Summary for CIFE Seed Proposals for Academic Year 2018-19

Proposal number: 2018-04

Proposal title: A Multidisciplinary Method to Optimize and Automate the Structural and

Connection Design of Large-Scale Steel Structures

Principal

investigator(s)1 and

department(s):

Martin Fischer, Civil and Environmental Engineering

Eduardo Miranda, Civil and Environmental Engineering

Ram Rajagopal, Civil and Environmental Engineering

Research staff: Filippo Ranalli, PhD candidate, Civil and Environmental Engineering

Total funds requested: $ 71,631

Project URL for

continuation proposals

N.A.

Project objectives

addressed by proposal2

Usable, Buildable, Sustainable

Expected time horizon < 2 years

Type of innovation Breakthrough

Abstract

(up to 150 words)

The problem: In modern structural design practice, the design

space is explored and iterated-through manually to find solutions

that are compliant with strength and drift requirements. However,

without the aid of computational design tools, manual exploration

can be insufficient, and the design sub-optimal. Furthermore,

when searching for the optimal design, conventional optimization

algorithms directly associate cost to weight, leaving out

fabrication and erection. Finally, ensuring the design meets

constructability requirements can be challenging if done

manually.

The proposed solution: A robust, scalable and generalized

algorithm that can act as a design aid by simultaneously

optimizing the member sizing, detailing and topology of

conventional steel building structures for cost, while guaranteeing

all strength/sizing/constructability requirements per code.

The proposed research approach: The further development of

computationally-efficient optimization methods, based on an

existing topology and sizing optimization framework. Validation

using industry case studies on conventional building structures.

1 Engineering Problem

Three of the major opportunities in the modern structural design practice are: optimization andautomation methods could be deployed more frequently if there were a generalized framework;project-specific cost drivers could be used to directly guide the design towards optimality; andconstructability checks could be dramatically enhanced with the aid of computational methods inthe design phase. This proposal suggests the development of a robust, scalable and generalizedalgorithm that can act as a design aid by simultaneously optimizing the member sizing, detailingand topology of conventional steel building structures for cost, while enhancing constructabilityand guaranteeing strength/sizing requirements per code. Due to the modularity and generality ofthe proposed framework, the optimization could be extended to any building structure, and tailoredat the granular level to the particular design features of each class of structures.

Computational tools have reached the maturity to enable designers to run thousands of simulationsto exhaustively explore the (very large) design space for a given structure. Very often in the currentpractice designers iterate through the design with manual iterations, and because such iterations arerelatively time-intensive, only a small fraction of the design space can be explored. The resultingdesign is thus unlikely to be the optimal for the specified objective function, and the more so thebigger the structure and the larger the number of load cases to design for.

Moreover, when structural optimization is deployed in an academic or industry setting, cost is al-ways directly and exclusively associated with weight. However, according to recent AISC data,on average weight accounts for only 28% of the total installed cost, while fabrication and erectioncombined account for the remaining 72% of the total cost. Ignoring such factors in the objective isa gross misrepresentation of the cost drivers (Paulson, 1976). Furthermore, the detailing of a struc-ture is in most cases performed on a schedule basis, rather than on a load demand-driven basis. Thepurpose of this simplification is to minimize the number of connection details. As a consequence,the majority of the connections is over-designed, as they are not sized for the actual load demandsat the joint, which could in fact be much lower.

Finally, with the exception of design-build firms, the structural design is performed independentlyfrom the construction planning and execution. Such lack of information exchange between designand construction often leads to delays (Hamzah et al., 2011). Without a streamlined process be-tween the design and the construction, the chance of delays that could otherwise have been avoidedis high. Such delays lead to cost deviations from the budget and potential litigation.

The three major design phases of the built environment subjectable to optimization are shownin Fig. 1. Significant efforts have been placed in the development of BIM tools for scenarioexploration (Grasshopper, Dynamo) in the architectural design phase, allowing for facade, shapeand energy optimizations of buildings. Artificial intelligence methods (Alice) have also recentlybeen developed to guide the later project phases, allowing to explore the cost-time trade-offs ofthousands of construction schedules. Precisely, the stage between the initial BIM and the finalconstruction planning phases is comprised of the topology, sizing and connection detailing designsof the structural steel. Presently, this stage is missing a scalable and generalized framework for anexhaustive exploration of design alternatives which attempts to minimize the total installed cost of

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the structure, while enhancing buildability and meeting all the code requirements for strength andstiffness.

Figure 1: Optimization areas in the built environment

Developing structural optimization algorithms to fill such a gap would achieve a twofold benefit,on one side allowing to navigate the design space to find cheaper alternatives, and on the otherallowing to digitalize and streamline the design process across the three main phases in Fig .1.

2 Intuition

The intuitions behind the proposed solutions to the three main engineering problems described inthe previous section are summarized as follows:

• Given the high dimensionality and non-convexity of the design space, it is statistically veryunlikely that the first compliant solution obtained with manual iteration is optimal. An al-gorithm that performs efficient exploration of the design space will always yield a superiorsolution. Such an algorithm can serve as a design aid, providing a compliant and locally(or sometimes globally) optimal starting design, while saving significant amounts of designtime.

• Because the total installed cost is made up of location-specific material, erection, fabrica-tion and miscellaneous cost, all such costs must be modeled to accurately drive the designtowards a minimum-cost solution. While cost data is difficult to come by, it is possible tocreate empirical cost models that estimate erection and fabrication costs as a function of themember geometry, connection layout, design specifications, and project location. If coming

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by fabricator cost data is a possibility, then powerful inference models can be trained to esti-mate the cost of future connections based on their features. While cost data is hard to obtain,it is still possible to estimate the relative cost of different detailing solutions. Over-designedconnections can significantly drive up costs, and this can be easily avoided if the detailing isautomated for each individual connection.

• To help avoid delays in the construction phase due to incompatible design choices, it is pos-sible to account for constructability in the design phase itself. The first step is to model rule-based buildability constraints to enforce in the design phase, such as splice lengths, availablesection sizes, and continuity assignments through nodes that feature multiple members. Thesecond step is to search for sub-assemblies in the structure that are potentially problematic,and iterate on the design to fix them.

3 Theoretical and Practical Points of Departure

3.1 Theoretical POD

Computational methods to optimize topology and member sizing for truss and frame structureswere originally developed for aerospace applications over twenty years ago (Agte et al., 2009),and progressively adapted to structures in the civil engineering domain.

Classic structural optimizations explored in the literature can be divided into the major categoriesof topology, sizing, connectivity and shape. The topology design space is either a discrete groundstructure (Dorn, 1964), or a mass continuum. Sizing optimization is only implemented in discrete-geometry optimization problems (i.e. linear beam-column or truss elements), and its design spacecan either be characterized by continuous or discrete cross-section sizes. Connectivity optimiza-tion iterates over the 3D degree of fixity of the nodal connections. Finally, shape optimization isdescribed by the variation of discrete-geometry member coordinates. Each of these design spacescan be explored simultaneously with the others or independently. Commonly in the literature, aswell as in the proposed methodology, topology and sizing are optimized sequentially. Such designchoice is due to the fact that changes in the topology lead to a redistribution of the forces, load pathand structural response, which require member sizes to be adjusted iteratively. The methodologypresented hereby also iterates through connectivity, strictly with the purpose of meeting stabilityand buildability requirements.

Continuous topology methods, such as those explored by Liang et al. (2000), Stromberg et al.(2012), Baldock et al. (2005), attempt to minimize the weight of a structure that is representedas a continuum. This class of algorithms is however generally impractical in design, due to thedifficulty in converting continuous finite element topology into discrete members with real sizesand connections, and incorporating cost and constructability considerations.

Conversely, discrete topology optimization methods begin with an array of nodes that are inter-connected with a dense mesh of linear members (ground structure). Since the optimal membertopology is found from within the original ground structure, an advantage of the method is thatthe initial ground structure geometry can be defined so as to ensure feasibility of fabrication and

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erection of all possible solutions before the optimization process begins. The most common al-gorithms used for simultaneous topology and sizing optimization are genetic algorithms, as seenin Tang et al. (2005), Rajan (1995), and Deb and Gulati (2001). The greatest limitation of thisfamily of algorithms is the scalability, as the design space increases very rapidly with the numberof elements, complexity of the costing model, constraints, different frame types, connectivity andconstructability considerations.

Achtziger and Stolpe (2007) propose a formulation of discrete topology and sizing optimization,featuring a global minimum weight solution. While genetic algorithms and heuristic methods con-verge to local optima, this study frames the problem as a convex optimization, thus guaranteeingthe global optimum. While this method only applies to trusses and does not consider cost or con-structability, it poses an upper bound on optimal convergence, as the other approaches describedhereby only capable of achieving local minima.

The Ground Structure Based Topology Optimization (GRAND) method (Zegard and Paulino,2014) uses a discrete-truss approximation of the full finite-element problem to dramatically im-prove computational efficiency compared to similar ground structure methods. However, theGRAND method does not account for bending forces in the members and, therefore, is only ap-plicable to truss structures. This method aims to minimize material weight rather than the totalinstalled cost of the structure. Asadpoure et al. (2015) takes a leap forward in discrete topologyand sizing optimization, formulating a smooth and differentiable cost objective for truss structures.The advantages of such method is that gradient descent can be utilized to optimize the objective,which leads to very fast convergence. However, this approach does not account for actual build-ability, and uses a very basic cost function that is dependent on the member weight.

Hassett and Putkey (2002) assemble a collection of cost drivers for the most common AISCmoment-connections. Coupled with a detailing engine, such information could be used to de-termine the cheapest configuration for a given connection.

The shortcomings of the structural optimization methods described above can be summarized as:lack of a comprehensive cost model that accounts for cost drivers other than solely weight, lackof scalability of sizing, topology and connection optimization algorithms to real buildings, lack ofproject-specific buildability constraints, and lack of simultaneous strength and stiffness compliancechecks per code.

3.2 Practical POD

As a first attempt to tackle some the shortcomings mentioned above, I have developed an inte-grated topology and sizing optimization framework (Ranalli et al., 2018), hereby referred to asMDO (Multidisciplinary Design Optimization) that has yielded overwhelmingly positive resultson industry test cases. The overall architecture is shown below in Fig. 2.

The MDO method is a discrete nested step-wise sizing and topology optimization algorithm, whichdirectly models designer-input buildability rules, strength and stiffness compliance with the AISCcode, and a simple detailing/costing function that heuristically estimates material, fabrication and

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Figure 2: MDO Architecture

Figure 3: MDO Case Study Results

erection costs. It can run on braced frames, moment frames, or a hybrid of the two. It deploysa fully automated Python API for SAP2000, numerical optimization techniques to significantlyspeed up the run time, and can tackle the optimization of large structures ( 10,000 elements). Itssizing and topology algorithms utilize a re-visitation of the virtual work methods developed by(Chan and Grierson, 1993) and (Charney, 1993). The sizing achieves a minimum weight solution,while the topology component achieves a minimum cost solution, leveraging the information fromthe detailing and costing functions. This tool was utilized in a retrospective case study on a largesteel cladding structure supporting concrete rockwork panels subjected to dead, live, wind andseismic load combinations. The resulting structure, which can be seen in Fig. 3, was roughly 40%lighter and had cheaper connections than the original structure designed by the firm.

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In order to generalize the MDO beyond cladding, it is necessary to improve and build upon its cur-rent components. First of all, the wide variety of AISC code requirements for commercial buildingstructures must be accounted for. Moreover, the sizing algorithm must be upgraded to accountfor inter-story drifts and serviceability criteria, and enhanced to include cost considerations. Thedetailing and costing engines, as well as the constructability checks, must be upgraded to a higherlevel of detail.

4 Research Methods and Work Plan

The proposed research will be performed in two phases: Method Development and Validation. Thegoals and work tasks for each phase of the research are discussed below.

4.1 Method Development

I am proposing to extend the MDO method above to include the functionality, algorithmic robust-ness and scalability required to function at the industry level. This research will generalize thealgorithm to work on a broader range of steel structures, from warehouses to residential buildings.It will furthermore offer the option to perform sizing, topology and connection optimizations indi-vidually, or any combination thereof.

Research Tasks

1. Develop a detailing engine that will instantiate individual connection details based on thenode geometry and load demands. An accurate detailing algorithm will on one hand allowfor a more accurate cost model, and on the other a demand-based connection design.

2. Develop constructability checks both on the local node/member scale, and on the globalsub-assembly scale. These checks will be guided by a designer-specified rule-set.

3. Develop a more granular connection cost model, based on fabricator/erector guidelines orcollected datasets on which to perform statistical learning.

4. Enhance the sizing algorithm to effectively minimize cost for strength and stiffness compli-ance, rather than only weight.

5. Allow for connection optimization in post-processing, to ensure the appropriate distributionof moment fixity throughout the structure.

6. Automatically generate the topology design space (ground structure) from the BIM or anal-ysis models, using the designer-input parameters. This will facilitate streamlining from theBIM to the structural model.

7. Enhance the topology algorithm through the introduction of a probabilistic framework, inorder to search for potentially better local minima by introducing parallel exploration.

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4.2 Validation

The goal of the validation phase is to qualify the improved MDO as a valid design aid tool, and toquantify the improvements possible with the proposed method over traditional design methods. Iaim to demonstrate that the MDO framework can generate a compliant design that is more optimalthan one designed by hand, and less prone to delays in the scheduling. Optimality is measured asoverall installed cost, constraint compliance, and constructability.

Research Tasks

1. Numerical Example Validation: I will test the improved connection detailing and costingfunctions, sizing and topology optimizers on a full scale steel building toy problem. I willshow how varying the buildability, connection and cost parameters yields significantly dif-ferent design options.

2. Case Study Validation: I will run the algorithm on retrospective or current projects from theindustry, in which a previously-completed design is re-designed with the improved MDOframework, and the cost and buildability performance are compared on a fair basis. Theideal structure is modular and similar across different designs, such as a car park, a stadium,or an airport terminal (Fig. 4).

Figure 4: Ideal candidate test cases for MDO

5 Expected Results

5.1 Findings and Contribution

I expect to show significant differences between structures designed by hand, and those obtainedwith the use of the MDO design automation tool. Specifically, a lower cost, less design time, fewer

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unique connections, and a more risk-averse design in terms of constructability.

Topology: A probabilistic topology algorithm will allow to explore many scenarios in parallel. Theintroduction of the stochastic element will be a step closer to the global optimum than a determin-istic approach.Sizing: A cost-driven sizing algorithm will find the cheapest solution that guarantees strength andstiffness compliance with the AISC requirements. There currently is no available software that canperform such task, and I expect the algorithm to outperform the original design in retrospectivecase studies.Connection cost and detailing: A more comprehensive and powerful detailing engine will be devel-oped to model the connection costs to a higher degree of accuracy, helping to avoid over-designedconnections.Constructability: Modeling constructability constraints will help avoid unfeasible designs, whichmay be hard to spot without the aid of a computational tool.

5.2 Impact on Practice

I anticipate the proposed research to have a twofold impact on the profession, by showcasing theadvantages of design automation, and by further advancing a tool that can provide an excellentstarting design. Such a tool would on one hand save significant design time, and on the other gen-erate a solution that is much more likely to be optimal in terms of cost, risk-averse constructability,and safety. The mainstream misconception that this research directly addresses is that each struc-ture is unique, and the structural aspects cannot be automated. This research aims to show thatwhile each resulting design is in fact unique, the variable space is shared among all structureswithin the same category, and the optimal solution can only be found through exploration. I be-lieve that the adoption of the suggested MDO framework as a design-aid would contribute to abetter, safer and faster design of our built environment, as well as significant reductions in costsand materials.

Buildability: With the optimization framework, it is possible to generate the design that is optimalin the specified criteria, helping designers improve the economy of their structures, while reducingthe chance of design errors and unforeseeable construction delays.Usability/Operability: A design that is compliant with the code, or goes beyond to meet the specificperformance-based designer requirements is more risk-averse during natural disasters.Sustainability: A well-designed building that minimizes construction delays would speed up thedesign cycle, having a positive impact on sustainability.

6 Industry Involvement

The industry partner(s) involved would provide either new projects to design or retrospective casestudies on which to run the MDO. Their direct involvement would be required in specifying thedesign space and a comprehensive buildability rule-set to customize to their projects of inter-est. Whenever possible, the partner(s) would supply cost or scheduling data that could be farmedwith state-of-the art AI algorithms, and subsequently leveraged to track the optimal solution more

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closely. Finally, they would supply, or collaborate in the development of the scheduling informa-tion necessary to develop a simple baseline schedule simulator.

7 Research Milestones

Milestone Start DateDevelop advanced detailing and connection cost engines September 2018Extend sizing functionality to directly minimize cost December 2018Benchmark the topology and sizing algorithms on conventional building structures February 2019Develop a stochastic topology optimization framework June 2019Develop case studies and submit one or two journal papers November 2019

8 Risks

Access to Project Data for Case Study Validation: whether the project stakeholders agree to shareconfidential information on the system based on confidence that this information will remain se-cure.Access to Capital Data from Mills, Fabricators and Erectors: whether mills, fabricators and erec-tors agree to share confidential capital data on the system based on confidence that this informationwill remain secure.Computational Requirement: exploring a design space of higher complexity will required addi-tional computational challenges.

9 Next Steps

Following a CIFE seed round, I expect multiple avenues available for continuation of the researchand possible funding sources. Possible funding sources include: American Institute of Steel Con-struction, and the National Science Foundation. The very next step would be to model salientscheduling aspects early in the design phase. Once the optimization of the design and schedul-ing of steel buildings reaches a higher level of maturity, the framework could be extended bothin depth and breadth. In the former case, non-linear analyses, composite floors, foundations andmore advanced seismic braces (such as BRBF’s) could be included in the optimization. In the lat-ter scenario, the MDO process could be applied to concrete structures. Finally, more effort couldbe placed in seamlessly streamlining the design process with its preceding phase (architecturalplanning), and its subsequent phase (construction planning).

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References

Achtziger, W. and M. Stolpe, 2007: Truss topology optimization with discrete design variables—guaranteed global optimality and benchmark examples. Structural and Multidisciplinary Opti-mization, 34(1), 1–20.

Agte, J., O. de Weck, J. Sobieszczanski-Sobieski, P. Arendsen, A. Morris, and M. Spieck, 2009:Mdo: assessment and direction for advancement—an opinion of one international group. Struc-tural and Multidisciplinary Optimization, 40(1), 17.

Asadpoure, A., J. K. Guest, and L. Valdevit, 2015: Incorporating fabrication cost into topologyoptimization of discrete structures and lattices. Structural and Multidisciplinary Optimization,51(2), 385–396.

Baldock, R., K. Shea, and D. Eley, 2005: Evolving Optimized Braced Steel Frameworks for TallBuildings Using Modified Pattern Search.

Chan, C.-M. and D. E. Grierson, 1993: An efficient resizing technique for the design of tall steelbuildings subject to multiple drift constraints. The Structural Design of Tall and Special Build-ings, 2(1), 17–32.

Charney, F. A., 1993: Economy of steel frame buildings through identification of structural behav-ior. In Proceedings of the Spring 1993 AISC Steel Construction Conference, pp. 21–1.

Deb, K. and S. Gulati, 2001: Design of truss-structures for minimum weight using genetic algo-rithms. Finite Elements in Analysis and Design, 37(5), 447 – 465, genetic algorithms and finiteelements in engineering.

Dorn, W. S., 1964: Automatic design of optimal structures. Journal de mecanique, 3, 25–52.

Hamzah, N., M. Khoiry, I. Arshad, N. M. Tawil, and A. C. Ani, 2011: Cause of constructiondelay-theoretical framework. Procedia Engineering, 20, 490–495.

Hassett, P. M. and J. J. Putkey, 2002: Steel tips.

Liang, Q. Q., Y. M. Xie, and G. P. Steven, 2000: Optimal topology design of bracing systems formultistory steel frames. Journal of Structural Engineering, 126(7), 823–829.

Paulson, B. C., 1976: Designing to reduce construction costs. Journal of the Construction Division,102.

Rajan, S. D., 1995: Sizing, shape, and topology design optimization of trusses using genetic algo-rithm. Journal of Structural Engineering, 121(10), 1480–1487.

Ranalli, F., F. Flager, and M. Fischer, 2018: A ground structure method to minimize the totalinstalled cost of steel frame structures. International Journal of Civil, Environmental, Structural,Construction and Architectural Engineering, 12(2), 147 – 155.

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Stromberg, L. L., A. Beghini, W. F. Baker, and G. H. Paulino, 2012: Topology optimization forbraced frames: Combining continuum and beam/column elements. Engineering Structures, 37,106 – 124.

Tang, W., L. Tong, and Y. Gu, 2005: Improved genetic algorithm for design optimization of trussstructures with sizing, shape and topology variables. International Journal for Numerical Meth-ods in Engineering, 62(13), 1737–1762.

Zegard, T. and G. H. Paulino, 2014: Grand — ground structure based topology optimization forarbitrary 2d domains using matlab. Structural and Multidisciplinary Optimization, 50(5), 861–882.

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Sponsor: CIFE

Submission Type: New

Budget Preparation Date: 10/1/2018

Budget Start Date: 10/1/2018

Project Name: Title: TBD

Department: Civil Engineering

Principal Investigator: Martin Fischer

Administrator: Blanca Rebuelta

Period 1 All Periods

From 10/1/2018 10/1/2018

To 9/30/2019 9/30/2019

Personnel Salaries

Graduate Students

Research Assistant Academic 50.0% 30,348            30,348            

Summer 50.0% 10,116            10,116            

Total Graduate Student Salaries 40,464            40,464            

Total Salaries 40,464            40,464            

Benefits

Graduate 2,023              2,023              

Total Benefits 2,023              2,023              

Total Salaries and Benefits 42,487            42,487            

Other Direct Costs

Tuition

Research Assistant Academic 50.0% 20,358            20,358            

Summer 50.0% 6,786              6,786              

Total Tuition 27,144            27,144            

Domestic Travel 2,000              2,000              

Total Other Direct Costs 29,144            29,144            

Total Direct Costs 71,631            71,631            

Total Amount Requested 71,631            71,631            

Rates Used in Budget Calculations

Benefit Rates                                                                   

Graduate:         FY 1   05.00%; FY 2   05.00%;      FY 3+   05.00%;

Indirect Cost Rates