters of each design alternative are shown in Table 8.

Table 8. Design alternatives parameters that selected for the case study a. Related to Wind and Sun b. Related to View a. Building Form b. Facade Material c. Glazing Percentage (G/W) a. Plan Efficiency (W/F) b. Shape Complexity a. Number of Storey b. Average Storey Height a. Glazing Shape b. Glazing Efficiency c. Sun-Breakers Geometry a. Span Dimension a. Circulation Area (C/F)

Design A

Design B

Design C

Unlikely Wind (N-E) Good View Simple Precast (Concrete) Medium (20-50%) Acceptable (70-90%) Simple M-Rise (3-4 Storeys) Normal (3.00-4.00m) Irregular Medium Simple (1-Panel) Medium (4.50-6.50m) High (>25%)

Likely Wind (N-W) Good View Normal Block (Concrete) Medium (20-50%) Efficient (>90%) Normal M-Rise (3-4 Storeys) High (>4.00m) Regular Low Normal (2-Panels) Long (>6.50m) High (>25%)

Likely Wind (N-W) Good View Normal Brick (Stone) Small (<20%) Efficient (>90%) Normal M-Rise (3-4 Storeys) High (>4.00m) Semi-Regular High Normal (2-Panels) Long (>6.50m) Normal (15-25%)

24

After selecting the 14 parameters of the three design alternatives (A, B and C) in the evaluation window as user‟s inputs, then, the system automatically generates the alternatives final alternatives scores in terms of the building energy performance as shown in Fig. 5.

Fig.5. Evaluation scores for the selected three design alternatives

Each design alternative has its score which is the sum of the impact values of its combination. It shows that design alternative C got the highest scores of 3.00, while design alternatives A and B obtained scores of -2.80 and -2.40 respectively. The negative scores of design alternatives A and B illustrate that both designs have excessive energy consumptions and require more cooling loads to reach thermal comfort. Thus, design C is the best design that achieves lower required cooling loads. By using this system, the user then can optimize the design by selecting the design features which have a high positive impact on energy as shown in Fig.6. This flexibility of the developed system is actually one of the strong features of the developed AEISS. The user can easily combine any group of design options to create multiple design alternatives, select the best design, or maximize the design alternative by selecting other high positive impact design options which could lead to optimizing the design.

25

Fig.6. Energy optimum design features The third function of the developed program is “Setting Values” and contains only one window as sown in Fig.7. The assessments values of each design (40 options) can be edited by the user on each values box. The user can reset the default data by pressing the green button of the reset. The new sum of these values will be automatically generated if any change has been made. This function makes this program fully flexible for other types of climates and buildings.

Fig. 7. Assessment values of the ADFs options in the AEISS 26

5.2 Design Alternatives 3-D BIM Models Using Autodesk Revit Architecture software, the 3-D BIM models of the design alternatives (A, B and C) were developed to visualize and embed each alternative physical properties before they would be exported to Ecotect and performing energy simulation. As shown in Figs. 8 and 9, the design 3-D perspectives and elevations show the design features such as storey height, envelope complexity, glazing percentage and shape, façade material, and sun-breakers configurations. The importance of this step is to define each design alternatives elements to be compatible and recognized by the Ecotect when performing energy simulation.

27

Fig. ‎8. Design alternatives perspectives BIM models and features

Fig. ‎9. Design alternatives front and back elevations BIM models After exporting alternatives‟ models and weather data file of Jeddah city to Ecotect, the thermal properties were set to reflect thermal design loads and the amount of cooling energy required to reach the comfort level. As shown in Table 9, the alternatives have fixed design conditions related to location, weather data, number of storey and spaces, gross floor areas and occupancy, thermostat range and HVAC operation hours. At the same time, elements of ADFs such as building height and volume, the percentages of plan efficiency, area glazing and circulation space are different and vary from alternative to another.

28

The purpose of creating a similar internal environment is to measure the impact of changes of ADFs on energy consumption as well as validate AEISS scoring result.

Table 9. Design alternatives physical features and energy analysis properties Analysis Property Weather/ Location Building Type Site Area Number of Storey Number of Spaces Gross Floor Areas (F) Total Floors Height Cooling Spaces Volume Exterior Wall Arear (W) % Plan Efficiency (W/F) Glazing Area (G) Glazing Percentage (G/W) Circulation Space Area (C) % Circulation Area (C/F) Building Occupancy Internal Condition Infiltration (Exchange) Rate Active System Thermostat Range HVAC Hours of Operation Number of Hours

Design A

Design B Design C Jeddah/Saudi Arabia Residential Villa 4,400 m2 3-Floor Plans (Ground Floor, First Floor and Attic Floor) 103 spaces (89 are cooled and 14 are not cooled) 2,547 m² 11.40 m 12.60 m 14.05 m 9,610 m³ 10,784 m³ 12,172 m³ 1782.05 m² 2511.70 m² 2798.61 m² 70.00 % 98.61 % 109.88 % 460.02 m² 573.47 m² 502.06 m² 25.81 % 22.83 % 17.94 % 846 m² 675 m² 603 m² 33.22 % 26.50 % 23.67 % 18 Persons (Sedentary-70w for their biological heat output) Humidity (60%), Air Speed (0.50 m/s), Lighting Level= 300 lux Air Change Rate (0.50/hour), Wind Sensitivity (0.25/hour) Cooling Only (95% Efficiency) Lower Band= 18C, Upper Band= 26C Weekdays (from 5 PM to 7 AM), Weekends (from 10 PM to 4 PM) Weekdays= 14 hours/day, Weekends= 18 hours/day , 106 hours/week

5.3 Thermal Loads Analysis in Ecotect The thermal load analysis is performed to calculate the amount of energy required to be added (in hot climate regions) or removed (in cooled climate regions) from buildings‟ spaces by the HVAC system to keep occupants comfortable. The comfort of occupants can be achieved by keeping the temperature at a specified level. Thus, it requires setting up a proper HVAC system which provides the heating and cooling loads within the space. From the sustainability aspect, high-performance buildings are always seeking to reduce the required thermal loads as much as possible and meet these loads as efficiently as possible. Thermal loads in the building are divided into two types: (1) external thermal loads transferred from the outside of the building (sun, weather, earth) to the interior spaces through the building envelope. (2) Internal thermal loads which are generated from people bodies, lighting and house equipment.

29

Ecotect Analysis software is used to evaluate the information gained from the thermal load analysis of each alternative and transfer those load measurements into cooling loads needed by the HVAC system. That process can help the designer to reduce the thermal loads, reach thermal comfort and then transfer those cooling load into the monthly cost. The environment of Ecotect Analysis software acts as an interactive analysis tool that allows building designers to simulate building performance from the conceptual design to evaluate the alternatives and also to the construction phase to examine the energy performance of the existing building. The main advantage of the software is that it has the ability to combines analysis functions along with an interactive display that presents analytical results directly within the context of the 3-D building model. After developing the design alternatives, exporting 3-D BIM models from Revit can be easy to gbXML format and imported directly into Ecotect Analysis for the simulation process. Based on the 3-D BIM models and location weather data file, building masses and geometry can be combined with site analysis in Ecotect to determine optimal location, building shape, orientation, material and windows glazing based on environmental factors. As an example, Fig.10 shows the Design C 3-D BIM model in Ecotect after the importing process from Revit. In the next section, the BIM simulation process and detailed analysis are applied to predict the energy performance of the design alternatives.

Fig. 01. Design C 3-D BIM model in Ecotect showing its ADFs design options 30

5.4 BIM based-Energy Simulation and Analysis 5.4.1 Passive Heat breakdown Losses and Gains Analysis Heat transmits between the interior and exterior of the building through building envelope elements (roofing, partitions, windows and skylight). Heat energy tends to flow from higher temperatures zones to lower zones simultaneously through six sources: (1) building fabric (conduction); (2) indirect solar exposure (sol-air); (3) solar direct heat; (4) ventilation; (5) internal heat; and (6) internal zonal heat. The rate of heat flow by any of these six sources is determined by the temperature difference between the building zones which is called passive heat losses and gains. Thus, controlling building heat exchange processes (losses and gains) with the outdoor environment plays a major role in regulating the indoor environment. Optimizing ADFs such as building envelope, building materials, windows glazing efficiency and sun-breakers can provide advantages in controlling the amount of heat exchange in the building operation. By using Ecotect, monthly heat breakdown losses and gains analysis for the design alternatives (A, B and C) were applied as shown in Fig.11. Wh/ m2

GAINS BREAKDOWN - All Visible Thermal Zones

1st January - 31st December

% 26.1%

2240 1680 48.2% 1120 560

8.6% 8.4%

0 560

Overall Gains/ Losses

1120 1680 2240

Design A

2800 14th Jan Conduction

Wh/ m2

28th 14th Feb

28th 14th Mar Sol-Air

28th Apr

14th

28th 14th May Direct Solar

28th 14th 28th Jun Jul Ventilation

14th

28th 14th Aug Internal

28th 14th 28th Sep Oct Inter-Zonal

14th

GAINS BREAKDOWN - All Visible Thermal Zones

28th 14th Nov

28th 14th Dec

99.9%

28th

1st January - 31st December

% 14.7%

2160 48.1%

1620 1080

7.5% 17.2%

540

11.9% 0 540

Overall Gains/ Losses

1080 1620 2160

Design B

2700 14th Jan Conduction

28th 14th Feb

28th 14th Mar Sol-Air

28th Apr

14th

28th 14th May Direct Solar

28th 14th 28th Jun Jul Ventilation

14th

31

28th 14th Aug Internal

28th 14th 28th Sep Oct Inter-Zonal

14th

28th 14th Nov

28th 14th Dec

28th

99.7%

Wh/ m2

GAINS BREAKDOWN - All Visible Thermal Zones

1st January - 31st December

%

2000 56.0% 1500 1000

9.8% 11.5%

500 16.6% 0 500

Overall Gains/ Losses

1000 1500 2000

Design C

2500 14th Jan Conduction

28th 14th Feb

28th 14th Mar Sol-Air

28th Apr

14th

28th 14th May Direct Solar

28th 14th 28th Jun Jul Ventilation

14th

28th 14th Aug Internal

28th 14th 28th Sep Oct Inter-Zonal

14th

28th 14th Nov

28th 14th Dec

97.9%

28th

Fig. 00. Monthly heat breakdown losses and gains analysis The importance of this step is to quantitatively compare the amount of heat losses and gains of the design alternatives in order to recognize the impact of the ADFs on the heat sources as sown in Table 10. In this table, the related 14 ADFs categories/perspectives and losses and gains percentages for the design alternatives are assigned. The percentage of each alternative losses and gains simulated were individually distributed based on the six heat sources. Since the alternatives have same gross floor area and the number of storeys, the heat source “Internal” has three factors (artificial lighting; occupancy; and equipment) which are fixed to have the same effects for all the alternatives. Thus, these three factors are not considered as ADFs in this study. However, to confirm the accuracy of the ADFs impact values assessments and AEISS scoring results, further discussions and explanations of the heat losses and gains analysis results are introduced in Table 10. For example, Design C is the minimum design to lose cooling loads from ventilation because it has small windows percentage and good site orientation. In addition, to compare the design alternatives, equivalent relative indices are calculated for the generated losses and gains percentages as illustrated in Fig. 12.

32

Table 10. Heat losses and gains percentages and relative indices of the design alternatives Heat Sources

Fabric (Conduction)

Sol-Air

Solar

Ventilation

Internal

Inter-Zonal

Related ADFs Perspectives - Losses/Gains - Building Form - Number of Storey - Average Storey Height - Facade Material - Glazing Percentage (G/W) - Sun-Breakers Geometry - Gains Only - Number of Storey - Plan Efficiency (W/F) - Plan Shape - Span Dimension - Gains Only - Building Orientation - Number of Storey - Windows Shape - Windows Efficiency - Glazing Percentage (G/W) - Sun-Breakers Geometry

Design Alternatives

Losses (%)

Gains (%)

Design A Design B Design C

0.1% 0.2% 1.4%

8.4% 11.9% 16.6%

Design A Design B Design C

-

1.8% 0.6% 3.8%

Design A Design B Design C

-

8.6% 17.2% 11.5%

Results Analysis - Design C is the best design to lose heat from fabric due to the use of stone material, small glazing percentage and normal building form. - Design A is the worst design since its form is simple. - Design A is the lowest design to gain heat from fabric due to the normal average of storey height (less volume) and the uses of precast concrete in elevations. - Design C is the maximum design to gain heat from internal sol-air due to its large exterior wall area. - Design B is the lowest design to gain heat from internal sol-air due to less material excitation of precast - Design A is the lowest design to gain heat from the direct sun since it has irregular windows shape. - Design C is the second lowest due to its small glazing percentage and the use of high-efficiency glazing. - Design B is the maximum design to receive heat from the sun since it uses regular and low-efficiency glazing.

- Losses/Gains - Building Orientation - Number of Storey - Building Form - Glazing Percentage (G/W)

Design A Design B Design C

0.1% 0.1% 0.8%

7.0% 7.5% 9.8%

- Design C is the minimum design to lose cooling loads from ventilation because it has small windows percentage and good site orientation. - Designs A and B are the worst due to the unlikely wind and the medium glazing percentage. - Design A is the lowest design to gain heat from ventilation since it has normal storey height (less volume), irregular windows shape.

- Gains Only - Artificial Lighting (Fixed) - Occupancy by People (Fixed) - Equipment (Fixed)

Design A Design B Design C

-

48.2% 48.1% 56.0%

- All designs have the same people occupancy that why the results are so close, but design C gains more heat from artificial lighting.

26.1% 14.7% 2.3%

- All designs can lose a high amount of heat from interzonal since they have the design of the same spaces. - Design C is the lowest design to gain heat from interzonal since its plane shape is normal and has the lowest circulation space. - Designs A gains a huge amount of heat from its zones due to simple plan shape and high circulation space.

- Losses/Gains - Plan Efficiency (W/F) - Plan Shape - Span Dimension - Circulation Areas(C/F)

Design A Design B Design C

99.9% 99.7% 97.9%

33

100.00 90.00

Relative Index (%)

80.00 70.00 60.00 50.00

Design A

40.00

Design B

30.00

Design C

20.00 10.00 0.00

Fig. 02. Relative indices of heat breakdown losses and gains analysis

5.4.2 Degree Days Analysis In hot climate regions, it is known that heat gains always exceed the losses, thus, the building requires cooling loads energy that must be supplied by the air conditioner to remove heat from the building spaces. In this case, maintaining the indoor temperature constantly in an acceptable range by adding cooling loads is essential to enhance the indoor environmental quality. A cooling degree day is the number of degrees demand for cooling energy needed to cool the buildings when a day's mean temperature is above 65° Fahrenheit (18° Celsius) during a day. It is the difference between the daily temperature mean, (high temperature plus low temperature divided by two) and 65°F/18°C. For example, if the cooling degree day is 300 DD for a month with 31 days, then the cooling degree day required to cool the building is 9.68 DD and the daily temperature mean is 27.68°C which is above than 18°C by that amount of this cooling degree day. For the design alternatives (A, B and C), the degree days are the same since they are at the same site location. That does not mean that they have the same amount of the heat losses and gains or cooling energy consumptions since they are formed by different ADFs combination. For that, the monthly 34

degree day‟s analysis simulation is used to indicate how much the heat exchange through the year is. In Saudi Arabia, as shown in Fig.13, the maximum cooling degree days required is 383.5 DD for August, while the minimum is 120.1 DD and that occurs in February. That signifies that the cooling loads required in August for all the alternatives are more than other months, therefore, it consumes more HVAC costs. Fig.13 also illustrates the cooling degree day‟s analysis simulation which estimates the amount of heat gains by Watt-hour per m2 (Wh/m2) for the design alternatives versus degree days. From Fig.13, as an example, the cooling degree days required for October is 293.6 DD, and Design A gains a heat of 58,211 Wh/m2 while Design B and C gains heats of 52,370 Wh/m2 and 50,340 Wh/m2, respectively. Thus, Design C is the best design that consumes cooling loads with a high energy performance. DD

MONTHLY DEGREE - AllThermal Visible Thermal MONTHLY DEGREE DAYS - DAYS All Visible Zones Zones DD

DD

DD

360.0

360.0

1st -January - 31st December 1st January 31st December Aug Aug

360.0 360.0 320.0 320.0

Jul Jul

280.0 280.0 240.0 240.0

320.0

320.0

280.0

280.0

240.0

240.0

200.0

200.0

160.0

160.0

120.0

120.0

80.0

80.0

40.0

40.0

SepSep Jun Jun May May Oct Oct

200.0 200.0 160.0 160.0 120.0 120.0 80.0

80.0

40.0

40.0

0.0

0.0

40.0

40.0

80.0

80.0

Nov Nov Apr

Mar Mar

120.0 120.0 160.0 160.0

JanJan Feb Feb DecDec

200.0 200.0 240.0 240.0

Design A

280.0 280.0 320.0 320.0 360.0 360.0 DD

DD

DD J

JF

M F

A M

M A

MJ

J

A J

D N

0.0 0.0 Jun Feb Aug Apr Jul Sep May Mar Nov Oct Jan Dec Apr Nov Oct Sep Aug Jul Jun May Mar Feb Jan Dec 0.0k 10.0k 20.0k 0.0k 10.0k

D

30.0k 20.0k

40.0k 30.0k

50.0k 40.0k

60.0k 50.0k

80.0k 70.0k

90.0k 80.0k

Wh/ m2 Wh/ m2 90.0k

Aug Jul

360.0

Sep Jun May Oct

320.0

70.0k 60.0k

1st January - 31st December

DD

Jul

360.0

200.0 160.0

N O

Aug

280.0 240.0

O S

1st January - 31st December

MONTHLY DEGREE DAYS - All Visible Thermal Zones DD

360.0 320.0

S A

Sep Jun May Oct

320.0

280.0

280.0

120.0 80.0

240.0

240.0

40.0 0.0

Nov 200.0

Nov

Apr

200.0

Apr

40.0 80.0

160.0

160.0

160.0

Mar

120.0

Jan Feb Dec

120.0

Mar Jan Feb Dec

120.0

200.0 240.0

80.0

80.0

280.0 320.0

Design B

Design C 40.0

40.0

360.0 DD

O

N

D

0.0 Jul Jun May Sep Apr Oct Nov Mar Feb Dec J 0.0kAug FJan M 10.0k

A 20.0k

M 30.0k

J 40.0k

J 50.0k A 60.0kS

O 70.0k

N 80.0k

D 90.0k

Wh/ m2

0.0 Nov Oct Sep Aug Jul Jun May Apr Mar Feb Jan Dec 0.0k

10.0k

20.0k

30.0k

Fig. 03. Cooling degree day‟s analysis 35

40.0k

50.0k

60.0k

70.0k

80.0k

90.0k

Wh/ m2

The degree day‟s analysis simulation in Ecotect, in addition, gives the monthly amount of heat losses and gains distribution as shown in Table 11. For example, Design A gains a total yearly heat of 672, 326 Wh/m2 and losses an amount of energy of 128,943 Wh/m2. In construct, Design C gains heat of 577,267 Wh/m2 while only loses 841 Wh/m2 of combined heat and cooling loads. The low amount of losses cooling loads (saved energy) in Design C is because the ability of its stone material elevations to store a huge amount of cooling loads thought the daylight time, in addition to the less amount of glazing used in the elevations.

Table 11. Monthly cooling degree day and heat losses and gains of the design alternatives Months January February March April May June July August September October November December Total

Cooling (DD) 132.6 120.1 151.0 201.0 303.6 319.4 359.4 383.5 330.7 293.6 208.5 113.3 2916.7

Design A Losses Gains (Wh/m2) (Wh/m2) 12,521 49,176 11,239 45,233 12,116 52,067 11,522 53,203 11,611 60,270 11,123 61,085 11,488 65,053 11,319 66,021 11,505 61,585 12,134 58,211 12,052 51,418 12,834 49,004 128,943 672,326

Design B Losses Gains (Wh/m2) (Wh/m2) 3,883 41,933 3,289 38,961 3,237 46,100 2,636 48,180 2,132 56,009 1,947 57,796 1,786 61,752 1,509 62,596 2,248 57,516 2,478 52,370 3,000 44,663 4,527 41,711 32,672 609,587

Design C Losses Gains (Wh/m2) (Wh/m2) 168 37,228 165 34,727 179 41,665 48 44,666 10 54,351 0 56,503 0 61,129 0 62,737 0 56,184 0 50,340 45 41,329 226 36,408 841 577,267

5.4.3 Energy Cooling Loads Consumptions and Cost Analysis After estimating the amount of heat gains and losses of the design alternatives, the monthly cooling loads simulation was performed using the designs analysis properties as in Table 9. As shown in Fig. 14, the blue bars indicate the amount of needed cooling loads, and since no HVAC machine for heating loads is required, the red bars do not appear.

36

W

MONTHLY HEATING/ COOLING LOADS - All Visible Thermal Zones

[WEA: Not Yet Loaded]

96000000

Design A Max. Cooling Loads: August: 113,808 kWh

72000000 48000000 24000000 0 24000000 48000000 72000000 96000000 120000000 Jan Heating

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

MONTHLY HEATING/ COOLING LOADS - All Visible Thermal Zones

W

Nov

Dec

Cooling

[WEA: Not Yet Loaded]

96000000

Design B Max. Cooling Loads: August: 106,155 kWh

72000000 48000000 24000000 0 24000000 48000000 72000000 96000000 120000000 Jan Heating

W

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Cooling

MONTHLY HEATING/ COOLING LOADS - All Visible Thermal Zones

[WEA: Not Yet Loaded]

96000000

Design C Max. Cooling Loads: August: 92,025 kWh

72000000 48000000 24000000 0 24000000 48000000 72000000 96000000 120000000 Jan Heating

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

Cooling

Fig. 04. Monthly cooling loads analysis

The maximum and minimum amounts of the cooling loads' consumptions of the design alternatives are illustrated in Fig.15. In this figure, it is shown that the required minimum amount of cooling load to reach the comfort zone is occurred on February for all design alternatives, while on August the maximum amount of cooling load occurs. Therefore, the optimization of the ADFs

37

combination has a significant impact than the effect of the month degree days to reduce the cooling consumptions. For example, the proper ADFs combination of Design C only consumes 92,025 kWh while it consumes 113,808 kWh for Design A on August, and that makes a reduction of 21,783 kWh on only that month.

120,000

113,808

Cooling Energy (kWh)

110,000 106,155

100,000

90,000

80,954

92,025

80,000 70,000

71,752

60,000 50,000

54,245

40,000 Jan

Feb

Mar

Apr

May

Design A Series7

Jun Jul Months

Series8 Design B

Aug

Sep

Oct

Nov

Dec

Design C Series9

Fig..05 Maximum and minimum cooling loads consumptions

In Saudi Arabia, for the residential building sector, the cost of 1 Kilowatt-hour (kWh) is 0.30 Saudi Riyal (SR) for the consumption more than 6000 kWh/month [92]. In addition, the capacity of the breaker has a constant monthly fee of 30 SR. Therefore, the total monthly cost for the HVAC cooling energy consumption by adding 5% Value Added Tax (VAT) is calculated for each design alternatives as shown in Table 12.

38

Table 12. Design Alternatives Monthly Cooling Energy (HVAC Loads) and their Costs

Months January February March April May June July August September October November December Total

Design A Energy Cost (kWh) (SR) 87,790.464 27,685.50 80,954.208 25,532.08 91,526.256 28,862.27 93,763.488 29,567.00 104,670.632 33,002.75 105,258.944 33,188.07 111,846.672 35,263.20 113,808.088 35,881.05 107,911.472 34,023.61 102,698.992 32,381.68 92,374.656 29,129.52 88,722.360 27,979.04 1,181,326.208 372,496

Design B Energy Cost (kWh) (SR) 76,975.800 24,278.88 71,752.424 22,633.51 82,062.624 25,881.23 85,356.776 26,918.88 96,165.408 30,323.60 97,544.344 30,757.97 103,848.200 32,743.68 106,155.296 33,470.42 100,156.440 31,580.78 93,230.496 29,399.11 82,669.736 26,072.47 78,130.504 24,642.61 1,074,048.000 338,703

Design C Energy Cost (kWh) (SR) 57,585.792 18,171.02 54,245.124 17,118.71 62,404.980 19,689.07 67,436.344 21,273.95 79,994.096 25,229.64 81,851.824 25,814.82 88,675.680 27,964.34 92,025.864 29,019.65 85,081.960 26,832.32 77,451.704 24,428.79 65,771.756 20,749.60 57,796.540 18,237.41 870,321.728 274,529

As a validation of the proposed AEISS results, Design C is the best design alternative in terms of energy performance and only consumes cooling loads of 870,322 kWh per year with a total cost of 274,529 SR. Design C makes a yearly cost savings of about 97,967 SR (27%) compared to Design A and 64,174 SR (19%) compared to Design B. Therefore, optimizing ADFs at the early design stage effectively could provide design solutions that have less energy consumption and cheaper costs.

6. Conclusion In this paper, to study the early impact of Architectural Design Features (ADFs) on HVAC cooling loads excessive consumption in hot regions‟ climate, seven ADFs were identified and assessed to develop an Architectural-Based Energy Impact Scoring System (AEISS). These ADFs are (1) Building orientation; (2) building envelope; (3) plan shape and complexity; (4) storey and height; (5) windows and glazing; (6) floor spans and (7) circulation space. Using a five-point assessment scale the ADFs of: (1) building orientation; (2) plan shape and complexity; and (3) window glazing were found as the design features that have a greater effect on cooling loads demand in hot weather climate. Further, the optimum design parameters that provide the lowest demand for cooling loads were also identified. However, to facilitate the evaluation of the design alternatives in design stage under this type of climate, 40 design options were defined and assessed to develop the AEISS. The proposed scoring system proved to offer various 39

benefits: (1) enhances the ability of the designers to understand the impact of design features interaction on building energy performance; (2) provides designers with an easy-to-use program of the developed scoring system that performs quantitative evaluation of design to reduce energy consumption, particularly at the early design stage; and (3) works as a powerful tool that can be used in architectural design options to provide a range of sustainable cost-effective energy design alternatives. To validate the proposed system results, a real case study of three design alternatives (A, B and C) of a residential villa project in Saudi Arabia were considered. The alternatives have fixed design conditions related to location, weather data, number of storeys and spaces, gross floor areas and occupancy, thermostat range and HVAC operation hours to demonstrate the impact of changed ADFs. Revit 3-D BIM models of the alternatives and energy simulation tool of Ecotect were integrated to estimate the heat breakdown losses/gains and cooling degree day‟s analysis. Afterwards, the energy monthly cooling loads simulation was performed to compare the energy cooling loads consumption and cost for the three proposed alternatives. The simulation results showed that Design C only losses a total yearly heat of 841 Wh/m2 and gains 577,267 Wh/m2 with cooling loads of 870,322 kWh and makes a cost reduction of 97,967 SR (27%) compared to Design A and 64,174 SR (19%) compared to Design B. These results were matched with the proposed AEISS outputs and proved that the system can act as a powerful decision support tool that helps the design team to provide early decision advice in a systematic way to the project client. In addition, the system can assist the designers in selecting the best architectural design alternatives as well as optimizing and minimizing the cooling load demands in building projects. This helps architects/engineers to obtain sustainable design solution as well as avoid energy cost overruns in the operations phase of the buildings.

Declaration of Competing Interest The authors declare no conflict of interest.

40

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