Avaliação probabilística da carga variável de projeto para garagens brasileiras de veículos leves

Costa, Luis Gustavo Lopes; Beck, André Teófilo

Luis Gustavo Lopes Costa

conceptualization

computer programming

data manipulation

formal analysis

methodology

writing

Universidade de São Paulo - USP, Escola de Engenharia de São Carlos, Departamento de Engenharia de Estruturas, São Carlos, SP, Brasil

https://orcid.org/0000-0003-3584-2112

André Teófilo Beck

formal analysis

writing review

proofreading

supervision

Universidade de São Paulo - USP, Escola de Engenharia de São Carlos, Departamento de Engenharia de Estruturas, São Carlos, SP, Brasil

https://orcid.org/0000-0003-4127-5337

Corresponding author: André Teófilo Beck. E-mail: atbeck@sc.usp.br

Conflict of interest: Nothing to declare.

Author contributions: LGLC: conceptualization, computer programming, data manipulation, formal analysis, methodology, writing; ATB: formal analysis, writing review, proofreading, supervision.

Editors: Diogo Ribeiro, Guilherme Aris Parsekian.

Figuras (15)
Tabelas (5)

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Figure 1
Typical realization of a mixed rectangular wave renewal processes with given probability density function.

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Figure 2
Vehicle placement in parking spot and load distribution.

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Figure 3
Schematic representations of the daily fluctuations of the total number of parked vehicles (

N

) in different types of car parks.

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Figure 2
Vehicle placement in parking spot and load distribution.

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Figure 3
Schematic representations of the daily fluctuations of the total number of parked vehicles (

N

) in different types of car parks.

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Figure 4
Registrations of one-liter engine passenger vehicles. Data source: ANFAVEA [⁴¹41 Associação Nacional dos Fabricantes de Veículos Automotores, “Anuário da Indústria Automobilística Brasileira 2023." ANFAVEA. https://anfavea.com.br/site/anuarios/ (Accessed: Apr 22, 2023).
https://anfavea.com.br/site/anuarios/... ].

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Figure 5
Registrations of passenger vehicles by powertrain technology. Data source: ANFAVEA [⁴¹41 Associação Nacional dos Fabricantes de Veículos Automotores, “Anuário da Indústria Automobilística Brasileira 2023." ANFAVEA. https://anfavea.com.br/site/anuarios/ (Accessed: Apr 22, 2023).
https://anfavea.com.br/site/anuarios/... ].

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Figure 6
Evolution of the mean curb weight of the Brazilian fleet of passenger vehicles from 2003 to 2022 (each point represents 5000 vehicle sales).

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Figure 7
Age distribution of the Brazilian fleet of passenger vehicles in circulation. Data source: Sindipeças [⁴⁶46 Sindicato Nacional da Indústria de Componentes para Veículos Automotores, “Relatório da Frota Circulante 2023." SINDIPEÇAS. https://www.sindipecas.org.br/sindinews/Economia/2023/RelatorioFrotaCirculante_2023.pdf (Accessed: Apr 22, 2023).
https://www.sindipecas.org.br/sindinews/... ].

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Figure 8
Curb weight distribution of the Brazilian circulating fleet of (a) passenger vehicles and (b) light commercial vehicles.

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Figure 9
Curb weight of the Brazilian fleet of light-duty vehicles in circulation.

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Figure 10
Characteristic live load for Brazilian light-duty vehicles parking garages.

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Figure 11
Sensitivity of the 50-year maximum EUDL with respect to the load effect.

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Figure 12
Sensitivity of the 50-year maximum EUDL with respect to temporal parameters.

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Figure 13
Sensitivity of the 50-year maximum EUDL with respect to forecast scenarios of fleet electrification in 2030 and 2035.

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Figure 14
1-year, 50-year and 140-year extreme value distributions for

A = 12 m^{2}

,

κ = 2.4

, and

n = 4

and corresponding bias factors and coefficients of variation.

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Figure 11
Sensitivity of the 50-year maximum EUDL with respect to the load effect.

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Figure 12
Sensitivity of the 50-year maximum EUDL with respect to temporal parameters.

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Figure 13
Sensitivity of the 50-year maximum EUDL with respect to forecast scenarios of fleet electrification in 2030 and 2035.

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Figure 14
1-year, 50-year and 140-year extreme value distributions for

A = 12 m^{2}

,

κ = 2.4

, and

n = 4

and corresponding bias factors and coefficients of variation.

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Figure 15
Variation of embodied carbon with design live load for a hypothetical four-story reinforced concrete building with flat slabs. Data source: Hawkins et al. [²⁸28 W. Hawkins, A. Peters, and T. Mander, "A weight off your mind: floor loadings and the climate emergency," Struct. Eng., vol. 99, no. 5, pp. 18-20, May 2021.].

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Table 1
Parking in residential and commercial real estate developments in the city of São Paulo from 1985 to 2013.

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Table 2
International market comparison of passenger vehicle fleet characteristics.

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Table 3
Temporal variability characteristics for different types of car parks.

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Table 4
Characteristics of the Brazilian fleet of passenger vehicles from 2013 to 2022.

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Table 5
Forecasted composition of the fleet in circulation in 2030 and 2035 [⁴⁸48 Associação Nacional dos Fabricantes de Veículos Automotores, "O caminho da descarbonização do setor automotivo no Brasil." ANFAVEA, 2021. https://anfavea.com.br/docs/APRESENTA%C3%87%C3%83O_ANFAVEA_E_BCG.pdf (Accessed: Apr 22, 2023).
https://anfavea.com.br/docs/APRESENTA%C3... ] and corresponding estimated average weights and standard deviations

Type of building use	Number of parking spots	Gross built area [m²]	Gross area per parking spot [m²]	Parking fraction of gross area^*	Net built area [m²]	Net area per parking spot [m²]	Parking fraction of net area^* * To estimate the percentage of area dedicated to parking, an average of 27.9 m2 per parking spot was used. Data sources: EMBRAESP, datasets made publicly available by the Center for Metropolitan Studies (CEM) in references [10], [11].
Residential	1,150,276	113,644,628	98.8	28.2%	61,626,189	53.6	52.1%
Commercial	107,631	8,287,281	77.0	36.2%	4,013,089	37.3	74.8%
Total	1,257,907	121,931,909	96.9	28.8%	65,639,277	52.2	53.5%

	Brazil (2013)	China (2014)	EU-28 (2015)	India (2015)	Japan (2011)	Mexico (2014)	Saudi Arabia (2012)	South Korea (2014^* * South Korea footprint reflects 2011 fleet, engine power reflects 2013 fleet.** Fuel consumption and CO2 emission tested on the New European Driving Cycle (NEDC). Data sources: International Council on Clean Transportation (ICCT) Reports [25], [26]. )	USA (2015)
Sales [million]	3.0	20.7	13.7	2.8	3.5	0.7	0.4	1.4	7.5
Engine displacement [L]	1.4	1.7	1.6	1.3	1.4	1.8	2.3	2.0	2.4
Engine power [kW]	76	98	93	59	78	95	120	120	149
Curb weight [metric ton]	1.1	1.4	1.4	1.1	1.2	1.2	1.4	1.5	1.6
Footprint [m²]	3.7	4.1	4.0	3.5	3.7	3.8	4.2	4.2	4.3
Fuel consumption^** [L/100 km]	6.8	7.3	5.1	5.3	5.8	6.3	6.8	6.4	6.8
CO₂ emission^** [g/km]	154	171	120	123	136	147	158	148	158
Petrol	6%	98%	44%	47%	86%	99%	-	51%	94%
Diesel	0%	2%	52%	50%	0%	1%	-	39%	1%
Hybrid-electric	0%	0%	2%	0%	13%	0%	-	0%	5%
Others	94%	0%	2%	3%	1%	0%	-	10%	0%
Manual transmission	83%	49%	75%	92%	1%	56%	-	2%	5%
Automatic transmission	17%	51%	25%	8%	99%	44%	-	98%	95%

Location of the parking garage	Number of busy days per year $t_{y}$ [days/yr]	Mean busy time per day ${\bar{t}}_{d}$ [h/day]	Mean dwell time ${\bar{t}}_{u}$ [h]	Mean number of cars per day $\bar{ρ}$ [cars/day]
In a residential area	360	17	8	2.1
In a commercial area or an area with offices, factories, etc.	300	8 - 12	3 - 6	1.0 - 3.0
Connected to an assembly hall, to an area with sport facilities, etc.	50 - 360	2.5	2.5	1.0
Connected to an airport, railway station or other building associated with transports	360	13 - 18	10 - 14	1.3

Year	Sales Top50 [million]	Sales total [million]	Market share Top 50	Engine displ. [cm³]	Curb weight [kg]	Length [mm]	Footprint [m²]	Engine power [kW]	PTWR^* * Power to weight ratio. [kW/kg]
2003	1.20	1.21	98.8%	1383	1032	3956	3.53	70	0.0638
2004	1.30	1.31	99.0%	1407	1039	3965	3.50	69	0.0637
2005	1.42	1.44	98.7%	1414	1052	3974	3.50	71	0.0645
2006	1.60	1.63	97.9%	1440	1064	4002	3.51	71	0.0646
2007	2.00	2.09	96.0%	1478	1077	4016	3.52	73	0.0644
2008	2.19	2.34	93.5%	1469	1080	4029	3.53	74	0.0654
2009	2.44	2.64	92.3%	1437	1078	4040	3.53	74	0.0654
2010	2.60	2.86	91.0%	1420	1076	4033	3.54	74	0.0659
2011	2.46	2.90	84.9%	1410	1081	4039	3.57	76	0.0678
2012	2.69	3.12	86.4%	1428	1091	4059	3.59	76	0.0678
2013	2.71	3.04	88.9%	1456	1108	4084	3.64	78	0.0683
2014	2.48	2.80	88.7%	1468	1123	4089	3.67	81	0.0690
2015	1.89	2.12	89.1%	1487	1133	4108	3.71	83	0.0707
2016	1.54	1.69	91.2%	1506	1149	4117	3.74	85	0.0716
2017	1.71	1.86	92.2%	1497	1159	4127	3.75	87	0.0729
2018	1.92	2.10	91.2%	1493	1166	4139	3.78	87	0.0729
2019	2.09	2.26	92.6%	1477	1179	4149	3.80	89	0.0730
2020	1.51	1.62	93.2%	1391	1211	4183	3.82	92	0.0737
2021	1.45	1.56	93.0%	1445	1243	4194	3.84	93	0.0722
2022	1.51	1.58	96.0%	1306	1245	4212	3.85	90	0.0704
Cumulative	38.72	42.17	91.8%	1445	1115	4074	3.64	79	0.0682

Year	Current	Inertial (L1)		Global Convergence (L2)
Year	2022	2030	2035	2030	2035
xEVs	-	2%	10%	4%	18%
ICEVs	100%	98%	90%	96%	82%
Avg. weight [kg]	1184.9	1318.5	1360.3	1328.9	1402.2
Std. dev. Weight [kg]	293.5	329.6	340.1	332.2	350.5
Avg. weight +20% [kg]	1421.9	1582.1	1632.4	1594.7	1682.6
Std. dev. weight +20% [kg]	352.2	395.5	408.1	398.7	420.7

[1] Corresponding author: André Teófilo Beck. E-mail: atbeck@sc.usp.br

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Avaliação probabilística da carga variável de projeto para garagens brasileiras de veículos leves

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