Early-Stage Structural Steel Estimation for Embodied Carbon Decision Making

Abstract

The structural system is commonly a large contributor to the embodied carbon in new construction projects. Therefore, when major decisions about structural systems are made late in the design process, the project team's ability to influence carbon impacts due to structural decisions is restricted. This can leave the embodied carbon impact of structural design as secondary to major design decisions made by architects before a structural engineer has been engaged. Similarly, early-stage carbon estimation can be limited by structural assumptions, including selection and estimation of structural systems and structural volumes. Exploring the feasibility of early-stage structural volume estimation, especially as it can be applied in analyzing embodied carbon (CO2e) studies, has the potential to bring the carbon impact of structural systems into the design discussion at earlier and earlier stages, when the costs of design changes are lower, and the potential impact is greater. By developing a methodology for estimating structural volumes based on initial architectural decisions such as building footprint, floor area, floor-to-floor heights, and preliminary structural selections, this research serves to bridge the gap between decision making and analysis to achieve effective and applicable early-stage embodied carbon analysis. This paper will examine the importance of embodied carbon estimation as it is shaped by early-stage structural estimations by a) presenting a brief review of the current methods, b) discussing how project teams can use early-stage estimating to ensure that embodied carbon is considered throughout the project life cycle, and c) outlining a sample methodology for early-stage structural estimation aimed towards volumetric takeoffs for embodied carbon calculations.

Full text

Sara Laudeman is an architectural researcher at cove.tool, Atlanta, Georgia. Twisha Raja is a computational designer at cove.tool,
Atlanta, Georgia. Nilesh Bansal is the Director of Sustainability at cove.tool, Atlanta, Georgia.
Early-Stage Structural Steel Estimation for
Embodied Carbon Decision Making
Sara M Laudeman Twisha Raja Nilesh Bansal
ABSTRACT
The structural system is commonly a large contributor to the embodied carbon in new construction projects. Therefore, when
major decisions about structural systems are made late in the design process, the project team’s ability to influence carbon
impacts due to structural decisions is restricted. This can leave the embodied carbon impact of structural design as
secondary to major design decisions made by architects before a structural engineer has been engaged. Similarly, early-
stage carbon estimation can be limited by structural assumptions, including selection and estimation of structural systems
and structural volumes. Exploring the feasibility of early-stage structural volume estimation, especially as it can be applied
in analyzing embodied carbon (CO2e) studies, has the potential to bring the carbon impact of structural systems into the
design discussion at earlier and earlier stages, when the costs of design changes are lower, and the potential impact is
greater. By developing a methodology for estimating structural volumes based on initial architectural decisions such as
building footprint, floor area, floor-to-floor heights, and preliminary structural selections, this research serves to bridge the
gap between decision making and analysis to achieve effective and applicable early-stage embodied carbon analysis. This
paper will examine the importance of embodied carbon estimation as it is shaped by early-stage structural estimations by a)
presenting a brief review of the current methods, b) discussing how project teams can use early-stage estimating to ensure
that embodied carbon is considered throughout the project life cycle, and c) outlining a sample methodology for early-stage
structural estimation aimed towards volumetric takeoffs for embodied carbon calculations.
INTRODUCTION
The built environment is responsible for approximately 30% of global greenhouse gas emissions according to the
Carbon Leadership Forum (CLF) (CLF 2020). Up to 8% of that is the result of construction manufacturing, making embodied
carbon a critical action item for building decarbonization. Despite this, embodied carbon has often been overlooked as the
industry works to reduce operational carbon. The built environment’s global warming potential (GWP) is a critical factor in
controlling global climate change. Additionally, as new regulations such as the Toronto Green Standard, ISO standards for
life cycle assessments (e.g., ISO 14040), the International Green Building Code, and the United Kingdom’s Whole life
carbon assessment for the built environment come into play, developers and designers will continue to be incentivized to
strive for net or near zero carbon buildings.
To reach these goals and comply with the continually developing standards, design teams must consider embodied
carbon as a major aspect of the built environment’s carbon footprint. In 2019, the World Green Building Council (World
GBC) report on embodied carbon suggested that “more than half” of the new construction emissions from 2020 to 2050
would be a result of embodied carbon (World GBC 2019). It is the responsibility of designers, developers, and building end-
users everywhere to advocate for more sustainable, environmentally friendly, and carbon-conscious buildings. There are
several ways to begin that conversation, but the most versatile tool in the design process is a life-cycle assessment (LCA).
LCAs can help design teams across the board understand the environmental impacts of design and procedure decisions.
While LCAs have become more prevalent, estimation methods for embodied carbon vary in terms of their accuracy,

required inputs, and required knowledge or training levels. Pomponi and Moncaster (2018) noted in that embodied carbon
has the potential to “become a ‘second wave’ of performance gap” in LCAs and building design considerations. Similarly,
Kaethner and Burridge (2012) noted that they did not anticipate LCA taking on a primary role in common practice for
engineers and contractors. They do however note that an LCA is important for understanding which decisions can be the
most impactful in reducing the embodied carbon footprint of a building.
According to the Institution of Structural Engineers (ISE), a building’s structure can account for more than half of its
total embodied carbon footprint (ISE). Kaethner and Burridge (2012) also found that superstructure alone accounted for more
than 40% of the total embodied carbon footprint of their case study buildings, and when including substructure, the total
structural impact was 51%-62%. These staggering values illustrate the need for early-stage awareness and discussion of
embodied carbon when making structural system selections.
BACKGROUND
Discourse on embodied carbon has long been characterized by gaps in knowledge and inconsistencies in results.
Current industry practices often involve consultants and users with varying degrees of embodied carbon and estimation
literacy, introducing discrepancies into the process that can have impacts on the results of such projects (Pomponi 2018b,
Pomponi et al. 2018c, De Wolf et al. 2017). ISO standards identify three levels of LCA. The ideal estimation method would
allow the same tool to be used at each stage, beginning with a shoebox model and culminating in precise material takeoffs
from construction documents. However, the manual material takeoff process is tedious and can produce highly variable
results that are not fully accounted for in data review (Pomponi 2018b). De Wolf et al. (2017) highlight that industry
standards should lead the drive to standardize methods of embodied carbon measurement. However, in the same paper, the
authors also highlighted the need for tools that begin to bridge knowledge and skill gaps within design teams (De Wolf et al.
2017, Hattan et al. 2015).
As one of the highest impact considerations, the embodied carbon cost of a building’s structural system should be at the
forefront of discussion when developing tools to close gaps and refine industry standards for embodied-carbon-focused
LCAs. The authors here recognize the need for a consistent method to estimate the volume of structural systems at early
stages of the project, prior to the inclusion of a structural engineer in the traditional design workflow, that can be developed
alongside the design process for continually refined results. Providing clear and accessible methods, as well as developing a
reliable way to improve day-one awareness of the impact design decisions have on embodied carbon, has the potential to
keep the impacts of structural system selection, building design, and materiality at the forefront of the design process even
before structural engineers and other sustainability consultants have been brought onto the team.
Current industry practices often involve consultants and users with varying degrees of embodied carbon and estimation
literacy, introducing gaps into the process that can have impacts on the results of such projects (Pomponi 2018b, Pomponi et
al. 2018c, De Wolf et al. 2017). Of course, the search for an early-stage LCA method for embodied carbon is not a new task.
Budig et al. proposed a regression model for an early-stage LCA tool in 2020 to facilitate considerations around the GWP
prior to floor plan decisions being made. (Budig et al, 2020) Budig’s team used a predictive model to correlate generic inputs
to LCA results. Unlike their contribution, this paper proposes a framework that does require some specific knowledge of
inputs; however, the framework proposed herein does not require more specialized knowledge than is typical for a design
team in early phases of project development. In 2021, Mehdi et al. explored the applications of integrating embodied carbon
considerations into value engineering (VE) pursuits; however, this involves the challenge and cost of design revisions (Robati
et al. 2021). The method presented herein is a single aspect of a larger-scale LCA process. However, accounting for
superstructure is often challenging to finalize as designers consider scope, current knowledge, and applicability of
comparisons. The industry faces a series of problems: lack of data, lack of experience, and lack of education in embodied
carbon estimation. This paper aims to address the second and third of these issues by proposing a method for automating
superstructural quantity estimates at early project stages, when the ability to effect changes is high.
It should be noted that the methods proposed here are unique to steel – however, similar approaches to mass timber,
masonry, concrete, and other systems may be used. The conceptual ability to develop structural system estimates at early
stages of design is the critical component of this work.
METHODS

The first step in decision making is data collection. By presenting data early and often, design teams can encourage
project stakeholders to consider the impacts of embodied carbon at all project phases. Project teams need access to actionable
data to convince project stakeholders of impacts. By presenting comparative data early in the project lifecycle, structural
components can be selected to control embodied carbon contribution without disrupting architectural design decisions that
have already been made. The MacLeamy curve clearly indicates the benefits in making these decisions early in the process
when the cost in both time and money is relatively low compared to the potential impacts (MacLeamy 2004).
The framework presented herein should be the first step towards a series of comparative estimates that can be used to
guide design decisions as a dataset instead of as independent values. As previously stated, the target is an accurate and useful
measure of embodied carbon, even in the early stages of design. The results of a structural systems estimate should be helpful
for all project team members and allow the carbon impacts of structural systems to be weighed at early stages. Further, the
model should be able to be updated throughout the design process as more precise information becomes available to the
project team.
To this end, the required inputs must be consistent across system estimates. In exploring methods for estimating
structural systems, the researchers established the following minimum required variables: structural grid spacing, number of
intermediate beams, floor area(s), floor-to-floor heights, building use, and structural system selection
From the above, any functional method should be able to provide approximate material takeoffs for the structural
framing and then apply a metric for baseline embodied carbon or specific Environmental Product Declarations (EPDs) to
narrow the scope of the estimate. Although this paper focuses on the impact of structural estimation, the authors expect to
continue this thread of research in the future to account for other building components and systems, including comparisons
between systems and optimization for structural embodied carbon.
This paper will describe an example of this methodology using structural steel, discuss its limitations, and provide
commentary on future work. Steel frames are a common and carbon-intensive system but provide exceptional versatility for
design, and they are often the most feasible solution for a given building form. Therefore, steel frames are a reasonable first
step in developing and validating this method. For the purposes of this paper, the target was aligning the results of this early-
stage method to the AISC rules of thumb for structural steel mass per unit square area, with the understanding that more
detailed inputs will yield more precise outputs. In “Rules of Thumb for Steel Design,” the authors put forward the following
rule of thumb for material intensity, Wt, of steel: (Ionnides 2012)
(1)
(2)
The proposed estimation method can be outlined as follows:
1. Calculate the live and dead load and apply the Allowable Stress Design (ASD) factor.
2. Compute the total length of members via floor plate geometry.
3. Select the “worst case” scenario for each member
4. Estimate the economical size member needed to carry the worst-case load using ASD methods, and then apply that
to each member on the floor of the same type.
5. Report summation values for beams, girders, and columns.
Load Categorizations
Given the building use type, standard assumptions can be made based on existing knowledge and from the International
Building Code. For instance, a typical office will have approximately 35% of its gross area dedicated to circulation and
similar spaces. An additional 9% will go towards mechanical spaces (Bell 2016)
By allocating percentages of the total building area to different use types (and thus different live loads), we can then
find an area-weighted average for the total live load, which we can apply across the entire building area for a reasonable
estimate of load conditions. We can make a similar estimate for dead load values based on the type of construction expected
– light (stud framing), medium (CLT, heavy stud framing, etc.), or heavy (masonry or block).
Geometry Interpretation

Using geometry analysis coupled with the input grid spacing, it is possible to count the numbers of beams, columns, and
girders on each floor based on either an area input (for a shoebox model) or a geometry upload. By projecting gridlines over
the floor plate, intersections between gridlines and between gridlines and perimeters can be counted to estimate the number of
columns. Line segments between columns correspond to girders, and each bay will contain the input number of
intermediate/secondary beams. This method also provides the total length of members (the sum length of all beams, for
example).
Edge-Case Selection
A simplifying assumption is made that beam sizes, girder sizes, and column sizes are internally consistent. Coupled
with using a whole-building, area-weighted average for live loads, this means that some members will be oversized, and
others will be undersized. It is important to note that this approach is only as accurate as its inputs and is not intended to
replace the involvement of a structural engineer, who should provide confirmation and/or overrides for the volumetric
takeoffs when they are brought onto the project team. In efforts to build accurate early-stage estimates, the authors have
identified the following edge cases which should be accounted for.
Beams. Beams should be sized as typical infill members, bearing the typical load conditions as outlined above. Interior
beams span the minimum of the bay spacing dimensions. Assume that all members spanning the minimum of the bay spacing
dimensions are sized similarly.
Girders. Bearing girders span the maximum of the bay spacing dimensions, carrying one half of all beams bearing on
them. These shall be broken into interior bearing girders, which carry n beams on either side, where n is the number of infill
beams in a typical bay, and exterior bearing girders, which carry n beams on only one side. The bearing loads will be one-half
of the load due to each beam per typical tributary calculations.
Columns. Columns shall be estimated in vertical runs of three floors, starting from the topmost level. Interior and
exterior columns shall be estimated separately to limit the margin of accuracy.
Economical Sizing
The method begins by estimating the beam depth required to support the live and dead loads by computing the section
modulus, Sx, required to support the moment generated by the total factored load, ωt. Recalling that beams span the shorter
bay dimension, we can write the maximum moment as:
(3)
Where W is the total load on the beam, expressed in terms of the factored load per square area, ωt, and the minimum and
maximum grid dimensions. Given that the required section modulus is written as Sreq = Mmax / fb, and the selected beam must
have Sx > Sreq to be sufficient, the appropriate section can be selected from standard steel sections, choosing the most
economical section available. This should include an iterative check to confirm that the beam is still suitable under the added
moment due to its own weight, as well as confirming that deflection and serviceability checks are met.
For girders, standard forumulas for the bending and deflection of members due to the bearing loads should be used. As
illustrated in the previous section, interior and exterior girders shall be estimated using two different load conditions (Fig.1b).
Girder loads and selections will additionally vary with n, the number of infill beams per bay. For a bay with one, two, or
three infil beams, the maximum moment can be written respectively as:
(a) (b) (c) (4)
For columns, the load conditions are more complex. Columns shall carry loads from the distributed live and dead loads,
the self weight of beams, and the self weight of girders. An interior column carries one full girder (one half on either side in
the longer span) and one full infill girder (one half on either side in the shorter span). The same column supports four quarter-
bays worth of live and dead load and beam self weight. These loads can be expressed as:

(5)
(6)
(7)
Where ωt is the total factored floor load, ωg,i is the linear weight of the selected interior girder, ωb is the linear weight of
the selected beam, n is the number of intermediate beams per bay, and x and y are the grid spacings in the respective
directions. The sum load on the worst-case column is then Pcolumn = ∑P.
For exterior columns, the edge case consists of one full bearing girder (one half on either side in the longer span), half
of an infill girder, and two quarter-bays worth of live and dead load and beam self weight. These loads can be expressed as:
(8)
(9)
(10)
where ωg,e is the linear weight of the selected exterior girder.
From this point, and assuming that the column is purely axially loaded, the method for calculating available
compressive strength from AISC 360 can be applied. Columns should be estimated for each group of three floors, starting
with the topmost level and moving downwards, accounting for vertical load transfer through consecutive columns.
Reporting
Given these edge-cases, the output from these calculations can be converted from sizing estimates, which are not of
direct use in the early-stage analysis process, to the more generic volume or mass estimates. Given that the total length of
members is known, the total mass takeoff for steel can be arithmetically computed.
DEMONSTRATION
Consider a six-story office building of steel frame construction with square bays measuring 9.144 m (30 ft) per side
with two secondary beams per bay, and all structural steel is A992 steel with Fb = 206842.719 kN/m2 (30 ksi). Assume that
9% of the floor area is dedicated to mechanical spaces, and of the remaining useable space, 35% is circulation and non-
programmable space (ASHRAE Handbook 2020) The remainder is of the office use type. The floor-to-floor height is 4.57 m
(15 ft), and the building area is 6020 m2 (64799 ft2) divided equally between all floors, and the construction is of a standard
weight at 1.676 kN/m2 (35 psf). Per IBC 2018, the minimum allowable distributed live load for circulation spaces on the
ground floor is 4.788 kN/m2 (100 psf), and for floors above, circulation spaces will match the spaces served - office spaces at
2.394 kN/m2 (50 psf). We can hold the load for mechanical spaces constant at 5.985 kN/m2 (125 psf). Using the area
percentages assumed above, the area-weighted average the whole-building typical live load is 2.597 kN/m2 (54.240 psf).
The worst-case beam will support the sum of the live and dead loads, times the ASD safety factor of 1.67, across the
tributary area. Given Equation 3, Mmax = 227.320 kN-m (167.648 kip-ft). To support that load condition, the selected member
will require:
(72.10 in.3) (11)
This value for Sreq is then compared to the EDI standards for steel shapes provided by AISC Shapes Database to select
the economical beam which has Ss > Sreq + Sadd, (where Sadd is the additional section modulus due to the self weight of the

beam) and meets serviceability checks for deflection per AISC 360. The section selected is a W530x66, which weighs 66
kg/m (44.35 lb/ft), has a section modulus of 1340.00 cm3 (81.77 in.3), and is 526.0 mm (20.1 in.) deep. Each beam then
accounts for a total load of 208.764 kN (46.932 kip) including floor loads and self-weight.
Via a similar method, the most economical girder can be selected by using the appropriate equations for bending
moments and deflections due to point loads. Interior girders will carry two half-beams on either side for a total of two loads
at 208.764 kN (46.932 kip). Exterior girders will carry a single half-beam at 104.382 kN (23.466 kip). Using (b) from
Equation 4, it can be shown that the interior girder must support Sx = 3216.55 cm3 (196.29 in.3). After accounting for self-
weight and deflection checks, the selected member is a W690X125 with a section modulus of 3490.0 cm3 (212.97 in3).
Similarly, the exterior girders need only support Sx = 1678.39 cm3 (102.42 in.3). The most economical section that supports
its own self-weight and the constraint on allowable total deflection is a W610X101 with a section modulus of 2520.0 cm3
(153.78 in3).
The column calculations shall be broken down in two parts. First, find the total load per column due to the structural
load (Pcolumn as outlined above). For interior columns, that shall be the sum of Equations 5-7. For exterior columns, the sum of
Equations 8-10. These expressions are written as:
(12)
(13)
Given that columns will be selected in groups of three floors, in a six-story building the 4th, 5th, and 6th floor columns
will be sized together and the 1st, 2nd, and 3rd floor columns will be sized together. Each column on the 4th floor carries the
total load of the three fstructural levels above. Applying Equations 12 and 13 respectively, it is shown that Pcolumn,i = 1880.11
kN (39.26 kip) and that Pcolumn,e = 950.43 kN (19.85 kip). Using methods outlined in AISC 360, the economical sections with
an available load greater than Pcolumn are a W200X15 section for the interior and a W150X29.8 section for the exterior. Using
the same approach for the lower three floors, ensuring that the building weight of the upper three floors is accounted for, a
W310X86 can be selected for the interior and a W360X44 for the exterior columns.
Given the dimensions of the bays and the plan areas above, projecting the square bay onto the area suggests that there
are 2139.7 m (7020.0 ft) of beams and infill girders, 438.9 m (1440.0 ft) each of interior and exterior girders, 164.6 m (540.0
ft) of interior columns, and 383.9 m (1259.5 ft) of exterior columns. Given the selections above, we see a total structural
frame utilizing 263833.5 kg (581653.3 lb) of steel, or 43.71 kg/m2 (8.95 psf) when adjusted for the gross square footage, as
compared to the expected value of 43.94 kg/m2 (9.00 psf) from Equation 1.
DISCUSSION
As shown, a sample building can be calibrated to well within a 15% confidence interval around of the rule-of-thumb for
structural framing. While this is an initial exploration for geometry-based embodied carbon estimation, this level of accuracy
is a useful indicator that can be used to guide future calibration exercises. Several key assumptions should be addressed and
documented.
This method does not right-size individual members. Instead, each floor is estimated at the worst-case load scenario,
and all members on that floor are assumed to be identical. This will typically lead to conservatively high estimates of
structural quantities. It is also assumed that all columns are under purely axial load conditions, which is a simplifying
assumption that can be refined in future work. However, in the case of this example, it is likely that columns have been
slightly undersized.
For this example, steel beams and girders are assumed to be controlled by bending and deflection – shear capacity is
not checked based on the assumption that if the beam passes the serviceability check for deflection as well as the bending
assessment, then it will be sufficient in shear.
Another consideration is requirements, such as that found in the International Green Construction Code (IgCC),

which require that structural system material quantities should be validated by a design professional. In the broader context of
LCA and continuous process validation, it is the authors’ intent that the methods proposed in this paper will be a single step
in the broader LCA, which would be made available as a web-based tool. To comply with the verification requirements, users
would be able to override the early-stage estimates when final structural takeoffs are available.
CONCLUSION
To progress towards a net-zero embodied carbon future, the industry needs a coordinated, feasible, and usable solution
for embodied carbon estimation. The method outlined in this paper is an early-stage step in a broader LCA workflow. With
proper implementation, the proposed method has the potential to help bridge the gap between schematic design, design
development, and final construction. Encouraging design teams, project stakeholders, and developers to engage in
conversations about carbon estimation earlier in the process can put carbon on the table before critical system decisions are
made.
Early-stage carbon estimation needs only to be reasonably accurate; however, it does need to be accurate and consistent
relative to other system comparisons. High-level overviews are critical design tools for limiting embodied carbon, and an
ideal tool would allow for continuous updates throughout the process. This would create a dynamic model that improves in
accuracy and precision throughout the design process. A model in schematic design can be compared to another in schematic
design, and the same project should be able to be tracked from schematic design to construction. Even with the limited scope
of the minimum inputs outlined previously, the results derived from different systems with the same inputs should be
comparable. Having a rough outline of structural quantities allows for analysis in terms of both carbon and cost. Bringing
embodied carbon to the conversation at early stages helps build awareness of the potential impacts of design decisions, even
before engineers and sustainability consultants are brought on board. Embodied carbon estimation is a daunting task that few
projects undertake. By implementing a method that relies on values that are well understood at early project stages, design
teams can quickly conceptualize and discuss the impacts of structural systems on a project’s level of carbon as well as its
design aesthetic.
It is the authors’ conclusion that methods similar to the one presented herein can be used to make initial decisions
that will then be refined throughout the traditional LCA process. However, having a day one estimate for embodied carbon,
especially when comparing the potential impacts of renovation versus new construction, or possible cost-benefit analyses for
different structural systems, cannot feasibly be left until after selections of structural systems, building form, or materiality
have been completed. This framework for structural steel is the first step in a larger ecosystem of early-stage structural
estimates for other systems, such as concrete, mass timber, and masrony.
The authors intend to continue to pursue this train of thought for future research towards developing an embodied
carbon assessment tool and to encourage all designers to consider the impacts of their decisions. Future considerations
directed by this work will include comparative analysis of this and similar methods for major structural system types,
calibration experiments in comparison to both rules of thumb such as that provided by AISC and referenced previously, as
well as comparison to available datasets of structural quantities from constructed buildings. Through these investigations, it is
the authors’ intent to develop a robust, available, and accessible method for early-stage embodied carbon estimation within a
broader LCA framework.
ACKNOWLEDGMENTS
The authors are grateful to their colleagues at cove.tool for collaboration and support on this project.
NOMENCLATURE
ω = distributed linear load
W = total load, sum of distributed loads across a span
x = grid span in x direction
y = grid span in y direction
L = span, general
P = point load, total axial load

fb = allowable bending stress capacity
n = number of intermediate/secondary beams
Mmax = maximum developed bending moment.
Sreq = section modulus required to resist bending moment, Mmax
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