A data structure and physical test database are implemented for cold-formed steel connections. The database is publicly available on GitHub in a format that is both human-readable and machine-readable. The data structure is designed to accommodate connection tests with varying test variables including fastener type, quantity of fasteners in a specimen, and the number and type of plies. The database design is made compatible with open-source software tooling to support future data-driven model development. Strategies are discussed for encouraging general user adoption of open-source databases and software including education, community guidelines, and easy-to-use interfaces

Chen, Baihan

Moen, Cristopher D.

JScholarship

Proceedings of the Cold-Formed Steel Research Consortium Colloquium17-19 October, 2022 (cfsrc.org)An open-source cold-formed steel connection test database to support future data modelsCristopher D. Moen1, Baihan Chen2AbstractA data structure and physical test database are implemented for cold-formed steel connections. The database is publiclyavailable on GitHub in a format that is both human-readable and machine-readable. The data structure is designed toaccommodate connection tests with varying test variables including fastener type, quantity of fasteners in a specimen, andthe number and type of plies. The database design is made compatible with open-source software tooling to support futuredata-driven model development. Strategies are discussed for encouraging general user adoption of open-source databasesand software including education, community guidelines, and easy-to-use interfaces.1. IntroductionData on its own is data. Human-accessible data that canbe downloaded, imported, and explored by anyone leads tonew ideas and observations, especially if the data is alsomachine-readable [1], i.e., it has a data structure that can beaccessed with software tooling. Strategies for making exper-imental data human- and machine-readable are explored inthis paper with the design and construction of a cold-formedsteel connection test database.Driving a screw fastener through two plies of steel happensin a few seconds on a construction site, however creating astructural model that simulates these fasteners working to-gether in a building currently takes much longer. As oneconnected ply moves relative to the other, the local connec-tion stiffness changes as the screw tilts and tears throughthe plies. Fastener load-deformation response is complexand mostly available only as physical test data. There arepowerful open-source tools and protocols for organizing andmodeling with data that are continuously supported by a vastglobal network of volunteers. Why not try to use them?For example, convenience functions for converting data fromtext files or Excel to a data structure, referred to herein asa struct, are available in most computer languages [2]. Astruct can have the look and feel of a spreadsheet, for ex-ample, a DataFrame [3], but instead of a single value per field,they can hold any data format, from a scalar to array to animage.1Cristopher D. Moen, RunToSolve, LLC cris.moen@runtosolve.com2Baihan Chen, RunToSolve, LLC baihan.chen@runtosolve.comThink about a struct in the context of a steel-to-steel screw-fastened connection test, where the steel ultimate stress fuis used to predict connection strength. The connection re-sponse trends with the steel ultimate stress fu, howeverstrength prediction may correlate better to some other un-known model where the full stress-strain curves for the pliesare utilized. The whole stress-strain curve from a test canbe stored in a struct, i.e. test.stress and test.strain.Increased data fidelity, access, and organization enabled bydata structures accommodates deeper data exploration.And defining data models with a data structure is especiallyconvenient these days. For example, Interpolations.jl pro-vides fast, continuous multivariate interpolation of discretedata sets. This means that for a set of connection tests, withply thicknesses t1 and t2 and fy and fastener size and load Pand relative ply displacement ∆, that a model can be createdwhere Pmodel,∆model = f(t1, t2, fy, fu). Instead of writingdown empirical equations that attempt to match the data, themodel f , informed by a database, directly approximates con-nection load-deformation response, strength, and stiffness.2. DatabaseThe interactivity of this particular open-source cold-formedsteel connection test database is defined by GitHub. Anyonecan copy the database (Clone), post a question or bug (Is-sue), and submit data (Pull Request). The database existsas a JSON file which is a data interchange format that we alluse every day on websites and mobile applications.When a data set is submitted, it is reviewed and mappedto a data structure. The struct is then converted to JSONformat, and written to the database JSON file.For this database, all the information about a test specimenis carried in the data structure Specimen which is made up ofSource, Fastener, Ply, Test.struct Specimensource::Sourcefastener::Fastenerply::Plytest::Testendstruct Sourceauthors::Array{String}date::Datetitle::Stringbibtex::Stringunits::Array{String}endstruct Fastenertype::Array{String}size::Array{Float64}endstruct Plytype::Array{String}thickness::Array{Float64}elastic_modulus::Array::Array{Float64}yield_stress::Array{Float64}ultimate_stress::Array{Float64}endstruct Testname::Stringloading::Stringforce::Array{Float64}displacement::Array{Float64}endThe data structure is designed with ease of modification andexpansion in mind. The struct fields are written as arrays tohold multiple values, and struct fields can be added or re-moved. This means the database can hold a connection testwhere there was just one fastener or a test with 4 fasteners,or a test with 2 plies or 3 plies, or a data set with 1 author or3 authors, or a test with gypsum ply and a steel ply, or a dataset with some screw-fastened tests and some puddle weldtests.Here are some examples of ways one might interact withthe Specimen object. To access all of the researchers asso-ciated with a specimen, use specimen.source.authors orfor just the first author specimen.source.authors[1]. Writespecimen.test.force to obtain the actuator force vector orspecimen.ply.thickness[1] to access the first measuredply thickness. A data set of many specimens can be repre-sented as an array of Specimen objects.This database has a unique web URL and it can be cited likeany other publication [4]. To use the database, clone it, im-port the JSON file into a data structure with your favorite pro-gramming language, and then query the data structure. Forexample, find all connection tests in the database where thefirst ply thickness is less than or equal to 2.0 mm using theJulia language [5]:using JSON3json_string = read("fastener_connection_data.json",String)database = JSON3.read(json_string)my_data = [database.test[i].ply.thickness[1]<=2.0u"mm"for i in eachindex(database)]3. DiscussionA database with a data structure is the jumping off pointto data visualization, data-driven models, machine learning,and AI techniques.Documentation that defines the data structure is essential forencouraging users. In this database, the structs are writtenwith descriptive names, which helps ease the documentationburden. For example, specimen.fastener.type leads youto the data you are looking for with words.The selection of a JSON file for this database was madebecause it is both human-readable and machine-readable,however large JSON datasets become cumbersome. Usingan SQL database provides faster access to data sources.Storing large data sets in a compressed binary format is alsocommon [6].The data submission and review stage is important becauseit influences data model quality downstream [7]. Data reviewand mapping data to the data structure is time-consumingand requires trained volunteers. Providing motivation to pub-lish test data directly in machine-readable format should bea high priority for any industry or institution.The successful use of data requires a community of users[8]. It is important that both universities and industry invest inopen-source database and software development and train-ing if they want to make use of data-driven models [9].Web-based user interfaces to a database are useful to en-gage users [10].2Units of measurement for each data type should be includedin the data structure. Useful packages are available for au-tomating unit conversion [11].Open-source community rules and democratic workflowsthat encourage trust between maintainers and users are im-portant [12].It is challenging to balance data structure fidelity and usabil-ity. There are examples of complex data models where everyparameter is documented [13], however the user is poten-tially burdened by this complexity. Thought and discussionand trial usage and iteration and testing are required to pro-duce a useful open-source database.4. ConclusionAn open-source cold-formed steel connection test databasewas designed and implemented. The database has a flexi-ble data structure from which future data-informed predictionmodels can be built.References[1] Victor, Bret. “What can a technologist do about cli-mate change?” (2015), [Online]. Available: http : / /worrydream.com/ClimateChange/.[2] The Julia language. “Types.” (2022), [Online]. Avail-able: https://docs.julialang.org/en/v1/manual/types/.[3] Jacob Quinn. “DataFrames.jl.” (2022), [Online]. Avail-able: https://github.com/JuliaData/DataFrames.jl.[4] C. D. Moen and B. Chen, FastenerConnectionData,2022. [Online]. Available: https : / / github . com /runtosolve/FastenerConnectionData.git.[5] J. Bezanson, A. Edelman, S. Karpinski, and V. B. Shah,“Julia: A fresh approach to numerical computing,” SIAMreview, vol. 59, no. 1, pp. 65–98, 2017. [Online]. Avail-able: https://doi.org/10.1137/141000671.[6] The HDF Group. “Hierarchical data format version5.” (2000-2010), [Online]. Available: http : / / www .hdfgroup.org/HDF5.[7] The National Science and Technology Council, “De-sirable characteristics of data repositories for federallyfunded research,” 2022. DOI: https://doi.org/10.5479/10088/113528.[8] NumFocus, NumFocus, 2022. [Online]. Available:https://numfocus.org/.[9] The Linux Foundation. “Participating in Open SourceCommunities.” (2022), [Online]. Available: https://www . linuxfoundation . org / resources / open -source-guides/participating-in-open-source-communities.[10] E. Dong, H. Du, and L. Gardner, “An interactive web-based dashboard to track covid-19 in real time,” TheLancet infectious diseases, vol. 20, no. 5, pp. 533–534,2020.[11] CalTech Painter Lab, Unitful.jl, 2022. [Online]. Avail-able: https://github.com/PainterQubits/Unitful.jl.[12] V. DEVENYI, D. DI GIACOMO, C. DUSSUTOUR, B.KUDZMANAITE, and M. SHAIKH. “Guidelines for Sus-tainable Open Source Communities in the Public Sec-tor.” (2021), [Online]. Available: https://joinup.ec.europa.eu/sites/default/files/inline-files/2021%20Updated%20Guidelines%20for%20creating%20sustainable%20OS%20communities_1.pdf.[13] S. J. Chalk, “Scidata: A data model and ontology forsemantic representation of scientific data,” Journal ofcheminformatics, vol. 8, no. 1, pp. 1–24, 2016.3

An open-source cold-formed steel connection test database to support future data models

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An open-source cold-formed steel connection test database to support future data models

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