ChapterPDF Available

FIRE PERFORMANCE EVALUATION OF A RESIDENTIAL BUILDING: STRUCTURAL ANALAYSIS AND RESPONSE

Authors:

Abstract and Figures

Following preliminary research and analysis, an eight-story residential building was created using SAP2000. Several fire scenarios were simulated on different floors, including on exposed primary carrier steel elements, to generate strength-temperature findings. The strength of the steel elements was determined by analyzing their axial compressive force, moment, and shear forces under various conditions, including yield, breaking stresses, and changes in the modulus of elasticity. Additionally, the temperature parameters of the reinforcement mesh used in the concrete block at different depths were assessed by considering the variation in axial, moment, and shear forces if all steel elements were protected or not. The amount of displacement of the slab due to the high-temperature effect over time as also evaluated and compared to the allowable deflection. Based on these findings, the impact of fire on the performance of steel structures was analyzed in accordance with the ISO 834 standard adopted by European Standards. To develop a new mathematical model, the Eurocode design equations (CEN, 2005a, 2005b, 2005c), as well as the Visual Basic Applications (VBA) in Excel software, were employed.
Content may be subject to copyright.
International Research in Engineering Sciences
485
ISBN: 978-625-6971-50-9
486
FIRE PERFORMANCE EVALUATION OF A RESIDENTIAL
BUILDING: STRUCTURAL ANALAYSIS AND RESPONSE
Dr. Burak Kaan ÇIRPICI
Erzurum Technical University
Engineering and Architecture Faculty
Civil Engineering Department
ORCID: 0000-0002-4310-2782
Mr. Abdulsamet AKTURK
Erzurum Technical University
Engineering and Architecture Faculty
Civil Engineering Department
1. Introduction
In the years following the September 11 attacks on the World Trade Center in
New York City in 2001, numerous studies have focused on the impact of fire on
building structures. The collapse of the World Trade Center towers demonstrated
the need for a deeper understanding of the behavior of tall buildings during fire.
During the lifetime of a structure, fire is one of the most severe threats. It is crucial
to ensure that structural frames have adequate fire resistance for a reasonable
range of different fire scenarios. The collapse of columns during a fire can led to
the progressive collapse of a structure, which typically results in substantial
economic loss and loss of life. An immediate study is being conducted on the
topic of fire-induced progressive collapse. It is necessary to analyze the more
realistic performance of columns under fire, including the post-buckling stage.
The columns of a structure are typically constrained axially and rotationally by
neighboring structural components (Hozjan et al., 2008). Many design
methodologies (Li et al., 2010; Sun et al., 2013; Yao & Hu, 2015) have
investigated the buckling or critical temperature of columns under fire. Huang
and Tan (2003) enhance the Rankine method for predicting the critical
temperatures of steel construction columns under restraint. Using the alternative
load path approach (APM) as proposed by GSA (GSA, 2013) and DoD (DoD,
2009), structural progressive collapse in civil engineering has been extensively
explored. Composed of a concrete structure (Brunesi et al., 2015; Kokot et al.,
International Research in Engineering Sciences
487
2012), a steel frame (Liu et al., 2015; Zhu et al., 2018), and a long-span structure
(Tian et al., 2017), comparative studies between nonlinear static and dynamic
analyses have been thoroughly studied. Although progressive collapse under fire
is a rising problem, comparatively less research has been conducted on the
progressive collapse of steel structures under fire compared to the investigation
of progressive collapse under explosion and impact. Sun et al. (2012) employ the
explicit integration approach to examine the dynamic process of progressive
structure collapse under fire conditions. Lou et al. (2018) conducted a fire
experiment and finite element (FE) simulation of a full-scale steel portal frame to
study progressive collapse. Additionally, Suwondo et al. (2019) examine the fire-
induced progressive collapse of three-dimensional composite steel frames
following an earthquake. A globallocal analytical method is proposed in Jiang
et al. (2020b) and Jiang et al. (2020a) for evaluating the robustness of planar steel
frames induced by the failure of a side column in situations of localized fire.
ABAQUS and SAFIR are used to study the progressive collapse processes of
steel frames with infill walls (Shan & Li, 2020) and composite floor slabs (Gernay
& Khorasani, 2020) subjected to fire scenarios. In spite of this, new structural
analysis techniques (e.g., utilizing machine-learning algorithms (Fu, 2020)) and
fundamental theories to analyze progressive collapse mechanisms for diverse
systems under varied threats are required (Foad Kiakojouri et al., 2020). These
investigations demonstrate conclusively that the increasing collapse of steel
structures exposed to fire is a serious problem. In this context, earlier experiments
and dynamic FE analyses for the progressive collapse of steel frames during a
fire have primarily focused on basic fire scenarios. In ANSYS's numerical
analysis of the behavior of a steel frame (Ren, 2020), various scenarios of fire
propagation are investigated. Yet, there is a lack of knowledge regarding the
dynamic progressive collapse of steel frames in various fire conditions. Hence,
an event-based analytical method is urgently required to investigate the dynamic
performance of progressive collapse for steel frames exposed to various fire
situations. F Kiakojouri et al. (2020) analyze material strain rate impacts and
column removal time in their progressive collapse assessment utilizing dynamic
analysis. A measure of structural complexity to detect load paths is provided in
De Biagi and Chiaia (2013). The above research demonstrates that the impacts of
column removal time and structural complexity are crucial to the mechanics of
progressive collapse.
Following preliminary research and analysis, an eight-story residential
building was created using SAP2000. Several fire scenarios were simulated on
different floors, including on exposed primary carrier steel elements, to generate
ISBN: 978-625-6971-50-9
488
strength-temperature findings. The strength of the steel elements was determined
by analyzing their axial compressive force, moment, and shear forces under
various conditions, including yield, breaking stresses, and changes in the modulus
of elasticity. Additionally, the temperature parameters of the reinforcement mesh
used in the concrete block at different depths were assessed by considering the
variation in axial, moment, and shear forces if all steel elements were protected
or not. The amount of displacement of the slab due to the high-temperature effect
over time was also evaluated and compared to the allowable deflection. Based on
these findings, the impact of fire on the performance of steel structures was
analyzed in accordance with the ISO 834 standard adopted by European
Standards. To develop a new mathematical model, the Eurocode design equations
(CEN, 2005a, 2005b, 2005c), as well as the Visual Basic Applications (VBA) in
Excel software, were employed.
2. Material and Methods
2.1. Design and Modelling of Steel Building in SAP2000
The structure has been designed with the utmost precision and attention to
detail to account for both vertical and earthquake loads. Its floor plan comprises
a basement level and eight additional floors, measuring 32.9 meters in length and
23.4 meters in width shown in Figure . The height of the basement level is 2.9
meters, while that of the ground floor stands at 4.0 meters. The remaining floors,
referred to as normal floors, have a height of 2.9 meters each. The axes of our
building have been identified to ensure the appropriate placement of load-bearing
elements. The structure consists of 15 axes with 8 openings in the X-direction
and 21 axes with 6 openings in the Y-direction presented in Figure. In accordance
with European standards, the materials to be used for the elements of our steel
structure have been identified. The material selection for the steel components
involved opting for S355 (ST52), whereas the composite decking has been chosen
to be C16/20 lightweight concrete. In accordance with the European material
classification for the designated S355 steel grade, the sections of the columns,
beams, and crossbars ranging from HE 100A to HE 1000A have been
automatically defined using the Auto Select function.
International Research in Engineering Sciences
489
(a)
(b)
Figure 1. (a) Architectural project floor plan. (b) Architectural project
section view
(a)
(b)
Figure 2. (a) View of horizontal axes (b) View of both horizontal and
vertical axes
The direction aspect of the column-beam design is of significant importance
to ensure compliance with the architectural project. With this in mind, the
column-beam layout has been carefully positioned to meet the appropriate
standards given in Figure. In accordance with the design requirements, the
ISBN: 978-625-6971-50-9
490
selection of a fixed support type shown in Figure, also known as a restrained
support, has been opted for to prevent both horizontal and vertical movements of
the attached element, as well as any rotations due to external forces.
(a)
(b)
Figure 3. (a) Column layout view (b) Beam layout view
In our design, the floor has been created for each level, and a subfloor has
been established to accommodate the load condition indicated in Figure. As part
of the structure's design, the floor slabs have been assigned a diaphragm to ensure
stability.
International Research in Engineering Sciences
491
Figure 4. Fixed support view
Figure 5. Floor modelling and view
Once the loads applied to the structure were determined according to the
standards mentioned above, the vertical loads utilized in the structural
calculations were established as per the following Table:
Table 1. The load used in the SAP2000 model
Location
Dead load
(kN/m2)
Live load
(kN/m2)
Roof floor
4
1
Standard floor
5
2
External wall load
3
-
The loading of the roof floor and normal floors has been applied to the slab,
while the external wall loads have been applied to the external beams. Our steel
design project will be constructed in the center of Erzurum Province, Turkey, and
ISBN: 978-625-6971-50-9
492
the soil class of the building site is ZC, which consists of very dense sand and
gravel, hard clay layers, or weathered, highly fractured weak rocks. The
earthquake site hazard level is DD-2 (the probability of exceeding it in 50 years
is 10%, and the recurrence period is 475 years).
According to Turkish Building Earthquake Code (TBDY, 2018), the
flexibility level consists of three parts: high ductility level (SMF) bearing
systems, mixed ductility level (IMF) bearing systems, and limited ductility level
(OMF) bearing systems. TBDY-2018 has determined R = 8, D = 2.5, and BYS =
5, S = 1.17%, S1 = 0.291, and TL = 6 based on the characteristics of the structures
where all seismic effects of Table 4.1 steel building bearing systems are met by
high eccentric or centrally braced steel frames with prevented torsion, according
to the seismic zone hazard report. In SAP2000, we defined the AISC standard
and analyzed the high-ductility load-bearing systems (SMF) using the design with
allowable stress (ASD) method.
3. Results
3.1. Design and Performance Analysis of Columns, Beams, and Floors at
Elevated Temperatures
Due to its high thermal conductivity, steel material transfers high temperatures
to other structural elements during a fire. As a result, steel elements heat up and
lose their strength. Permanent shape changes occur in steel at high temperatures,
resulting in elongation and differences at joint locations. This leads to a reduction
in the load-carrying capacity of steel columns and beams. High heat causes a
decrease in the elastic modulus and yield stress of steel elements, leading to
displacements in steel structural systems and the loss of their strength. The fire
compartment characteristics of the section affected by high temperature on the
column, beam, and slab are illustrated in Figures Figure , Figure and Figure.
International Research in Engineering Sciences
493
(b)
Figure 6: Fire compartment places (a) Ground floor (b) Normal floor
The section between the 2-5/C-D axes, where the fire scenario occurred, has
been enclosed within a blue circle. Due to the loss of strength in the corner
columns and beams of the structure, resulting in lateral displacement and
migration, the fire scenario was created in the corner section of the building. In
addition, in past fires, such as the Twin Towers of the World Trade Center, the
loss of strength of the corner columns and beams during the fire resulted in the
collapse of the towers onto the surrounding buildings. Therefore, this study also
focuses on the fire scenario in the corner section.
Figure 7. The corner areas (fire compartments) exposed to fire on the
ground floor and normal floors
ISBN: 978-625-6971-50-9
494
(a)
(b)
(c)
Figure 8. Floor beam sections (a) Ground floor (b) First floor (c) 2-3-4-5-6-
7. floors
3.1.1. Thermal Analysis of Columns and Beams
In the analysis, the load combination of 1.35Gk + 1.5Qk has been applied
according to Eurocode for load combinations. The fire compartment’s properties
are presented in Table. The fire resistance class is determined based on the fire
resistance duration obtained from fire resistance tests specified in European
standards TS 1263 and TS 4065, under appropriate heating and pressure
conditions, for a structural material and/or element, as shown below. The
selection of the duration is parallel to the population density and based on the F60
standard, with a range of 6089 minutes for residential structures, targeting a 60-
minute fire effect.
International Research in Engineering Sciences
495
The fire curve applied in the developed mathematical models is the ISO-834
Standard fire (ISO) (2014). The results obtained (temperature calculations, etc.)
are up to this duration. The fire load ratio applied in the models is taken at 90%
(PD 6688-1-2:2007, Table A.2. (CEN, 2002). In order to perform the structural
calculations and ensure the applicability of the developed mathematical model,
Microsoft Excel macros have been utilized. The temperature results of the steel
elements, based on the protected with plasterboard and unprotected conditions of
the column and beam members on different floors, are presented in Table 6.2.
Table 2. Fire compartment properties for the temperature prediction of
columns and beams
In column design, B (span length) represents the sum of half the lengths of the
beams carrying the load in the x direction, L; the sum of half the lengths of the
beams carrying the load in the y direction, H; the floor height of the column, Af;
the compartment area, Av; the area of opening in the compartment, h; the window
height in the compartment, At; the total surface area in the compartment, Gk; the
dead load, Qk; the live load, Et; the modulus of elasticity, fy; and the yield strength.
The protection methods for our steel elements include using either box
gypsum board or spray paint for protection in columns and only spray paint for
protection in beams due to its ease of use with a thickness of 12-16 mm and 18-
22 mm, respectively (Cirpici, 2020; Cirpici et al., 2020; Cirpici et al., 2016a;
Cirpici et al., 2016b). In determining the thicknesses of the gypsum board and
spray paint to be used in the protection method, minimum thicknesses were
ISBN: 978-625-6971-50-9
496
considered to ensure that the steel does not reach its limit temperature value of
550 within a duration of 60 minutes. It has been determined that the
temperature values of unprotected steel elements (columns and beams) for
different temperature degrees that may occur on different floors are between
938.7 ℃ and 940.3 ℃ for a 60-minute duration.
3.1.2. Thermal Analysis of Slabs
ATAPANEL Composite Floor Sheet Panel is used in composite flooring, and
its characteristics are specified in Figure. A lightweight concrete material with a
compressive strength of C16/20 (fck=16 N/mm2) has been selected for use in the
flooring system. A structural fire design has been carried out based on the detailed
plan of the structure, and composite flooring has been chosen for the floor system.
The effect of structural parameters on the fire performance of the flooring system
has been determined. A dead (permanent) load of 5 kN/m2 (3.5 kN/m2 + 1 kN/m2
for partitions + 0.5 kN/m2 for ceiling coverings, service gaps, and flooring
materials) and a live (imposed) load of 2 kN/m2 have been considered in all
analyses. According to Eurocode, the load combination of 1.35Gk + 1.5Qk has
been applied.
The structural analysis results indicate that the main beams used in the flooring
system underneath are HE 140 A for the ground floor, HE 180 A for the first
floor, and HE 160 A for the 2nd, 3rd, 4th, 5th, 6th, and 7th floors, while the secondary
beams are HE 120 A for the ground floor, HE 140 A for the first floor, and HE
140 A for the above-mentioned floors. Temperature calculations for the varying
parameters will be conducted using these beams. The yield strength used in the
model is fy=355 N/mm2 for both beam profiles and reinforcement. After the 60-
minute design, the displacement that occurred was calculated as 160.65 mm on
the ground floor and 155.44 mm on the upper floors, which exceeded the
allowable displacement limit of 149.58 mm depending on the flooring area
presented in Figure. A TSlab deflection calculation was also carried out according
to the study by Newman et al. (2006), and the value obtained was 150.63 mm.
According to the graph, there is no change in the deflection amount until the 41st
minute, after which an increase in deflection occurs, exceeding the allowable
limit at the 61st minute.
International Research in Engineering Sciences
497
󰇛󰇜
 

6
- Allowable vertical displacement
- Coefficient of thermal expansion of concrete
- Temperature at the bottom surface of the floor
- Temperature at the top surface of the floor
- Long side of the floor (long span)
- Short side of the floor (short span)
In case of fire, the concrete floor slabs in the composite floor system
experience an increase in temperature from their bottom surface. Due to the
temperature gradient and decreased strength at high temperatures along the
thickness direction, they undergo a change in direction. As a result of the
deflection of the floor slab, membrane stresses are generated, leading to an
increase in the load-carrying capacity. This phenomenon has been observed in
this study. In the mathematical model, the main beam of the floor on the 2-5/C-
D axis has a length of 5.1 meters, while the secondary beam has a length of 4.15
meters. The reinforcement mesh chosen for the floor is A393 mesh according to
BS 4483 ((BSI), 2005). The temperature of the A393 reinforcement under 60-
minute heat exposure at different depths (full depth, average depth, weighted
mean depth, and continuous depth) is presented in
Figure .
Figure 9. The properties of ATAPANEL Composite Floor Steel Decking
ISBN: 978-625-6971-50-9
498
Figure 10. The time-dependent vertical displacement (deflection) of the
flooring system
Figure 11 Temperature values of reinforcement at different depths inside the
concrete
Span
Temperature
(℃)
Depth locations
Continuous depth
594,948
Average depth
285,675
Full depth
177,659
Weighted mean
depth
377,468
International Research in Engineering Sciences
499
3.2. The Stress Analysis of Structural Elements Under Load Due to Fire
Scenarios at Different Temperatures and Floors
It has been determined that the temperature values of unprotected steel
elements (columns and beams) at different temperature levels that may occur on
different floors reach between 938.7 ℃ and 940.3 ℃ after 60 minutes. According
to Eurocode 4 Part 1.2, Table 3.2 (CEN, 2005c), the reduction factor for strength
at high temperatures is . The initial values for the yield strength,
ultimate strength and modulus of elasticity are 355 MPa, 510 MPa and 210×103
MPa, respectively at room temperature. Figure presents the change of yield
strength, ultimate strength and modulus of elasticity at elevated temperatures. In
fire scenarios where different temperatures can occur on different floors, values
for yield strength, fracture strength, and elasticity modules of steel members were
determined at 300-600-940 ℃. Using the obtained strength and modulus of
elasticity values at 300–600 and 940 for the column on the 4C axis and the
beam on the 25/C axis, we modified the material properties in SAP2000 and
observed changes in the axial, moment, and shear strengths of our structure
according to the analysis results. The decreased yield strength, ultimate strength,
and elastic modulus with temperature are presented in Table 6.6.
Figure 12. The variation in yield strength, ultimate strength, and modulus of
elasticity with respect to temperature
ISBN: 978-625-6971-50-9
500
3.2.1. Individual floor fires
The axial force, moment, and shear force values of the structural elements in
a selected corner fire compartment (with 4C axis column and 2-5/C console and
edge beams) at high temperatures (300-600-940 ℃) have been determined under
the effect of ISO834 Standard fire. These values are shown in Figures Figure,
Figure and Figure.
According to the findings obtained for the ground floor, it is observed that
there is a decrease in strength as temperature values increase. During the period
from room temperature to 940 ℃, under axial force, a decrease of 33.2% is
observed in the HE300A column, while a decrease of 99.4% is observed under
shear force. The HE140A console beam shows a decrease of 75.13% in moment
change and a decrease of 38.89% in shear force, while the HE140A edge beam
shows a moment change decrease of 24.74% and a shear force decrease of 6.16%.
As temperature levels increase, stress ratios also increase. It is observed from the
Figure that the HE300A column has ideal stress ratio values above 01 at 1.4 at
600 ℃ and 7.941 at 940 ℃, while the HE140A console beam has a value of 1.544
and the HE140A edge beam has a value of 6.699 at 940 ℃. Therefore, it is
understood that the steel elements lose their load-bearing capacities at the
specified temperatures.
As a result of the large fire occurred on the 1st floor, there was a decrease of
0.7% in the axial force of the HE280A column at room temperature, followed by
an increase of 13.52% up to 600 °C and then a decrease of 22.89% as the
temperature rose from 600 °C to 940 °C. The moment changed by 100%, and the
shear force decreased by 78.56%. As for the HE180A console beam, there was
an 80.62% decrease in moment change and a 62.4% decrease in shear force, while
the moment changes of the HE180A edge beam decreased by 52.58% and the
decrease in shear force was 25.73%. The stress ratios presented in Figure for the
HE280A column were 1.339 at 600 °C and 8.614 at 940 °C. For the HE180A
console beam, the ratio was 1.74 at 940 °C, and for the HE180A edge beam, it
was 3.752 at 940°C. These values indicate that the ideal stress ratio value of 0–1
was exceeded at the specified temperatures, indicating that the steel members lost
their load-bearing capacities at those temperatures.
As a result of the fire scenario created on the second floor, the HE300A
column experienced a 15.48% increase in axial force and a 100% decrease in
moment change, as well as a 95.53% decrease in shear force as the temperature
rose from room temperature to 940 °C. As for the HE160A console beam, there
was a 99.9% decrease in axial force, an 82.6% decrease in moment change, and
International Research in Engineering Sciences
501
a 59.37% decrease in shear force. The moment changes of the HE160A edge
beam decreased by 48.3%, and the decrease in shear force was 20.96%. The stress
ratios for the HE300A column were 1.08 at 600°C and 6.711 at 940 °C. For the
HE160A console beam, the ratio was 1.792 at 940 °C, and for the HE160A edge
beam, it was 4.543 at 940 °C. These values indicate that the ideal stress ratio
value of 01 was exceeded at the specified temperatures, indicating that the steel
members lost their load-bearing capacities at those temperatures indicated in
Figure.
For the third-floor fire scenario, during the temperature increase from room
temperature to 940 °C, the HE260A column experienced a 1.93% increase in
axial force, a 100% decrease in moment change, and an 88.68% decrease in shear
force. As for the HE160A console beam, there was a 93.3% decrease in axial
force, an 82.73% decrease in moment change, and a 61.91% decrease in shear
force. The moment changes of the HE160A edge beam decreased by 49.27%, and
the decrease in shear force was 21.32%. The stress ratio of the structural elements
on the 3rd floor after the fire is shown in Figure.
For the 4th floor fire scenario, during the temperature increase from room
temperature to 940 °C, the HE240A column experienced a 12.82% increase in
axial force, a 100% decrease in moment change, and a 96.49% decrease in shear
force. As for the HE160A console beam, there was a 99.96% decrease in axial
force, an 82.78% decrease in moment change, and a 59.9% decrease in shear
force. The moment changes of the HE160A edge beam decreased by 49.44%, and
the decrease in shear force was 20.85%. Figure displays the stress ratio of the
structural elements on the fourth floor subsequent to exposure to fire.
For the fifth-floor fire scenario, the HE240A column showed a 1.01% increase
in axial force, a 92.02% decrease in moment change, and a 93.32% decrease in
shear force during the temperature increase from room temperature to 940 ℃.
The HE160A console beam exhibited a 99.97% reduction in axial force, an
82.93% decrease in moment change, and a 59.73% decrease in shear force. The
moment changes for the HE160A edge beam decreased by 51.09%, and the
decrease in shear force was 21.55%. The stress ratio of the structural elements on
the 5th floor after the fire has been illustrated in Figure.
According to the results of the 6th floor fire scenario, the HE220A column
experiences a 15.93% increase in axial force, a 100% decrease in moment change,
and a 97.62% reduction in shear force during the period from room temperature
to 940 °C. The HE160A console beam experiences a 93.21% decrease in axial
force, an 82.91% decrease in moment change, and a 62.22% decrease in shear
ISBN: 978-625-6971-50-9
502
force. The moment change in the HE160A edge beam is reduced by 48.01%,
while the decrease in shear force is 19.76%. A visual representation of the stress
ratio of the structural elements on the sixth floor following the application of fire
is displayed in Figure.
For the top floor (7th floor), HE260A column experienced a 10.64% increase
(compression) in axial force, a 66.99% decrease in bending moment, and a
69.89% decrease in shear force during the period from room temperature to 940
℃. HE160A console beam experienced 100% decrease in axial force, an 84.3%
decrease in bending moment, and a 60.9% decrease in shear force, while the
HE160A edge beam experienced a 44.4% decrease in bending moment and a
17.16% decrease in shear force under axial force. Figure displays a visual
depiction of the stress ratio exhibited by the structural components located on the
third level of the building, subsequent to the impact of the fire.
In summary, these results suggest that the load-carrying ability of the steel
components decreases at the designated temperatures, especially after 600 ℃ for
columns and 940 ℃ for beams. This indicates that the steel elements lose their
load-bearing capacity at the specified temperatures.
Figure 13. Temperature-dependent variation in shear force (Vu) for beams
and columns on different levels
International Research in Engineering Sciences
503
Figure 14. Temperature-dependent variation in axial force (Pu) for columns
on different levels
Figure 15. Temperature-dependent variation in moment (Mu) for beams on
different levels
ISBN: 978-625-6971-50-9
504
(a)
(b)
(c)
(d)
Figure 16. The stress ratio of the columns and beams at the ground floor at
(a) 25⁰C, (b) 300⁰C, (c) 600⁰C, and (d) 940⁰C temperatures
International Research in Engineering Sciences
505
(a)
(b)
(c)
(d)
Figure 17. The stress ratio of the columns and beams at the 1st floor at (a)
25⁰C, (b) 300⁰C, (c) 600⁰C, and (d) 940⁰C temperatures
ISBN: 978-625-6971-50-9
506
(a)
(b)
(c)
(d)
Figure 18. The stress ratio of the columns and beams at the 2nd floor at (a)
25⁰C, (b) 300⁰C, (c) 600⁰C, and (d) 940⁰C temperatures
International Research in Engineering Sciences
507
(a)
(b)
(c)
(d)
Figure 19. The stress ratio of the columns and beams at the 3rd floor at (a)
25⁰C, (b) 300⁰C, (c) 600⁰C, and (d) 940⁰C temperatures
ISBN: 978-625-6971-50-9
508
(a)
(b)
(c)
(d)
Figure 20. The stress ratio of the columns and beams at the 4th floor at (a)
25⁰C, (b) 300⁰C, (c) 600⁰C, and (d) 940⁰C temperatures
International Research in Engineering Sciences
509
(a)
(b)
(c)
(d)
Figure 21. The stress ratio of the columns and beams at the 5th floor at (a)
25⁰C, (b) 300⁰C, (c) 600⁰C, and (d) 940⁰C temperatures
ISBN: 978-625-6971-50-9
510
(a)
(b)
(c)
(d)
Figure 22. The stress ratio of the columns and beams at the 6th floor at (a)
25⁰C, (b) 300⁰C, (c) 600⁰C, and (d) 940⁰C temperatures
International Research in Engineering Sciences
511
(a)
(b)
(c)
(d)
Figure 23. The stress ratio of the columns and beams at the 7th floor at (a)
25⁰C, (b) 300⁰C, (c) 600⁰C, and (d) 940⁰C temperatures
4. Conclusion
This study investigated several parameters that affect the fire performance of
steel structures. A design model of an eight-story residential project was created
using the SAP2000 program, and the sections of the structure elements were
determined. The temperatures of protected and unprotected steel structure
ISBN: 978-625-6971-50-9
512
elements were determined by applying the standard fire (ISO 834) for one hour
to the determined sections. Different passive protection materials (gypsum board
and intumescent paint) were used for protection. After the analysis, fire scenario
analyses were conducted. Changes in the steel load-bearing elements under
different temperatures were seen in the column-beam colour palette, and
increases in stress ratios were determined as temperatures increased. The
decreased (reduced) mechanical properties (strength and elasticity modulus) of
each structure element at high temperatures were determined based on the
obtained unprotected steel temperatures, and the structural performance of the
structure against fire was determined by conducting SAP2000 structural analyses
based on these mechanical properties. According to the Eurocode 3 standard, the
reduced mechanical properties (strength and elasticity modulus) of steel material
at high temperatures have been determined. At temperatures of 300 ℃, 450 ℃,
600 ℃, and 940 ℃, the strength of steel has lost approximately 1-2%, 7-8%,
60%, and 95% of its room temperature strength, respectively. The reduced tensile
strength has been determined based on the yield strength. To sum up, the findings
indicate that the load-bearing capacity of the steel elements decreases at the
specified temperatures, particularly after 600 for columns and 940 for
beams, implying a loss of load-bearing capacity of the steel components.
International Research in Engineering Sciences
513
References
(BSI), B. S. I. (2005). British Standard BS 4483:2005, Steel fabric for the
reinforcement of concrete - Specification. In. London.
(ISO), I. O. f. S. (2014). ISO 834-11:2014 Fire resistance tests - Elements of
building construction - Part 11: Specific requirements for the
assessment of fire protection to structural steel elements. In (pp. 53).
Brunesi, E., Nascimbene, R., Parisi, F., & Augenti, N. (2015). Progressive
collapse fragility of reinforced concrete framed structures through
incremental dynamic analysis. Engineering Structures, 104, 65-79.
doi:https://doi.org/10.1016/j.engstruct.2015.09.024
CEN. (2002). EN 1991-1-2:2002 Eurocode 1: Actions on structures. In Part
1.2: General actions - Actions on structures exposed to fire. BSI:
London.
CEN. (2005a). EN 1993-1-1: Eurocode 3. Design of Steel Structures. In Part
1-1: General rules and rules for buildings. BSI: London.
CEN. (2005b). EN 1993-1-2: Eurocode 3. Design of Steel Structures. In Part
1.2: General Rules - Structural fire design. BSI: London.
CEN. (2005c). EN 1994-1-2:2005, Eurocode 4: Design of Composite Steel
and Concrete Structures Part 1-2: General Rules Structural Fire
Design. In Part 1-2: General Rules Structural Fire Design. BSI:
London.
Cirpici, B. K. (2020). Design analysis of a steel industrial building with wide
openings exposed to fire. Challenge Journal of Structural Mechanics,
6(3), 99-109.
Cirpici, B. K., Orhan, S. N., & Kotan, T. (2020). Finite element study on
composite slab-beam systems under various fire exposures. Steel and
Composite Structures, 37(5), 589-603.
Cirpici, B. K., Wang, Y. C., & Rogers, B. (2016a). Assessment of the thermal
conductivity of intumescent coatings in fire. Fire Safety Journal, 81,
74-84. doi:http://dx.doi.org/10.1016/j.firesaf.2016.01.011
Cirpici, B. K., Wang, Y. C., Rogers, B. D., & Bourbigot, S. (2016b). A
theoretical model for quantifying expansion of intumescent coating
under different heating conditions. Polymer Engineering & Science,
56(7), 798-809. doi:https://doi.org/10.1002/pen.24308
ISBN: 978-625-6971-50-9
514
De Biagi, V., & Chiaia, B. (2013). Complexity and robustness of frame
structures. International Journal of Solids and Structures, 50(22),
3723-3741. doi:https://doi.org/10.1016/j.ijsolstr.2013.07.019
DoD, U. (2009). UFC 4-023-03: Design of buildings to resist progressive
collapse. US Department of Defense, Washington, DC, USA.
Fu, F. (2020). Fire induced progressive collapse potential assessment of steel
framed buildings using machine learning. Journal of Constructional
Steel Research, 166, 105918. doi:https://doi. org/10.1016/j.
jcsr.2019.105918
Gernay, T., & Khorasani, N. E. (2020). Recommendations for performance-
based fire design of composite steel buildings using computational
analysis. Journal of Constructional Steel Research, 166, 105906.
doi:https://doi.org/10.1016/j.jcsr.2019.105906
GSA. (2013). Alternate path analysis & design guidelines for progressive
collapse resistance. General Services Administration.
Hozjan, T., Planinc, I., Saje, M., & Srpcic, S. (2008). Buckling of restrained
steel columns due to fire conditions. Steel and Composite Structures,
8(2), 159-178. doi:https://doi.org/10.12989/scs.2008.8.2.159
Huang, Z.-F., & Tan, K.-H. (2003). Rankine approach for fire resistance of
axially-and-flexurally restrained steel columns. Journal of
Constructional Steel Research, 59(12), 1553-1571.
doi:https://doi.org/10.1016/S0143-974X(03)00103-2
Jiang, B., Li, G. Q., & Yam, M. C. H. (2020a). Simplified robustness
assessment of steel framed structures under fire-induced column
failure. Steel and Composite Structures, 35(2), 199-213.
doi:10.12989/scs.2020.35.2.199
Jiang, B., Wang, M., Shen, Y., & Li, Y. (2020b). Robustness assessment of
planar steel frames caused by failure of a side column under localized
fire. Structural Design of Tall and Special Buildings, 29(5).
doi:10.1002/tal.1711
Kiakojouri, F., De Biagi, V., Chiaia, B., & Sheidaii, M. R. (2020). Progressive
collapse of framed building structures: Current knowledge and future
prospects. Engineering Structures, 206, 110061. doi:https://doi.
org/10. 1016/j.engstruct.2019.110061
International Research in Engineering Sciences
515
Kiakojouri, F., Sheidaii, M., De Biagi, V., & Chiaia, B. (2020). Progressive
collapse assessment of steel moment-resisting frames using static-and
dynamic-incremental analyses. Journal of Performance of
Constructed Facilities, 34(3), 04020025.
Kokot, S., Anthoine, A., Negro, P., & Solomos, G. (2012). Static and dynamic
analysis of a reinforced concrete flat slab frame building for
progressive collapse. Engineering Structures, 40, 205-217.
doi:https://doi.org/10.1016/j.engstruct.2012.02.026
Li, G.-Q., Wang, P., & Wang, Y. (2010). Behaviour and design of restrained
steel column in fire, Part 1: Fire test. Journal of Constructional Steel
Research, 66(8), 1138-1147.
doi:https://doi.org/10.1016/j.jcsr.2010.03.017
Liu, C., Tan, K. H., & Fung, T. C. (2015). Component-based steel beam
column connections modelling for dynamic progressive collapse
analysis. Journal of Constructional Steel Research, 107, 24-36.
doi:https://doi.org/10.1016/j.jcsr.2015.01.001
Lou, G., Wang, C., Jiang, J., Jiang, Y., Wang, L., & Li, G.-Q. (2018). Fire
tests on full-scale steel portal frames against progressive collapse.
Journal of Constructional Steel Research, 145, 137-152.
doi:https://doi.org/10.1016/j.jcsr.2018.02.024
Newman, G. M., Robinson, J. T., & Bailey, C. G. (2006). Fire safe design: A
new approach to multi-storey steel-framed buildings. In. UK: The
Steel Construction Institute.
Ren, W. (2020). Behaviour of Steel Frames Exposed to Different Fire Spread
Scenarios. International Journal of Steel Structures, 20(2), 636-654.
doi:10.1007/s13296-020-00311-x
Shan, S., & Li, S. (2020). Fire-induced progressive collapse mechanisms of
steel frames with partial infill walls. Structures, 25, 347-359.
doi:https://doi.org/10.1016/j.istruc.2020.03.023
Sun, R., Burgess, I., & Huang, Z. (2013). Behaviour of Frame Columns in
Localised Fires. Journal of Structural Fire Engineering, 4(3), 175-
186. doi:10.1260/2040-2317.4.3.175
Sun, R., Huang, Z., & Burgess, I. W. (2012). Progressive collapse analysis of
steel structures under fire conditions. Engineering Structures, 34, 400-
413. doi:https://doi.org/10.1016/j.engstruct.2011.10.009
ISBN: 978-625-6971-50-9
516
Suwondo, R., Cunningham, L., Gillie, M., & Bailey, C. (2019). Progressive
collapse analysis of composite steel frames subject to fire following
earthquake. Fire Safety Journal, 103, 49-58.
doi:https://doi.org/10.1016/j.firesaf.2018.12.007
TBDY. (2018). Türkiye Bina Deprem Yönetmeliği. In. Afet ve Acil Durum
Yönetimi Başkanlığı, Resmi Gazete.
Tian, L.-m., Wei, J.-p., Hao, J.-p., & Wang, X.-t. (2017). Dynamic analysis
method for the progressive collapse of long-span spatial grid
structures. Steel and Composite Structures, 23(4), 435-444.
doi:https://doi.org/10.12989/scs.2017.23.4.435
Yao, Y., & Hu, X. X. (2015). Cooling behavior and residual strength of post-
fire concrete filled steel tubular columns. Journal of Constructional
Steel Research, 112, 282-292.
doi:https://doi.org/10.1016/j.jcsr.2015.05.020
Zhu, Y. F., Chen, C. H., Yao, Y., Keer, L. M., & Huang, Y. (2018). Dynamic
increase factor for progressive collapse analysis of semi-rigid steel
frames. Steel and Composite Structures, 28(2), 209-221.
doi:10.12989/scs.2018.28.2.209
ResearchGate has not been able to resolve any citations for this publication.
Chapter
Full-text available
Günümüzde Yapay Zeka (YZ) ve buna bağlı olan teknolojilerin kullanımı giderek artmaktadır. YZ teknolojisi bir problemin çözümünde hem algoritma yapısı hem de programlama yapısına katkı sağlayarak problemlerin çözümünde programlama dili fark etmeksizin ortak çözüm noktası haline gelmiştir. Algoritma bir olayın çözüme ulaşması için belirli bir amaca ulaşmada gerekli olan sıralı mantıksal işlem adımlarının tümüne denilmektedir. Programlama ise belirli bir problemi çözüme kavuşturulması için bir bilgisayar programlama dili kullanarak yazılmış deyimler dizisi olarak tanımlanmaktadır. YZ teknolojisi algoritma ve programlama ile birleşerek ilgili görevleri yapmak için insan zekasını taklit etme ve topladığı bilgiler ile algoritmasını sürekli olarak geliştirebilen makineler anlamına gelmektedir. YZ günümüzde teknoloji ile birleşince büyük adımlar doğrultusunda ilerlemiştir. YZ kullanım alanları içerisinde askeri ve polis uygulamaları, tarımsal uygulama, görüntü işleme uygulamaları, veri bilimi çalışmaları, doğal dil işleme uygulamaları, siber güvenlik uygulamaları ve ses tanıma ve işleme uygulamaları gibi çok fazla uygulama alanı mevcuttur. YZ bulanık mantık, makine öğrenmesi, uzman sistemler ve genetik algoritmalar olmak üzere konu ve içerik olarak birbirinden farklı olan alt sistemleri vardır. Bu farklılıklar uygulama alanlarına bağlı olarak ilerlemektedir. Örneğin YZ alt dallarının kullanım alanları içerisinde olan derin öğrenme ile imge işleme konusunda çok başarılı sonuçlar elde edilmektedir. Makine öğrenmesi ile veri bilimi çalışmaları gerçekleştirilebilmektedir. Bulanık mantık ile kontrol sistemleri üzerinde geliştirilen uygulamalarda karar verme çalışmaları gerçekleştirilmektedir. YZ problemlerinde ele alınan konu üzerine uygulanacak YZ yönteminin veya algoritmasının belirlenmesi büyük önem taşımaktadır (Rao, 1995; Klir, 1996; Allahverdi, 2002; Nabiyev, 2003; Elmas, 2007; McNeill, 2014, Zadeh, 1978). Gerçek dünya uygulamalarında matematiksel modeller her ne kadar iyi olursa olsun sistemin çalışma anında belirsizlikler ortaya çıkabilir. Bu tip belirsizlik durumlarında bulanık mantık metodu insan düşüncelerini dilsel değişkenler ve üyelik fonksiyonları yardımıyla işleme alır.
Article
Full-text available
In preparation for the Fourth Industrial Revolution (IR 4.0) in Malaysia, the government envisions a path to environmental sustainability and an improvement in air quality. Air quality measurements were initiated in different backgrounds including urban, suburban, industrial and rural to detect any significant changes in air quality parameters. Due to the dynamic nature of the weather, geographical location and anthropogenic sources, many uncertainties must be considered when dealing with air pollution data. In recent years, the Bayesian approach to fitting statistical models has gained more popularity due to its alternative modelling strategy that accounted for uncertainties for all air quality parameters. Therefore, this study aims to evaluate the performance of Bayesian Model Averaging (BMA) in predicting the next-day PM10 concentration in Peninsular Malaysia. A case study utilized seventeen years’ worth of air quality monitoring data from nine (9) monitoring stations located in Peninsular Malaysia, using eight air quality parameters, i.e., PM10, NO2, SO2, CO, O3, temperature, relative humidity and wind speed. The performances of the next-day PM10 prediction were calculated using five models’ performance evaluators, namely Coefficient of Determination (R2), Index of Agreement (IA), Kling-Gupta efficiency (KGE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The BMA models indicate that relative humidity, wind speed and PM10 contributed the most to the prediction model for the majority of stations with (R2 = 0.752 at Pasir Gudang monitoring station), (R2 = 0.749 at Larkin monitoring station), (R2 = 0.703 at Kota Bharu monitoring station), (R2 = 0.696 at Kangar monitoring station) and (R2 = 0.692 at Jerantut monitoring station), respectively. Furthermore, the BMA models demonstrated a good prediction model performance, with IA ranging from 0.84 to 0.91, R2 ranging from 0.64 to 0.75 and KGE ranging from 0.61 to 0.74 for all monitoring stations. According to the results of the investigation, BMA should be utilised in research and forecasting operations pertaining to environmental issues such as air pollution. From this study, BMA is recommended as one of the prediction tools for forecasting air pollution concentration, especially particulate matter level.
Article
Full-text available
With the industrialization of society, air pollution has become a critical environmental issue, leading to excessive morbidity and mortality from cardiovascular and respiratory diseases in humans. Accurate air pollution prediction has strongly promoted air quality control, which is important for human health. However, previous studies have failed to model spatiotemporal dependencies simultaneously with non-Euclidean distributions considering meteorological factors. In this study, a novel multigraph convolutional neural network for air pollution prediction is proposed. First, a spatial graph, an air pollution pattern graph and a meteorological pattern graph are constructed to model different relationships among non-Euclidean areas. Second, the graph convolutional network is applied to learn and incorporate the information of neighbour nodes of the corresponding graph, and then the graphs after convolution are fused. Finally, the fused matrix of GCNs is input into the gate recurrent units to capture temporal dependencies. Experimental results on the real dataset collected at air quality monitoring stations in Beijing validate the effectiveness of our proposed model.
Article
Full-text available
We updated the anthropogenic emissions inventory in NOAA’s operational Global Ensemble Forecast for Aerosols (GEFS-Aerosols) to improve the model’s prediction of aerosol optical depth (AOD). We used a methodology to quickly update the pivotal global anthropogenic sulfur dioxide (SO2) emissions using a speciated AOD bias-scaling method. The AOD bias-scaling method is based on the latest model predictions compared to NASA’s Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2). The model bias was subsequently applied to the CEDS 2019 SO2 emissions for adjustment. The monthly mean GEFS-Aerosols AOD predictions were evaluated against a suite of satellite observations (e.g., MISR, VIIRS, and MODIS), ground-based AERONET observations, and the International Cooperative for Aerosol Prediction (ICAP) ensemble results. The results show that transitioning from CEDS 2014 to CEDS 2019 emissions data led to a significant improvement in the operational GEFS-Aerosols model performance, and applying the bias-scaled SO2 emissions could further improve global AOD distributions. The biases of the simulated AODs against the observed AODs varied with observation type and seasons by a factor of 3~13 and 2~10, respectively. The global AOD distributions showed that the differences in the simulations against ICAP, MISR, VIIRS, and MODIS were the largest in March–May (MAM) and the smallest in December–February (DJF). When evaluating against the ground-truth AERONET data, the bias-scaling methods improved the global seasonal correlation (r), Index of Agreement (IOA), and mean biases, except for the MAM season, when the negative regional biases were exacerbated compared to the positive regional biases. The effect of bias-scaling had the most beneficial impact on model performance in the regions dominated by anthropogenic emissions, such as East Asia. However, it showed less improvement in other areas impacted by the greater relative transport of natural emissions sources, such as India. The accuracies of the reference observation or assimilation data for the adjusted inputs and the model physics for outputs, and the selection of regions with less seasonal emissions of natural aerosols determine the success of the bias-scaling methods. A companion study on emission scaling of anthropogenic absorbing aerosols needs further improved aerosol prediction.
Article
Full-text available
The ambient air quality in a city is heavily influenced by meteorological conditions. The city of Siliguri, known as the “Gateway of Northeast India”, is a major hotspot of air pollution in the Indian state of West Bengal. Yet almost no research has been done on the possible impacts of meteorological factors on criterion air pollutants in this rapidly growing urban area. From March 2018 to September 2022, the present study aimed to determine the correlations between meteorological factors, including daily mean temperature (℃), relative humidity (%), rainfall (mm), wind speed (m/s) with the concentration of criterion air pollutants (PM2.5, PM10, NO2, SO2, CO, O3, and NH3). For this research, the trend of all air pollutants over time was also investigated. The Spearman correlation approach was used to correlate the concentration of air pollutants with the effect of meteorological variables on these pollutants. Comparing the multiple linear regression (MLR) and non-linear regression (MLNR) models permitted to examine the potential influence of meteorological factors on concentrations of air pollutants. According to the trend analysis, the concentration of NH3 in the air of Siliguri is rising, while the concentration of other pollutants is declining. Most pollutants showed a negative correlation with meteorological variables; however, the seasons impacted on how they responded. The comparative regression research results showed that although the linear and non-linear models performed well in predicting particulate matter concentrations, they performed poorly in predicting gaseous contaminants. When considering seasonal fluctuations and meteorological parameters, the results of this research will definitely help to increase the accuracy of air pollution forecasting near future.
Article
Full-text available
In this study, metakaolin and red mud were used as solid precursors to synthesize geopolymer monoliths coupling hierarchical porosity with suitable compressive strength (4.5 MPa) enabling their use as bulk-type (not powders) sorbents. Then, the lead removal ability of these novel materials was investigated under various conditions. The present work is one of the first investigations evaluating the use of red mud-containing geo-polymer monoliths in the extraction of heavy metals from wastewater. Herein, metakaolin was employed as a reactive precursor to overcome the low reactivity of red mud and ensure the production of benign monolithic sorbents. The lead (II) sorption of the metakaolin/red mud sorbents was studied by varying the contact time, lead concentration, pH value and the volume of the solution. Results show that this unexplored approach, involving the use of a toxic waste to produce monolithic bodies able to treat lead-containing wastewaters, is not only feasible, but highly effective. The maximum lead removal capacity of the porous bodies reached 30.7 mg/g (at pH 5, C 0 = 600 ppm) being amongst the highest values reported to date for bulk-type geopolymers. The monoliths were also successfully regenerated post-sorption without significantly affecting their performance, and this enables their reuse in multiple sorption cycles provided that a suitable desorption agent is used. These are promising results that might contribute towards the industrial deployment of clay derived geopolymers in wastewater treatment systems, while encouraging a novel and sustainable recycling strategy for the red mud waste.
Article
Full-text available
This work reports a novel management strategy for a hazardous waste derived from the alumina industry, known as bauxite residue or red mud (RM). Herein, and for the first time, RM-containing porous structures were prepared by direct ink writing (DIW) and then used directly to extract methylene blue (MB) from synthetic wastewater. The lattices were prepared using a 50:50 wt.% blend between RM and metakaolin (MK). The incorporation of RM was evaluated by comparing with the MK-based printed structure. Despite the high residue amount, the lattices showed high compressive strength (10.7 MPa), high open porosity (62.40%), very high specific surface area (55 m²/g), and excellent stability throughout the tests (leaching, sorption, and thermal regeneration). In the batch adsorption tests, the impact of contact time, and dye concentration was evaluated. At the optimized conditions ([MB]0 = 50 mg/g; contact time: 48 h) the RM/MK-based structures showed a MB uptake of 19.96 mg/g, ranking them amongst the best performing bulk-type (not powders) geopolymer adsorbents. Furthermore, lattices were successfully regenerated and reused (up to ten cycles) without compromising their performance. Their excellent performance was also corroborated under continuous-flow column experiments. These promising results demonstrate the potential valorisation of a hazardous waste in wastewater treatment.
Article
This study reports the synthesis and characterization of a porous geopolymer systems based a naturally occurring Moroccan pyrophyllite clay. The contribution of pyrophyllite in geopolymerization reaction was very low, the geopolymer matrix formed was assured by alkaline activation of the metakaolin present in the sample. The pyrophyllite role is forming of disordered and heterogeneous geopolymer promote the pore creation. The structure and morphology of the raw natural clay, calcined meta-pyrophyllite phase and prepared geopolymer samples are characterized by several methods namely, X-ray Diffraction (XRD), 29Si and 27Al magic-angle-spinning nuclear magnetic resonance (MAS NMR) analysis, Fourier Transform Infra-Red (FTIR) spectroscopy, Differential Thermal Analysis (DTA) and Thermal Gravimetric Analysis (TGA), N2adsorption/desorption isotherm analysis by the BrunauerEmmett-Teller (BET) method and Scanning Electron Microscopy (SEM). The specific surface area (SBET) and pore volume (Vp) measured from products utilizing the calcined metapyrophyllite precursor (SBET = 86 m2/g and Vp = 0.08 cm3/g) have both increased markedly compared to those values determined using a raw pyrophyllite clay phase (SBET = 30 m2/g and Vp = 0.05 cm3/g). The methylene blue (MB) molecular adsorption experiment realized in water medium exhibited Pseudo Second Order (PSO) rate kinetics and adopted a Langmuir adsorption Journal. The thermodynamics data shows that the chemisorption is spontaneous, irreversible, and endothermic, with the quantity of MB molecules retained by the porous geopolymer specimen reaching 64.10 mg/g. Keywords: Natural Pyrophyllite, Porous geopolymer, alkaline activation solution, adsorption, methylene blue.
Article
Chongqing, a metropolitan with over 32 million residents in southwest China, has suffered from SO2 pollution since 1980s. The emission inventory is an important tool to evaluate the SO2 pollution and to design the effective emission reduction policies. The present work developed a scheme to update the obsolescent SO2 emission inventory in Chongqing obtained from Multi-resolution Emission Inventory for China in 2008 (MEIC2008). The updated emission inventory was estimated by integrating the a priori knowledge of the baseline emissions and the current observations based on Bayesian inference, in which the source-receptor sensitivities were calculated by the Decoupled Direct Method in Three Dimensions in the Community Multiscale Air Quality Modeling System (CMAQ DDM-3D). An analytical solution of the Bayesian theorem was derived based on the linear response assumption and applied to estimate the actual SO2 emissions. The updated emission inventory was comparable with the most recent MEIC emission inventory in 2016 and 2017, and was in line with the decline trend of SO2 emissions in Chongqing in the last decade. The adjustment of the emissions improved the accuracy in predicting SO2 concentrations with the developed method.