body odor levels

Journal Pre-proof Optimal design of classroom spaces in naturally-ventilated buildings to maximize occupant satisfaction with human bioeffluents/body odor levels Dario F. Acosta-Acosta, Khaled El-Rayes PII:

S0360-1323(19)30755-3

DOI:

https://doi.org/10.1016/j.buildenv.2019.106543

Reference:

BAE 106543

To appear in:

Building and Environment

Received Date: 28 June 2019 Revised Date:

25 October 2019

Accepted Date: 11 November 2019

Please cite this article as: Acosta-Acosta DF, El-Rayes K, Optimal design of classroom spaces in naturally-ventilated buildings to maximize occupant satisfaction with human bioeffluents/body odor levels, Building and Environment, https://doi.org/10.1016/j.buildenv.2019.106543. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Elsevier Ltd. All rights reserved.

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Optimal design of classroom spaces in naturally-ventilated buildings to maximize occupant satisfaction with human bioeffluents/body odor levels

4 5 6 7 8 9 10

Dario F Acosta-Acosta a,b,*, Khaled El-Rayes a

11

Abstract

a

Dept. of Civil and Environmental Engineering, Univ. of Illinois at Urbana-Champaign. Address: 3112 Newmark Civil Engineering Bldg, 205 N. Mathews, 61801, Urbana, Illinois, US. b Facultad de Ingeniería, Universidad Panamericana Campus Guadalajara. Present address: Álvaro del Portillo No. 49, Ciudad Granja, 45010, Zapopan, Jalisco, México.

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Inadequate indoor air quality in education buildings has been reported to cause health

13

problems, contagion, poor academic performance and absenteeism of occupants. To minimize

14

these adverse effects, the use of natural ventilation systems and hybrid ventilation systems in

15

buildings have increased in recent years. This paper presents the development of a novel

16

optimization model that provides the capability of optimizing education building design in order

17

to maximize occupant satisfaction in the classrooms space in terms of perception of human

18

bioeffluents/body odor while minimizing the construction cost. The multi-objective optimization

19

model is developed in three main stages: (1) model formulation stage that identifies relevant

20

design variables, objective functions and constraints; (2) model implementation stage that

21

executes the optimization computations; and (3) model evaluation stage that analyses the

22

performance of the developed model using an application example. The findings of this

23

performance analysis illustrates the capability of the developed optimization model to generate a

24

wide range of optimal trade-offs solutions, where each provides a unique and optimal trade-off

25

between occupant satisfaction with human bioeffluents/body odor levels and its construction

26

cost. This enables designers to generate and analyze these optimal trade-offs and accordingly *

Corresponding author. Tel.: +52 (33) 1368 2200, fax: +52 (33) 2306 4093. e-mail: [email protected].

27

identify an optimal set of design decisions that maximize satisfaction with human

28

bioeffluents/body odor levels of occupants while complying with specified budget constraint.

29

Keywords: Natural ventilation; Indoor air quality; Human Bioeffluents; Human Body

30

Odor; Education buildings; Multi-objective genetic algorithm.

31

1. Introduction

32

Education buildings are used on a daily basis by 29% of the US population that include

33

58.0 million students in schools, 19.2 million students in colleges and universities, and 10.9

34

million faculty and staff who spend a major part of their daytime in these buildings [1–3]. The

35

total floor space of education buildings was reported to be 12,237 million square feet, which

36

represents approximately 14% of the total floor space of non-residential buildings in the US [2].

37

They are the second major consumer of energy in the category of commercial buildings with an

38

annual consumption of 820 trillion BTU [4]. The majority of education buildings are aging and

39

are reported to suffer from poor ventilation and antiquated HVAC systems [5]. This causes poor

40

indoor air quality (IAQ) in education buildings that has been linked to health problems,

41

contagion, poor academic performance and absenteeism of occupants [6–14].

42

The aforementioned poor indoor air quality in education buildings can be addressed by

43

improving natural and mechanical ventilation to supply fresh outside air into classrooms to dilute

44

and remove carbon dioxide (CO2) concentration and other airborne contaminants. CO2

45

concentration levels are used to evaluate indoor air quality in terms of human bioeffluents

46

acceptability and therefore, the adequacy of the ventilation rate to control body odor [15,16]. The

47

relationship between CO2 concentrations and the acceptability of a space in terms of human

48

bioeffluents/body odor has been experimentally-determined in both chambers and real buildings

49

[15]. Accordingly, ASTM standard D6245 and ASHRAE standard 62.1 recommends that the

50

difference between CO2 concentration of indoor and outdoor air be equal to or below 650 ppm

51

[15] or 700 ppm [17] to control human body odor at an acceptable level. The ASTM standard

52

states that this 650 ppm concentration difference, combined with an assumed outdoor CO2

53

concentration of 350 ppm, is the basis of the commonly-referenced guideline value for CO2 of

54

1000 ppm to control human body odor [15].

55

The use of natural ventilation systems (NVS) and hybrid ventilation systems (HVS) in

56

buildings have increased in recent years to improve their CO2 concentration and energy

57

consumption [18]. The use of these NVS and HVS to replace HVAC systems can lead to

58

significant savings in the energy consumption of education buildings [18–20] because HVAC

59

systems account for 55% of their total energy consumption [2]. A number of research studies

60

have been conducted to investigate and improve the use of NVS and HVS in buildings. These

61

studies focused on (1) analyzing and modeling the natural ventilation processes; (2) determining

62

the effectiveness of NVS and HVS in buildings; (3) improving the design of NVS and HVS in

63

buildings, and (4) optimizing the performance of natural and hybrid ventilated buildings.

64

First, several research studies were conducted to analyze and model natural ventilation

65

processes in buildings. For example, Swami and Chandra [21] analyzed wind pressure

66

coefficients that can be used to calculate natural ventilation airflow rates in buildings. Similarly,

67

Cóstola et al.

68

coefficients calculation on NV airflow rates. Other studies analyzed the performance of various

69

models and simulation tools that were developed to simulate natural and hybrid ventilated

70

buildings. For example, Gandhi et al. [25,26] analyzed how the available simulation tools and

71

methods are being used by practitioners and researchers for simulating mixed mode and

[22,23] and Muehleisen and Patrizi [24] analyzed the impact of pressure

72

naturally-ventilated buildings. Other studies utilized building energy simulation programs such

73

as EnergyPlus to model natural ventilation processes in buildings. For example, Zhai et al. [27]

74

assessed the accuracy of EnergyPlus in modeling thermal-induced airflows in naturally-

75

ventilated buildings by comparing its simulated results to field measurements obtained from

76

three existing buildings. Dols et al. [27,28] combined thermal, airflow and contaminant modeling

77

of naturally-ventilated buildings by coupling EnergyPlus with CONTAM, which is a multizone

78

building airflow and contaminant transport simulation tool. Other studies investigated the design

79

and performance of natural ventilation in atrium buildings [29–33]

80

Second, several studies focused on determining the effectiveness of NVS and HVS in

81

buildings in different climates. For example, Axley [34] developed a model to evaluate the

82

climate suitability of natural/hybrid ventilation to provide passive cooling in commercial

83

buildings in North America. Hiyama and Glicksman [35] proposed the use of target air change

84

rate as a new criterion to evaluate the potential energy savings of NVS during the early stages of

85

building design. Chen et al. [36] estimated the energy saving potential of NVS in 1854

86

geographical locations around the world based on their NV hours which represents the number of

87

hours in a year when the weather and indoor conditions are appropriate for natural ventilation.

88

That study reported that (a) subtropical high-land and Mediterranean climates in regions such as

89

California are more suitable for utilizing NVS, and (b) NV hours in several US locations ranged

90

from 2,248 hours in Houston to 7,197 hours in Los Angeles out of the total annual hours of

91

8,760.

92

Third, a number of studies were conducted to improve the design of NVS and HVS in

93

buildings. For example, Heiselberg [37] developed a natural ventilation design process that

94

integrates the design of heating, cooling, lighting, and ventilation of buildings. CIBSE [38]

95

provided guidelines for the design of NVS for non-residential buildings and for their selection,

96

specification, and integration of various types of ventilation components. Omrani et al. [39]

97

developed a process model to integrate and evaluate the natural ventilation design into the

98

overall building design process for multi-story buildings. Aflaki et al. [40] provided a number of

99

design recommendations to improve the performance of NVS in tropical climates, including (1)

100

providing shading at top of east and west openings, (2) setting the orientation of the building in

101

the direction of the dominant angles of wind and sun, (3) reducing the size of apertures and

102

windows on east and west sides, (4) shaping the building as narrow as possible with the shorter

103

side facing west and east, (5) providing vegetation surrounding the building, and (6) insulating

104

the external walls. Belleri et al. [41] compared natural ventilation rates predicted by an

105

EnergyPlus model to field study measurements and identified several key parameters that can

106

improve the accuracy of model predictions, including: occupant behavior, wind-speed profile,

107

internal heat gains, envelope conductivity, and wind pressure coefficients. Mora-Pérez et al. [42]

108

developed a computational fluid dynamic (CFD) model for selecting the best building location

109

that improves its natural ventilation performance by analyzing wind paths around and through

110

the building.

111

Fourth, several studies focused on optimizing the performance of natural and hybrid

112

ventilated buildings. For example, Deblois and Ndiaye [43] implemented a multivariable

113

optimization model to optimize the design of HVS in four elementary classrooms by maximizing

114

their occupied hours that utilize natural ventilation. Malkawi and Wang [44] developed a

115

methodology that combines genetic algorithms, CFD and network airflow models to optimize the

116

building form in order to maximize its natural ventilation potential in an urban environment. Guo

117

et al. [45] developed a methodology for optimizing site planning, building shape, and building

118

envelope of naturally-ventilated buildings. Rinaldi et al. [46] used a particle swarm optimization

119

method to develop a control strategy of windows opening in order to minimize the thermal

120

discomfort hours in hybrid ventilated residential buildings. Wang and Wang [47] developed an

121

intelligent control system for HVS in energy-efficient buildings that achieves an acceptable

122

indoor air quality while reducing energy consumption, by adjusting building ventilation rates

123

based on indoor CO2 concentration forecasts. Hamdy et al. [48] compared the performance of

124

seven commonly used multi-objective evolutionary optimization algorithms in solving design

125

problems of nearly zero energy buildings that utilize HVS and concluded that the two-phased

126

optimization using genetic algorithm provided the best performance among the analyzed seven

127

algorithms. Despite the significant contributions of the aforementioned research studies, they are

128

all incapable of (1) maximizing occupant satisfaction with human bioeffluents/body odor levels

129

in education buildings that utilize NVS or HVS; and (2) minimizing the construction cost of

130

these types of buildings; and (3) generating and analyzing optimal trade-offs between these two

131

important design objectives of education buildings.

132

2. Objective

133

The objective of this paper is to develop a model that optimizes the design of the

134

classroom space of naturally-ventilated education buildings that is capable of maximizing their

135

occupant satisfaction with human bioeffluents/body odor levels, minimizing their construction

136

cost, and generating optimal trade-offs between these two design objectives. The scope of the

137

model focuses on optimizing classroom spaces in education buildings and does not optimize the

138

design of other building spaces such as offices, cafeteria, library, and gymnasium. The multi-

139

objective optimization model is developed in three main stages: (1) model formulation stage that

140

identifies relevant design variables, objective functions and constraints; (2) model

141

implementation stage that executes the optimization computations; and (3) model evaluation

142

stage that analyses the performance of the developed model using an application example, as

143

shown in Fig. 1. The following sections describe in more detail these three development stages

144

of the multi-objective optimization model.

145 146

Fig. 1.

Model development

147

3. Model formulation

148

The present model was formulated in three main steps: (1) identifying the design

149

variables for the model; (2) defining the objective functions for the multi-objective optimization

150

model; and (3) formulating the model constraints.

151

3.1 Design variables

152

The model integrates ten design variables that represent the design decisions of education

153

building that have significant impact on (1) occupant satisfaction with human bioeffluents/body

154

odor levels, and (2) building construction cost as shown in Fig. 1. First, these ten design

155

variables directly affect CO2 concentration levels in classroom spaces and accordingly they have

156

significant impact on their occupant satisfaction with human bioeffluents/body odor levels.

157

Second, these ten design variables have significant impact on the building construction cost. The

158

possible selection of each of these ten design variables and their impact on the two optimization

159

objectives of the developed model are described in the following section.

160

Classroom layout selection (Lfw,c): represents the selected classroom layout from a set of

161

feasible alternatives for education buildings with atriums. Naturally-ventilated buildings often

162

require the use of an atrium to promote natural temperature stratification to increase buoyancy-

163

driven airflows [29], and their layout can be classified based on the atrium location as

164

centralized, semi-enclosed, linear, or attached as shown in Fig. 2 [49]. Each of these feasible

165

building layouts organizes the building classroom space into a number of wings that ranges from

166

1 in the linear layout to 4 in the centralized layout (see Fig. 2). The classroom space within each

167

of these wings can be designed to accommodate different types of classrooms with varying

168

student capacities such as daycare rooms, classrooms, and lecture halls [17] as shown in Fig. 2.

169

This variable (Lfw,c) is designed to consider and optimize the impact of both the building layout

170

type (centralized, semi-enclosed, linear, or attached) and the types of classrooms that will be

171

allocated within each wing in all building floors, as shown in Fig. 2. Accordingly, Lfw,c is

172

modeled using an integer variable that represents the selected classroom type for each class (c) in

173

each wing (w) in each floor (f). The value of this integer variable (Lfw,c) can range from 1 to n to

174

represent the selection of each classroom type from a set of n feasible alternatives. For example,

175

the selection of a lecture hall (fifth classroom type) for the second classroom location (c=2) in

176

the third wing (w=3) of the first floor (f=1) is represented by this variable as Lf=1w=3, c=2 = 5, as

177

shown in Fig. 2a.

178 179

Fig. 2.

Classroom layout selection for atrium buildings.

180

Building orientation (O): represents the azimuth angle in degrees between the true north

181

and the building direction perpendicular to the ventilated exterior wall of the building that has

182

ventilation openings and is considered to be the façade of wing A in this model that has the

183

longer length of the building, as shown in Fig. 3. This variable is designed to consider and

184

optimize the impact of building orientation on the natural-ventilation airflows driven by wind.

185

For example, a building orientation of zero (O = 0o) represents that wing A of the building faces

186

the true North direction.

187 188

Fig. 3.

Model design variables.

189

Floor height (FH): represents the height in meters of each floor of the building and is

190

used in the model to calculate the volume of each classroom, as shown in Fig. 3. FH is designed

191

to account for and optimize the impact of: (1) classroom volume on CO2 concentration over

192

time; (2) the position and height of building openings on buoyancy-driven airflows; and (3) floor

193

height on building cost.

194

Ventilation shaft height (SH): represents the vertical distance in meters from the roof of

195

the building to the roof of the atrium, as shown in Fig. 3. SH is designed to consider and

196

optimize the effect of the atrium height on buoyancy-driven natural ventilation (stack ventilation)

197

by inducing further temperature (and pressure) difference along the atrium. SH also impacts the

198

building cost.

199

Atrium width (AW): represents the minimum width of the atrium in meters, as shown in

200

Fig. 3. AW is designed to account for and optimize the effect of atrium volume on both the

201

natural ventilation and cost of the building.

202

Vents length (VLj): represents the length of vents located in wing (j) and is expressed as a

203

percentage of the classroom ventilated-wall length, as shown in Fig. 3. This percentage of

204

classroom ventilated-wall length (VLj) is allowed in the present model to exceed 100% to

205

provide multiple vents. For example, a solution of VLj = 160% represents double vents that

206

cover 80% of the ventilated-wall length. VLj is designed to search for and identify the size of

207

classroom vents to maximize natural ventilation airflows. It should be noted that the model

208

assumes in its calculation of CO2 levels the most conservative case when ventilation is provided

209

only through vents and all windows are closed. Opening any of these windows will produce

210

additional ventilation and further reduction in CO2 concentration and human bioeffluents/body

211

odor levels.

212

Window to wall ratio of classrooms (WWRc): represents the ratio between the glazed

213

area to the total area of the ventilated wall in each classroom, as shown in Fig. 3. WWRc is

214

designed to consider and optimize the impact of fenestration on the classroom air temperature

215

due to the heat gains by solar radiation and conduction through the glazing. Temperature

216

difference between adjoining spaces (e.g., outdoor, classrooms and atrium) affects buoyancy-

217

driven airflows.

218

Window to wall ratio of the atrium (WWRa): represents the ratio between the atrium’s

219

fenestration area and the exterior wall area excluding the wall area above the building roof level,

220

as shown in Fig. 3. WWRa is designed to consider and optimize the effect of fenestration on heat

221

gains in the atrium to enhance its stack ventilation.

222

Window to wall ratio of the shaft (WWRs): represents the ratio between the glazing area

223

in the roof and walls of the ventilation shaft and the total envelope area of the shaft, as shown in

224

Fig. 3. WWRs is designed to consider and optimize the effect of glazing on the walls and roof of

225

the ventilation shaft to enhance buoyancy-driven airflows through the atrium by generating a

226

solar chimney effect.

227

Occupancy density of classroom (ODi,j): represents the maximum occupant density of

228

each type of classroom (i) in each wing (j) of the building, represented by the number of

229

occupants per 100 square meters of classroom area. The ODi,j is designed to account for and

230

optimize the effect of classroom density on CO2 concentration levels inside the classroom.

231

3.2 Objective functions

232

The model incorporates two optimization objective functions that are designed to (1)

233

maximize the occupant satisfaction with human bioeffluents/body odor levels, and (2) minimize

234

the construction cost of classroom space in education buildings. It should be noted that these two

235

optimization objectives are often conflicting because improvements in the first objective function

236

can be achieved by increasing classroom space and height which have a negative impact on the

237

performance of the construction cost objective function. Accordingly, the model is designed to

238

support multi-objective optimization to enable designers to search for and identify building

239

designs that provide optimal trade-offs between these two conflicting objectives.

240

3.2.1 Maximizing occupant satisfaction with human bioeffluents/body odor levels

241

(OS)

242

The first objective function in the model is designed to calculate and optimize occupant

243

satisfaction with human bioeffluents/body odor levels (OS) of classrooms in naturally-ventilated

244

buildings. The performance of OS in this model is measured in terms of the percentage of time

245

that classrooms provide acceptable human bioeffluents/body odor levels for their occupants

246

during the analysis period. ASHRAE [17] defines acceptable IAQ as “air in which there are no

247

known contaminants at harmful concentrations as determined by cognizant authorities and with

248

which a substantial majority (80% or more) of the people exposed do not express

249

dissatisfaction”. Ventilation-related contaminants that are reported to cause occupant

250

dissatisfaction and can be controlled by ventilation are carbon dioxide (CO2) and human body

251

odor emitted by occupants [17,50]. Other contaminants such as formaldehyde, VOC, lead, PM,

252

radon that are listed in Appendix B in ASHRAE Standard 62.1 [17] are controlled more

253

efficiently using source control strategies such as selecting low-emitting materials for

254

construction, furniture and cleaning.

255

Improved ventilation has been reported by many studies to reduce the negative impacts

256

of the CO2 and human body odor contaminants. These studies showed that a ventilation rate of

257

7.5 L/s per person of outdoor air enables 80% of unadapted persons (visitors) to find human

258

body odor at an acceptable level [51–55]. The same level of body odor acceptability is achieved

259

when the difference between the indoor and outdoor CO2 levels is less than or equal to 650 ppm

260

[15]. This correlation between CO2 concentration and dissatisfaction with bioeffluent odor is

261

used in the ventilation requirements in existing standards and guidelines [15,17,56].

262

Accordingly, the present model quantifies and measures the performance of the overall

263

satisfaction with human bioeffluents/body odor levels of building occupants in two steps that are

264

designed to: (1) calculate the concentration levels of carbon dioxide (CO2) in all classrooms, and

265

(2) estimate the percentage of the time that classrooms provide acceptable occupant satisfaction

266

based on their calculated CO2 levels.

267

In the first step, the model calculates CO2 concentration in each classroom by simulating

268

the natural ventilation of the building during a whole calendar year using: (a) CONTAM [57],

269

which is a widely-used multizone building airflow and contaminant transport simulation tool;

270

and (b) EnergyPlus [58], which is a whole-building energy simulation program, as shown in Fig.

271

7. Both simulation tools are coupled using a quazi-dynamic method [28] to perform building co-

272

simulation. CONTAM is used to generate a network airflow model of the building to estimate

273

indoor (interzone) and outdoor (natural ventilation) airflows required by EnergyPlus and to

274

perform a mass balance analysis to compute CO2 levels in each building zone (classrooms and

275

atrium). EnergyPlus, on the other hand, is used to perform heat transfer calculations to compute

276

indoor temperature required by CONTAM to calculate buoyancy-driven airflows. This

277

CONTAM-EnergyPlus co-simulation is used in the present model to calculate the changes in the

278

CO2 levels over an entire calendar year in each classroom, as shown in the example classroom

279

CO2 variations over one year in Fig. 4a and one day in Fig. 4b.

280 281 282

Fig. 4.

Example of calculated CO2 levels variations over (a) one year, and (b) one day in one classroom.

283

In the second step, the CO2 levels calculated in the previous step are used to estimate the

284

average OS in all classrooms in the education building over an entire calendar year in three sub-

285

steps that are designed to calculate (1) OSc,d in classroom (c) for each school day (d), as shown in

286

Eq. 1; (2) average OSc in each classroom (c) for an entire calendar year (d = 1 to D), as shown in

287

Eq. 2; and (3) average OS in all classrooms in the education building for an entire calendar year,

288

as shown in Eq. 3. In the first sub-step, the model calculates OSc,d by identifying the percentage

289

of the time that students experience an acceptable human bioeffluents/body odor levels during

290

their occupancy of classroom (c) in school day (d). This OSc,d percentage is calculated as the

291

ratio between (1) total daily time that the classroom has acceptable levels of CO2, and (2) total

292

time of a school day. The total daily time of acceptable CO2 levels is calculated as the sum of the

293

durations of all timesteps (tc,d,s) that comply with designer-specified acceptable CO2 levels, as

294

shown in Fig. 5, the designer-specified acceptable CO2 level is assumed in the application

295

example to be 1000 ppm based on an assumed CO2 level of 350 ppm in outside air and the

296

reference value provided by ASTM standard D6245 of 650 ppm above outdoor levels [15]. The

297

total time of a school day is determined by the daily occupied class time that starts at timestep

298

(B) and ends at timestep (F), as shown in Fig. 5 and Eq. 1. The second and third sub-steps are

299

then used to calculate the average OS(c) in each classroom (c) for an entire calendar year and the

300

average OS in all classrooms in the education building during a calendar year, as shown in Eq. 2

301

and Eq. 3, respectively.



,

% =

, ,

−

+1 ∗ ,

% = !

% =

"

,

(1)

∗ 100 (2)

(3)

302

Where tc,d,s is the amount of time in seconds that simulation timestep (s) of day (d) of

303

classroom (c) has a calculated CO2 level equal than or below to the specified acceptable CO2

304

level of 1000 ppm, as shown in the example on Fig. 5; T is the duration in seconds of each

305

simulation timestep; B is the timestep when classes begin each day following the occupancy

306

schedule defined for the co-simulation; F is the timesteps when classes finish each day; D is the

307

total number of days in the co-simulation period which is assumed to be an entire calendar year

308

in this model; and C is the total number of classrooms in the building.

309 310

Fig. 5.

311

3.2.2 Minimizing construction cost (CC)

Example of calculation of OSc,d in classroom (c=11) on March 28th (d=87)

312

The second objective function in the model is designed to calculate and minimize the

313

construction cost (CC) of classroom space in education buildings. This cost (CC) is computed

314

using square foot cost models for green institutional buildings that are developed by RSMeans

315

(2018). Accordingly, this cost (CC) is calculated by aggregating the cost of the major group of

316

construction elements of substructure, shell, interiors, services, equipment and furnishings,

317

special construction, and building sitework based on the UNIFORMAT II classification system

318

[60], as shown in Eq. 4.

=#

$%&

∗

+

$'())

6

+

*+,

+

$(-.

+

/0&2

+

$3

+

4$ 5 ∗

1+

(4)

319

Where, CSub is the assembly cost estimate of the building substructure; CShell is the

320

assembly cost estimate of the building shell; CInt is the assembly cost estimate of the building

321

interiors; CServ is the assembly cost estimate of the building services; CEq&F is the assembly cost

322

estimate of the building equipment and furnishings; CSC is the assembly cost estimate of the

323

building special construction; CBS is the assembly cost estimate of the building sitework; CF

324

represents the contractor overhead and profit percentage; and CI represents the city index used to

325

adjust the building cost estimates to a specific location.

326

The cost of each of the aforementioned seven major groups of construction elements is

327

estimated by aggregating the cost of all its individual elements that are listed in Table 1. Each of

328

these individual element costs is estimated by multiplying its assembly unit cost by its number of

329

units calculated by the model based on the building design solution. For example, the cost of

330

individual element “A1030 Slab on grade” is estimated by multiplying the assembly unit cost of

331

a “4 in reinforced concrete slab with recycled vapor barrier and granular base” that is equal to

332

$5.84 per square foot [59] by its quantity take-off equal to 8,740.30 square feet as shown in Fig.

333

6. The cost of the remaining individual elements of the building are estimated using a similar

334

approach.

335 336 337 338

339 340

Table 1.

Building elements breakdown for model G.580 School, Jr High [59]

Level 1 Major group elements A Substructure (CSub)

Level 2 Group elements A10 Foundation

A20 Basement Construction B Shell (CShell)

B10 Superstructure B20 Exterior enclosure

B30 Roofing C Interiors (CInt)

C10 Interior construction C20 Stairs C30 Interior finishes

D Services (CServ)

D10 Conveying D20 Plumbing

D30 HVAC

D40 Fire protection D50 Electrical

E Equipment and furnishings (CE&F)

E10 Equipment E20 Furnishing

341

Level 3 Individual elements A1010 Standard foundation A1030 Slab on grade (see example in Fig. 6) A2010 Basement excavation A2020 Basement walls B1010 Floor construction B1020 Roof construction B2010 Exterior walls B2020 Exterior windows B2030 Exterior doors B3010 Roof coverings B3020 Roof openings C1010 Partitions C1020 Interior doors C2010 Stair construction C3010 Wall finishes C3020 Floor finishes C3030 Ceiling finishes D1010 Elevators & lifts D2010 Plumbing fixtures D2020 Domestic water distribution D2040 Rainwater drainage D3040 Distribution system D3020 Heat generating system D3050 Terminal & package units D4010 Sprinklers D4020 Standpipes D5010 Electrical service/distribution D5020 Lighting & branch wiring D5030 Other equipment D5090 Other electrical systems E1020 Institutional equipment E1090 Other equipment E2020 Moveable furnishings

342 343 344

Fig. 6.

Example of cost calculation of individual element A1030 Slab on grade.

3.3 Constraints

345

The developed model is designed to comply with three types of constraints: design

346

variables constraints, budget constraint, and residual space constraint. The model constraints are

347

set to ensure practical solutions, reduce the design and solution spaces, and decrease the model

348

computation time.

349

Design variable constraints: This constraint provides building designers with the

350

flexibility to specify a practical range for each design variable that can vary from one project to

351

another. The model is formulated to analyze all alternative design solutions within that designer-

352

specified range in order to identify an optimal solution for each design variable. For example, a

353

designer can specify that the window to wall ratio of classrooms (WWRc) can range from 20%

354

to 80% with an increment of 1%. The model is designed to comply with this constraint by

355

generating and analyzing all alternative design solutions that are limited by that range. The

356

constraints of the remaining design variables can be specified by designers using a similar

357

approach.

358

Budget constraint (Bmax): This constraint represents the owner-specified maximum

359

budget that can be allocated to the project. The budget constraint is designed to reduce the

360

solution space and computational time of the model by discarding non-complying solutions

361

before the evaluation of the OS objective function, as shown in Fig. 7.

362

Residual space constraint (RSmax): This constraint represents the designer-specified

363

maximum residual space allowed in a feasible design solution. The residual space represents a

364

space filler that is often needed to be placed in one of the building wings to equalize the length of

365

opposed building sides in order to produce a building with a rectangular shape. Although the

366

residual space is reduced during the optimization as a byproduct of minimizing the construction

367

cost, the present model provides designers with the flexibility to specify an upper limit on this

368

variable based on the specific conditions of their project.

369

4. Model implementation

370

The proposed model is implemented using multi-objective non-dominated sorting genetic

371

algorithm II NSGAII [61] due to their capabilities to generate optimal/near-optimal trade-offs

372

among optimization objectives and to model nonlinear and discontinuous objective functions.

373

The optimization model is developed using Visual Basic and combined with a multizone

374

building airflow and contaminant transport simulation tool, CONTAM, and an external building

375

simulation engine, EnergyPlus. The optimization computations are performed in three modules:

376

(1) initialization module which enables designers to specify the values of all required input data;

377

(2) optimization module which generates an initial population, ranks the generated solutions,

378

produces subsequent generations using the genetic algorithm operators, and identifies optimal

379

trade-off solutions between the occupant satisfaction with human bioeffluents/body odor levels

380

and construction cost objective functions; (3) objective function module which calculates the

381

occupant satisfaction with human bioeffluents/body odor levels and construction cost for each of

382

the generated solutions using two external simulation tools (CONTAM and EnergyPlus), as

383

shown in Fig. 7.

384

4.1 Initialization module

385

The purpose of this module is to enable designers to specify all the required project input

386

data. This input data are classified in 10 categories: (1) weather data that represents average

387

weather conditions in the project location for an entire calendar year that is required by the

388

external building energy simulation program EnergyPlus; (2) classroom data that includes for

389

each type of classroom in the building its required number, planned number of occupants, and

390

side ratio; (3) building data that includes its required type of atrium as well as the minimum and

391

maximum number of classrooms per building wing; (4) daily schedule of classroom occupancy;

392

(5) construction cost data for all the individual building elements shown in Table 1; (6)

393

CONTAM simulation tool parameters that specifies simulation period, simulation timestep

394

duration, CO2 generation rate of occupants, airflow parameters to model the building openings,

395

relative height and length of the wall openings, and; (7) EnergyPlus simulation program

396

parameters that includes the construction material properties and heat gains from equipment and

397

lighting; (8) design variable constraints that specifies the minimum and maximum value of each

398

design variable and its increment; (9) budget (Bmax) and residual space (RSmax) constraints; and

399

(10) NSGAII algorithm parameters that specifies population size (S), maximum number of

400

generations (G), recombination probability (Pc), and mutation probability (Pm).

401

4.2 Optimization module

402

The purpose of this module is to identify the Pareto front of optimal or near-optimal

403

trade-off solutions between the OS and CC objective functions. To achieve this, the

404

computational procedure in this module is implemented in nine tasks that are designed to: (1)

405

generate an initial population (Pg=1) with (S) random solutions; (2) calculate the value of the two

406

objective functions for each solution in the initial population (Pg=1) using the objective function

407

module; (3) sort and rank all solutions in the initial population (Pg=1) based on their calculated

408

objective functions using the Pareto dominance and crowding distance criteria; (4) generate child

409

population (Cg) using tournament selection, recombination and mutation; (5) calculate the value

410

of the two objective functions for each solution in the child population (Cg) using the objective

411

function module; (6) create a new combined population (Ng) that combines the parent (Pg) and

412

child populations (Cg); (7) sort and rank all solutions in the new combined population (Ng = Pg +

413

Cg) using the Pareto dominance and crowding distance criteria; (8) select the top (S) solutions

414

from Ng to generate a new parent population (Pg+1); (9) repeat tasks (4) to (8) until the maximum

415

generation (G) is reached; and (10) identify the Pareto optimal front based on the results of the

416

last generation (PG).

417

4.3 Objective function module

418

The main purpose of this module is to calculate the OS and CC objective functions for

419

each solution (s) in the initial population (P1) and subsequent Child populations (Cg) to evaluate

420

their fitness. The fitness of each solution enhances its rank in the population by increasing its

421

chances to survive and be recombined in the next generation. The computational procedure in

422

this module is implemented in six tasks that are designed to: (1) generate a building design based

423

on the selected values for its design variables; (2) calculate the residual space (RS) of the

424

generated building design which represents a space filler that is often needed to be placed in one

425

of the building wings to equalize the length of opposed building sides in order to produce a

426

building with a rectangular shape; (3) compute the construction cost (CC) objective function

427

using Eq. 4; (4) verify if the generated building solution complies with both the budget constraint

428

(CC ≤ Bmax) and residual space (RS ≤ RSmax) constraints; (5) discard solutions that do not

429

comply with both constraints; (6) generate simulation files for all solutions that comply with both

430

constraints: CONTAM input file (prj file), EnergyPlus input file (idf file), and coupled

431

simulation data exchange files (contam.vef, and modelDescription.xml) [28]; (7) execute the

432

CONTAM-EnergyPlus coupled simulation; and (8) compute the OS objective function from the

433

CONTAM output file (sim file) using Eq. 3.

434 435 436

Fig. 7.

Optimization model implementation using NSGAII.

437

5. Performance evaluation

438

An application example of a school building is analyzed to illustrate the capabilities of

439

the developed model and evaluate its performance in generating optimal trade-offs between the

440

occupant satisfaction with human bioeffluents /body odor levels and the building construction

441

cost. The project design input data for this education building example is specified to have: (1)

442

weather data based on its location in Los Angeles, CA; (2) 12 classrooms (c=12) that have

443

varying design requirements as shown in Table 2; (3) a linear atrium with two to four classrooms

444

per building wing; (4) a daily schedule of four classes that has a duration of one hour and 45

445

minutes each and are separated by 15 minutes recesses (see Fig. 4b), with an occupancy rate of

446

100% during class time and 10% during recess; (5) construction cost data, as shown in Tables 3

447

and 4; (6) CONTAM simulation tool parameters that specifies simulation to be performed for an

448

entire year using four timesteps per hour (T=900 s), CO2 generation rate of 17.28 liters per hour

449

per occupant [56], and assuming that each classroom has vents located at 0.3 of the floor height,

450

where each is capable of providing an airflow rate of 34.2 l/s/m at 1 Pa; (7) EnergyPlus

451

simulation program parameters that assumes that envelope materials comply with standard

452

ASHRAE 189.1-2014 and internal heat gains from equipment and lighting are estimated to be

453

24.3 W/m2 of floor area; (8) design variable constraints as shown in Table 5; (9) budget and

454

residual space constraints that are specified to be less than or equal $6 million and 160 m2,

455

respectively; (10) NSGAII algorithm parameters are specified to be 320 generations, initial

456

population of 400 random solutions, subsequent child populations of 40 solutions, crossover

457

probability of 85%, and a mutation rate of 5% [62,63].

458

459

Table 2.

Required classroom types for application example.

Classroom type Quantity Capacity [Persons] Sides Ratio (1:x)

460 461

Table 3.

1

2

3

4

5

2 20 1.5

4 25 1.5

4 40 1.8

1 65 1.5

1 80 1.8

Application example construction cost data for model G.580 School

462

(RSMeans 2018). Individual elements A1010 Standard foundation A1030 Slab on grade A2010 Basement excavation A2020 Basement walls B1010 Floor construction B1020 Roof construction B2010 Exterior walls*

B2020 Exterior windows*

B2030 Exterior doors B3010 Roof coverings*

B3020 Roof openings*

C1010 Partitions C1020 Interior doors C2010 Stair construction C3010 Wall finishes C3020 Floor finishes C3030 Ceiling finishes D1010 Elevators & lifts D2010 Plumbing fixtures

Assembly items Poured concrete; strip and spread footings 4” reinforced concrete with recycle vapor barrier and granular base Site preparation for slab and trench for foundation wall and footing 4” foundation wall Open web steel joists, slab form, concrete, columns Metal deck, open web steel joist, columns Face brick with concrete block backup and 2 in of EPS (R-1.35) continuous insulation (total assembly U-0.4809) Double pane window, 6 mm clear glasses with a hi-reflective coating in one surface (U2.864, SHGC =0.247 and VT=0.881) Wall vents Double aluminum & glass Single-ply TPO membrane & standing seam metal with 6.75 in of EPS (R-4.57) insulation (total assembly U-0.2096) Skylight with the same glass than exterior window Roof ventilators Concrete block w/foamed-in insulation Single leaf kalamein fire doors, low VOC paint Concrete filled metal pan 50% paint, low VOC, 40% glazed coatings, 10% ceramic tile 50% vinyl comp. tile, recycled content, 30% carpet tile, 20% terrazzo Mineral fiberboard on concealed zee bars One hydraulic passenger elevator Low flow, auto sensor, service fixtures, supply and drain (1 fixture per 1170 SF Floor)

Assembly unit cost $2.72 $5.84

per SF Ground per SF Slab

$0.18

per SF Ground

$98.00 $30.22

per LF Wall per LF Floor

$12.58 $33.49

per SF Roof per SF Wall

$78.69

per SF Fenestration

$60.96 $7,200.00 $6.05

per LF Vents per Each per SF Roof

$113.86

per SF Skylight per LF Vents per SF Partitions per Each

$457.20 $14.95 $1,222.00 $15,800.00 $4.04 $9.24 $7.60 $91,300.00 $7,675.00

per SF Floor per SF Surface per SF Floor per SF Ceiling per Each per Each

463

Table 4.

Application example construction cost data (cont’d).

Individual elements D2020 Domestic water distribution D2040 Rainwater drainage D3040 Distribution system D3050 Terminal & package units D4010 Sprinklers D4020 Standpipes D5010 Electrical service/distribution D5020 Lighting & branch wiring D5030 Other equipment D5090 Other electrical systems E1020 Institutional equipment E1090 Other equipment

464 465 466

467

Assembly items

Assembly unit cost

Gas fired, tankless water heater Roof drains Enthalpy heat recovery packages Multizone rooftop air conditioner SEER 14 (includes heat generating system) Sprinklers, light hazard Standpipes, wet, Class III 1600 ampere service, Panel board and feeders LED fixtures, daylight dimers, light on/off/ receptacles, switches, and A.C. power Addressable alarm system, internet wiring, communication systems and emergency light Emergency generator, 100 kW, and energy monitoring systems Laboratory casework and counters

$0.38

per SF Floor

$2.02 $38,725.00 $20.20

per SF Roof per Each per SF Floor

$2.57 $0.45 $0.75

per SF Floor per SF Floor per SF Floor

$13.28

per SF Floor

$5.11

per SF Floor

$0.80

per SF Floor

$2.65

per SF Floor

Waste handling recycling tilt truck, built-in $1.35 per SF Floor athletic equipment, bleachers & backstops E2020 Moveable furnishings No smoking signage $0.02 per SF Floor * Cost rates adjusted to comply with Standard 189.1-2014 (SI) for non-residential building in climate zone 3

Table 5.

Application example design variable constraints.

Design variable

Min. Value

Max. Value

Increment

Possible options

Classroom layout selection (L) Building Orientation (O) Floor height (FH) Ventilation shaft height (SH) Atrium width (AW) Vents length (VL1 and VL2) Window to wall ratio of classrooms (WWRc) Window to wall ratio of the atrium (WWRa) Window to wall ratio of the shaft (WWRs) Occupancy density of classroom 1 (OD1,1 and OD1,2) Occupancy density of classroom 2 (OD2,1 and OD2,2) Occupancy density of classroom 3 (OD3,1 and OD3,2) Occupancy density of classroom 4 (OD4,1 and OD4,2) Occupancy density of classroom 5 (OD5,1 and OD5,2)

1 0⁰ 2.8 m 0.5 m 5m 80% 20% 10% 0% 19 19 26 49 60

5 359⁰ 4.5 m 3.0 m 8m 160% 90% 90% 90% 25 25 35 65 80

1 1⁰ 0.1 m 0.1 m 1m 1% 1% 1% 1% 1 1 1 1 1

20,432 360 18 26 4 81 71 81 91 7 7 10 17 21

468

The aforementioned specified project input data for this application example creates a

469

solution space that includes 1.4x1030 feasible alternative solutions which is calculated as the

470

product of multiplying the possible combinations of all the possible options of the design

471

variables in Table 5. Each of these solutions provides an alternative building design that delivers

472

a unique OS and construction cost for the education building. Designers who are trying to

473

maximize the occupant satisfaction with human bioeffluents/body odor levels of this building

474

while keeping its construction cost to a minimum need to evaluate the impact of various building

475

designs on these two important criteria. Analyzing the OS and cost performance of all these

476

1.4x1030 feasible alternatives to identify an optimal design is impractical due to its prohibitive

477

computational time and effort. To support building designers in this challenging task, the

478

developed model is used to optimize the design of this application example in order to generate

479

and analyze optimal trade-offs between its two optimization objectives of maximizing OS and

480

minimizing construction cost. The model was able to generate 137 non-dominated near-optimal

481

solutions, where each represents a unique and optimal trade-off among the two objective

482

functions, as shown in Fig. 8. This wide range of optimal tradeoffs between the OS objective and

483

construction cost objective includes two extreme solutions (a and b) that are highlighted in Fig.

484

8. The first extreme solution (a) provides the highest OS of 98.39% at a construction cost of

485

$5,718,636, while the second extreme solution (b) provides the least construction cost of

486

$3,573,381 with an OS of 54.03% (see Fig. 8 and Table 6).

487 488 489

490 491 492

Fig. 8.

Optimal trade-offs between the construction cost and occupant satisfaction with human bioeffluents/body odor levels for the school building example.

Fig. 9.

Representative solutions of the application example.

493

Table 6.

Sample of optimal non-dominated solutions.

Design variables

Solution (a)

Solution (b)

Solution (c)

(See. Fig 10a)

(See. Fig 10b)

(See. Fig 10c)

25 m

27 m

23 m

Building length

40 m

30 m

39 m

Building Orientation (O) Floor height (FH)

166⁰ 4.5 m

51⁰ 2.8 m

51⁰ 2.8 m

Ventilation shaft height (SH)

3.0 m

0.5 m

3.0 m

Atrium width (AW) Vents length (VL1 and VL2)

5m 86 m, 68 m

5m 51 m, 49 m

5m 66 m, 63 m

Classroom layout selection (L) Building width

Window to wall ratio of

classrooms (WWRc)

83%

21%

21%

the atrium (WWRa) the shaft (WWRs)

79% 78%

11% 0%

89% 0%

Occupancy density of classroom

1 (OD1,1 and OD1,2)

25, 25

24, 21

25, 24

2 (OD2,1 and OD2,2) 3 (OD3,1 and OD3,2)

25, 20 35, 33

25, 20 32, 35

24, 20 32, 35

4 (OD4,1 and OD4,2)

56, 56

62, 62

57, 57

5 (OD5,1 and OD5,2)

72, 72

80, 80

80, 80

98.39% $5,718,636

54.03% $3,573,381

90.06% $4,055,775

Occupant satisfaction with human bioeffluents levels Construction cost

494

495 496

Fig. 10.

Classroom layout selection for solutions (a), (b) and (c)

497

The first extreme solution (a) in the generated Pareto optimal front was able to achieve

498

the maximum OS (98.39%) for the education building example by maximizing natural

499

ventilation airflows and size of classrooms to slow the CO2 accumulation over time while

500

keeping construction cost to a minimum. As shown in Fig. 9 and Table 6, this was achieved by

501

(1) selecting a classroom layout that places 61% of the school students in wing A of the building

502

that has better natural ventilation due to the orientation of the building and the prevailing wind

503

direction; (2) selecting a building orientation of 166⁰ that maximize the stack ventilation; (3)

504

increasing floor height to 4.5 m to maximize the volume of indoor air in classroom volumes and

505

the stack effect in the building; (4) raising the height of the ventilation shaft to 3 m to increase

506

the stack effect; (5) reducing the width of the atrium to its minimum value (5 m) which raises its

507

temperature to facilitate buoyancy driven airflows; (6) selecting a large vent length of 85.6 m m

508

in wings A and 68 m in wing B of the building to maximize its natural ventilation; (7) increasing

509

the windows to wall ratios of classrooms, atrium and shaft (83, 79%, and 78%, respectively) to

510

increase difeerences between indoor and outdoor temperatures to enhance bouancy driven

511

airflows; (8) reducing overall classroom occupancy densities of the building to 30.4 occupants

512

per 100 m2 to minimize CO2 concentrations in classrooms.

513

On the other extreme of the generated optimal trade-off solutions, solution (b) was able to

514

achieve the lowest construction cost ($3,485,507) by: (1) selecting a building orientation of 51⁰

515

that enables the high density classrooms in wing B to face the prevailing wind direction in this

516

geographical location to improve their wind-driven natural ventilation; (2) reducing building

517

floor height to its minimum allowed limit of 2.8 m to reduce the building construction cost; (3)

518

lowering the height of the ventilation shaft to 0.5 m to reduce its construction cost; (4)

519

minimizing the atrium width to 5 m to reduce construction costs; (5) reducing the length of

520

vents in Wings A and B to 51 m, and 49 m, respectively to reduce their cost; (6) decreasing the

521

windows to wall ratios of classrooms, atrium, and shaft to 21%, 11%, and 0%, respectively to

522

minimize the use of windows and their cost in the building; and (7) increasing overall classroom

523

occupancy densities of the building to 35.2 occupants per 100 m2 to minimize classroom space

524

and their construction cost, as shown in Fig. 9 and Table 6.

525

In addition to the two aforementioned extreme solutions, the optimization model was able

526

to generate 135 other trade-off solutions, including solution (c). This solution generated an OS

527

of 90.06%, which is lower than solution (a) performance of 98.39%; however, it provides a

528

lower construction cost of $4,055,775 compared to that of solution (a) that had a cost of

529

$5,718,636. This performance of solutions (c) was achieved by selecting an optimal set of

530

solutions for the design variables, as shown Fig. 9 and Table 6. This illustrates the capability of

531

the developed optimization model to generate a wide range of optimal trade-offs solutions, where

532

each provides a unique and optimal trade-off between the occupant satisfaction with human

533

bioeffluents/body odor levels and their construction cost, as shown in Fig. 8. This enables

534

designers to generate and analyze these optimal trade-offs and accordingly identify an optimal

535

set of design decisions that strikes an optimal balance between the two conflicting optimization

536

objectives considered in the developed model. Furthermore, the application example results

537

clearly illustrate the impact of the education building design decisions on its occupant

538

satisfaction with human bioeffluents/body odor levels and cost and the need for designers to

539

consider and optimize these impacts during the design of naturally-ventilated education

540

buildings.

541

6. Summary and Conclusions

542

The paper presented the development of a multi-objective optimization model for

543

optimizing the design of naturally-ventilated education buildings in order to maximize their

544

occupant satisfaction with human bioeffluents/body odor levels while minimizing their building

545

construction cost. The multi-objective optimization model is developed in three main stages: (1)

546

model formulation stage that identifies relevant design variables, objective functions and

547

constraints; (2) model implementation stage that executes the optimization computations; and (3)

548

model evaluation stage that analyzes the performance of the developed model using an

549

application example of a school building. The results of this application example analysis

550

highlight the new and unique capabilities of the developed model in generating a wide range of

551

Pareto-optimal design solutions, where each provides a unique and optimal trade-off between

552

occupant satisfaction with human bioeffluents/body odor levels and construction cost of

553

education buildings.

554

The model was developed based on a number of assumptions, including: (1) outdoor air

555

quality in the educational building location is acceptable and does not include more contaminants

556

than indoor air; (2) the scope of the model is limited to naturally-ventilated education buildings

557

that do not integrate mechanical ventilation or HVAC systems; (3) the model minimizes only

558

building construction cost and does not consider other costs such as energy, productivity, health-

559

related, operation, and maintenance costs; (4) calculation of CO2 levels in classroom spaces

560

assumes the most conservative case when ventilation is provided only through vents and all

561

windows are closed; and (5) optimal window sizes are selected from a designer-specified range

562

of feasible sizes for each window that provide the required level of lighting in classrooms.

563

The primary contribution of this research to the body of knowledge include (a) a novel

564

methodology for measuring and quantifying the impact of the design decisions of naturally-

565

ventilated education buildings on their occupant satisfaction with human bioeffluents/body odor

566

levels and construction cost, and (b) an original multi-objective optimization model that is

567

capable of generating optimal trade-offs between these two critical design objectives. These new

568

and innovative capabilities are expected to improve current design practices of naturally-

569

ventilated education buildings and will contribute to maximizing their human bioeffluents/body

570

odor satisfaction of occupants while minimizing their construction cost. The scope of the

571

developed model can be expanded in future research to enable the consideration and

572

optimization of additional (i) costs such as energy, productivity, health-related, and maintenance

573

costs; and (ii) optimization objectives such as indoor air quality, thermal comfort, energy

574

consumption, and life cycle costs of education buildings to generate optimal trade-offs among

575

them.

576

Acknowledgement

577

This material is based upon work supported by the Universidad Panamericana, the

578

Consejo Nacional de Ciencia y Tecnología (CONACYT) and the ZJU-UIUC Institute Research

579

Program. Any opinions, findings, conclusions, or recommendations expressed in this publication

580

are those of the writers and do not necessarily reflect the views of Universidad Panamericana,

581

CONACYT and ZJU-UIUC Institute Research Program.

582

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583

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Model Formulation Objective Functions

Design Variables 1. Classroom layout selection 2. Building orientation 3. Floor height

1. Maximize occupant satisfaction with human bioeffluents /body odor levels (OS)

6. Vents length

1. Design variable limits 2. Construction budget 3. Residual space

4. Ventilation shaft height 5. Atrium width

Constraints

2. Minimize construction cost (CC)

7. Classroom window/wall ratio 8. Atrium window/wall ratio 9. Shaft window/wall ratio 10. Classroom occupancy density

Model Implementation 1. Initialization module 2. Optimization module 3. Objective function module Model Evaluation

OS

CC

Classroom Types

(a) Centralized C

A

B

D

,

,

…

Start/End of Wing

,

-

,

,

…

,

-

Atrium

5 …

,

Wing A (w=1) Wing B (w=2)

Start of Floor

,

-

,

Wing C

,

…

,

Wing D

-

,

,

Wing A Floor 2

Floor 1 (f=1)

(b) Semi-enclosed A

C

,

,

…

,

-

,

,

…

,

-

,

,

…

,

-

,

,

…

D Wing A

Wing C

Atrium

Wing D

Floor 1

(c) Linear

Wing A Floor 2

A ,

,

…

,

-

,

,

…

,

-

,

,

…

B Wing A Atrium

Wing B Floor 1

(d) Attached

Wing A Floor 2 Classroom layout selection

A ,

,

…

Wing A Atrium

Classroom type (c)

,

-

,

,

…

,

Wing A

Floor 1 Floor 2 Example classroom types

Where: f = floor, w = wing, and c = classroom

1

2

3

4

5

Daycare (age <4)

Classroom (age 5-8)

Classroom (age 9+)

Lecture Classroom

Lecture Hall (Fixed seats)

Occupants

20

25

40

65

120

Density (#/100 m2)1

25

25

35

65

150

Sides Ratio(1:x)

1.5

1.5

1.8

1.5

1.8

Description1

1From

…

occupancy categories and density default values in Table 6-1 Standard 62.1 (ASHRAE 2010)

True North

Building orientation (O)

Window to wall ratio of classroom (WWRc)

Window to wall ratio of shaft (WWRs)

Window to wall ratio of atrium (WWRa)

Building direction Wing A

Wing B Shaft height (SH) Vents length (VL)

Floor height (FH)

Atrium width (AW)

(a) CO2 concentration variation over one year in a classroom 3,000 CO2 level [ppm] 2,500 2,000 1,500 1,000 500

(b) CO2 concentration variation in a classroom on March 28th Occupnats

CO2 level [ppm]

1,250 1,000

Dec 31

Nov 05

Oct 08

Sep 10

Aug 13

Jul 16

Jun 18

May 21

Apr 23

Mar 26

Feb 26

Jan 29

Jan 01 1,500

Dec 03

Time [day]

0

30 25

Designer-specified acceptable level of CO2: 1000 ppm

750

20 15

650 ppm

500 Outdoor CO2

10

level: 350 ppm

250 0

5 Simulation timesteps of 15 min (s)

1 hr

0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84 88 92 96 CO2 level limit

Classroom CO2 level

Occupants

0

1,500

t11,87,45 = 0 s

CO2 level [ppm]

t11,87,43 = 533 s

t11,87,34 = 900 s

Occupnats

30

t11,87,48 = 846 s

1,250

25 1,130 ppm

1,016 ppm

20

1,000 811 ppm

744 ppm

750

15

500

10

250

T = 900 s

5

Simulation timesteps (s) 0 0 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 B F CO2 level limit Classroom CO2 level Occupants Total complying daily time with designer-specified CO2 levels Total non-complying daily time with designer-specified CO2 specified levels ,

=

23,876 ∗ 100 = 85.58% (63 − 33 + 1) ∗ 900

Major group element:

A Substructure

Individual element:

A1030 Slab on grade

Assembly item:

4 in reinforced concrete slab with recycled vapor barrier and granular base

Assembly unit cost:

$ 5.84 / S.F.

Building length (Y):

XB=10 m Y=28 m

AW=6 m XA=13 m Design solution

28 m

Wing A width (XA):

13 m

Atrium width (AW):

6m

Wing B width (XB):

10 m

Building Width (X= XA+AW+XB):

29 m

Slab area (X*Y):

812 m2 8,740.3 S.F.

A1030 Slab on grade cost

$ 51,043.35

Start

Initialization Module Project Input Data (1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Weather data Classroom data Building data Daily schedule of classroom occupancy Construction cost data Contam simulation tool parameters EnergyPlus simulation program parameters Design variable constraints Budget (Bmax) and residual space (RSmax) constraints NSGAII algorithm parameters

Optimization Module

Objective Function Module Solutions (s = 1)

Generate random solutions (s = 1 to S) for initial population P1 of first generation (g=1)

No

Generate building design Design Variables

First generation (g=1) Yes

Sort and rank all solutions (s=1 to S) of initial population P1 based on Pareto optimal rank and crowding distance Generate child population Cg using tournament selection, recombination and mutation Combine child population Cg and parent population Pg to form a new combined population Ng Sort and rank all solutions (s = 1 to S) of combined population Ng based on Pareto optimal rank and crowding distance

Calculate CC objective function and residual space (RS)

Yes

CC ≤ Bmax RS ≤ RSmax

No

Discard and replace solution

Generate simulation files (PRJ, XML, VEF and IDF files)

Perform Contam-EnergyPlus co-simulation Temperatures Airflows Contam

Keep top S solution to form next generation’s parent population Pg+1 No

Last generation

Calculate OS objective function from co-simulation results

Yes Yes

Next generation (g = g + 1)

Last solution No

End

Next solution (s = s + 1)

100%

Occupant satisfaction with human bioeffluents/body odor levels (OS) [%] Solution (c) CC: $4.06M OS: 90.6%

Solution (a) CC: $5.72M OS: 98.4%

90%

80%

70%

60%

50% Solution (b) CC: $3.57M OS: 54.0%

40% $3.4

$3.6

$3.8

$4.0

Construction cost (CC) [Millions $] $4.2

$4.4

$4.6

Non-optimal dominated solutions

$4.8

$5.0

$5.2

$5.4

$5.6

Optimal non-dominated solutions

$5.8

$6.0

Solution (b) CC: $3.57M OS: 54.0%

Solution (c) CC: $4.06M OS: 90.6%

Solution (a) CC: $5.72M OS: 98.4%

Floor 1 (f=1)

Classrooms (c)

Floor 2 (f=2)

Wing A (w=1)

Wing B (w=2)

1 2 3

4 5 6

7 8 9

10

11 12

3 2 2 − 4 3 3 − 1 2 2 − 5 3 1 Classroom layout selection (Lfw,c):

4 2 1 − 5 3 1 − 2 2 2 − 3 3 3 5 4 1 − 3 3 2 − 3 1 2 2 − 3 2

Solution (a)

Solution (b)

Floor 2 Wing B RS

1

4

5

2

Floor 1 Wing B

Floor 1 Wing A

2 3

3 4

5

5 Floor 1 Wing A

4

3

1

3

2

1 1

3

Floor 1 Wing A

1 3

3

3

Floor 2 Wing B

2

2

Floor 2 Wing B

Floor 2 Wing A

Floor 1 Wing B

Floor 2 Wing A

1

2

3

2

2

2

2

3

3

3

Floor 2 Wing A

2 2

Solution (c)

Floor 1 Wing B

September 4, 2019 Chao-Hsin Lin, Ph.D. Editor Building and Environment Ms. Ref. No.: BAE-D-19-01641 Title: Optimal design of classroom spaces in naturally-ventilated buildings to maximize occupant satisfaction with human bioeffluents/body odor levels Authors: Dario F Acosta-Acosta and Khaled El-Rayes Journal: Journals of Building and Environment

Dear Mr. Chao-Hsin Lin, The highlights of this submitted manuscript include:  A novel methodology is developed for measuring and quantifying the impact of the design decisions of naturally-ventilated education buildings on classroom acceptability in terms of human bioeffluents/body odor; 

An original multi-objective optimization model is elaborated that is capable of generating optimal trade-offs between two critical design objectives of maximizing occupant satisfaction with human bioeffluents/body odor levels and minimizing building construction cost;

 The developed model optimizes ten design variables that represent the design decisions of education buildings that have an impact on these conflicting optimization objectives; and  Practical capabilities of the developed model are expected to improve current design practices of naturally-ventilated education buildings and will contribute to maximizing occupant satisfaction with human bioeffluents/body odor levels while minimizing their construction cost. Please let me know if you need any additional information. Best Regards, Dario F Acosta Acosta PhD Candidate, (the corresponding author) Department of Civil and Environmental Engineering University of Illinois at Urbana-Champaign 3112 Newmark Civil Engineering Laboratory Urbana, Illinois, 61801 and affiliated to Universidad Panamericana Campus Guadalajara Tel: +52 (33) 3025-5984 Email: [email protected] / [email protected]

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: