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Articles

Understanding structural health monitoring data to support decision-making processes and service life management of mass timber buildings. A preliminary study on use of data scaffolding

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Pages 42-59 | Received 11 Jan 2022, Accepted 16 Dec 2022, Published online: 15 Feb 2023

References

  • Anderson R, Dawson E, Muszynski L. 2021. International mass timber report. The Forest Business Network.
  • Arda Buyuktaskin HA, Yatagan MS, Erol Soyoz G, Tanacan L, Dilmaghani M. 2019. Experimental investigation of the durability of load bearing timber-glass composites under the effects of accelerated aging. J Green Build. 14(2):45–59.
  • Baas EJ, Riggio M, Barbosa AR. 2021. A methodological approach for structural health monitoring of mass-timber buildings under construction. Constr Build Mater. 268:121153. DOI:10.1016/j.conbuildmat.2020.121153.
  • Baas EJ, Riggio M, Schmidt E, Mugabo I, Barbosa AR. 2019. Living lab at Peavy Hall: structural health monitoring of mass timber buildings. 5th International Conference on Structural Health Assessment of Timber Structures; Sep 25–28; Guimarães, Portugal.
  • Björngrim N, Hagman O, Wang XA. 2016. Moisture content monitoring of a timber footbridge. BioResources. 11:3904–3913.
  • Bowen GM, Roth WM. 2005. Data and graph interpretation practices among preservice science teachers. J Res Sci Teach. 42(10):1063–1088.
  • Brand-Gruwel S, Kammerer Y, Van Meeuwen L, Van Gog T. 2017. Source evaluation of domain experts and novices during web search. J Comput Assist Learn. 33(3):234–251.
  • Canham M, Hegarty M. 2010. Effects of knowledge and display design on comprehension of complex graphics. Learn Instr. 20(2):155–166.
  • Carpenter PA, Shah P. 1998. A model of the perceptual and conceptual processes in graph comprehension. J Exp Psychol Appl. 4(2):75–100.
  • Carswell CM. 1992. Choosing specifiers: an evaluation of the basic tasks model of graphical perception. Hum Factors. 34(5):535–554.
  • Chang SJ, Wi S, Kang SG, Kim S. 2020. Moisture risk assessment of cross-laminated timber walls: perspectives on climate conditions and water vapor resistance performance of building materials. Build Environ. 168:106502.
  • Chi MT, Feltovich PJ, Glaser R. 1981. Categorization and representation of physics problems by experts and novices. Cogn Sci. 5(2):121–152.
  • Ciribini A, Pasini D, Tagliabue L C, Manfren M, Daniotti B, Rinaldi S, De Angelis E. 2017. Tracking users’ behaviors through real-time information in BIMs: workflow for interconnection in the Brescia Smart Campus demonstrator. Proc Eng. 180(2017):1484–1494.
  • Cohen J. 1988. Statistical power analysis for the behavioral sciences. New York: Routledge.
  • Curcio FR. 1987. Comprehension of mathematical relationships expressed in graphs. J Res Math Educ. 18(5):382–393.
  • De Amicis R, Riggio M, Shahbaz Badr A, Fick J, Sanchez C A, Prather E A. 2019. Cross-reality environments in smart buildings to advance STEM cyberlearning. Int J Interact Des Manuf (IJIDeM). 13(1):331–348.
  • Dietsch P, Franke S, Franke B, Gamper A, Winter S. 2015. Methods to determine wood moisture content and their applicability in monitoring concepts. J Civil Struct Health Monit. 5(2):115–127.
  • Elias M, Bezerianos A. 2011. Exploration views: understanding dashboard creation and customization for visualization novices. IFIP Conference on Human-Computer Interaction; September. Berlin: Springer. p. 274–291.
  • Fast P, Gafner B, Jackson R, Li J. 2016. Case study: An 18 storey tall mass timber hybrid student residence at the university of British Columbia, Vancouver. In Proceeding of the World Conference on Timber Engineering (WCTE); August 22–25; Vienna, Austria.
  • Febretti A, Garzotto F. 2009. Usability, playability, and long-term engagement in computer games. CHI'09 Extended Abstracts on Human Factors in Computing Systems; Apr 4–9; Boston, MA, USA. p. 4063–4068.
  • Franke B, Schiere M, Franke S. 2018. Stress developments in large timber cross sections in relation to geometry and encountered climate. In World Conference on Timber Engineering; Aug 20–23; Seoul, Republic of Korea.
  • Giordano PF, Prendergast LJ, Limongelli MP. 2020. A framework for assessing the value of information for health monitoring of scoured bridges. J Civil Struct Health Monit. 10(3):485–496. DOI:10.1007/s13349-020-00398-0.
  • Glaser SD, Tolman A. 2008. Sense of sensing: from data to informed decisions for the built environment. J Infrastruct Syst. 14(1):4–14.
  • Glass SV, Zelinka SL. 2010. Chapter 4 - moisture relations and physical properties of wood. In: Ross RJ, editor. Wood handbook - wood as an engineering material. Vol. 1. GTR-190. Madison, WI: U.S. Dept. of Agriculture, Forest Service, Forest Products Laboratory. p. 1–20.
  • Glisic B, Yarnold M, Moon F, Aktan A. 2014. Advanced visualization and accessibility to heterogenous monitoring data. Comput-Aided Civ Infrastruct Eng. 29, 382–398.
  • Granello G, Palermo A. 2020. Monitoring dynamic properties of a Pres-Lam structure: Trimble Navigation Office. J Perform Constr Facil. 34(1):04019087.
  • Grossman PUA. 1976. Requirements for a model that exhibits mechano-sorptive behavior. Wood Sci Technol. 10:163–168.
  • Hall GD, Flock SK. 2008. In situ moisture testing of building products as a predictor of actual conditions. Proceedings of the BEST1 Conference; June 10–12; Minneapolis, MN, United States.
  • Herrmann MR, Brumby DP, Oreszczyn T, Gilbert XM. 2018. Does data visualization affect users’ understanding of electricity consumption? Build Res Inf. 46(3):238–250.
  • Johannessen T V, Fuglseth A M. 2014. The effectiveness of data presentation formats: an exploratory study. Nokobit 2014, norsk konferanse for organisasjoners bruk av IT, Fredrikstad, Norway.
  • Kim NJ, Belland BR, Walker AE. 2018. Effectiveness of computer-based scaffolding in the context of problem-based learning for STEM education: Bayesian meta-analysis. Educ Psychol Rev. 30(2):397–429.
  • Kohnen AM, Mertens GE. 2019. “I'm always kind of double-checking”: exploring the information-seeking identities of expert generalists. Read Res Q. 54(3):279–297.
  • Kordziel S, Pei S, Glass SV, Zelinka S, Tabares-Velasco PC. 2019. Structure moisture monitoring of an 8-story mass timber building in the pacific northwest. J Archit Eng. 25(4):04019019.
  • Lanata F. 2019. Long-term monitoring of the highest 100% timber building in France. In: Branco JM, Poletti E, Sousa HS, editors. Structural health assessment of timber structures (SHATiS). ISISE, Institute of Science and Innovation for Bio-Sustainability (IB-S), Department of Civil Engineering, University of Minho; Guimarães, Portugal; p. 327–336.
  • Leyder C, Chatzi E, Frangi A. 2015. Structural health monitoring of an innovative timber building. In: Second International Conference on Performance-Based and Lifecycle Structural Engineering; Brisbane, Australia. p. 1383–1392.
  • Longman RP, Xu Y, Sun Q, Turkan Y, Riggio M. 2023. Digital twin for monitoring In-service performance of post-tensioned self-centering cross-laminated timber shear walls. J Comput Civ Eng. 37(2). DOI:10.1061/(ASCE)CP.1943-5487.0001050.
  • Maichle U. 1994. Cognitive processes in understanding line graphs. In: Schnotz W, Kulhavy RW, editors. Advances in psychology, Vol. 108. North-Holland; p. 207–226.
  • McCalley LT, Midden CJ. 2002. Energy conservation through product-integrated feedback: the roles of goal-setting and social orientation. J Econ Psychol. 23(5):589–603.
  • Morris HW, Zhu M, Wang M. 2011. The long-term instrumentation of the NMIT arts building–EXPAN shear walls. N Z Timber Design J. 20(1):13–24.
  • Moxley JH, Ericsson KA, Charness N, Krampe RT. 2012. The role of intuition and deliberative thinking in experts’ superior tactical decision-making. Cognition. 124(1):72–78.
  • Mustapha G, Khondoker K, Higgins J. 2018. Structural performance monitoring technology and data visualization tools and techniques – featured case study: UBC tallwood house. 1st International Conference on New Horizons in Green Civil Engineering (NHICE-01); April 25–27; Victoria, BC, Canada.
  • Muszyński L, Lagaňa R, Davids W, Shaler SM. 2005. Comments on the experimental methodology for quantitative determination of the hygro-mechanical properties of wood. Holzforschung. 59(2):232–239.
  • Napolitano R, Blyth A, Glisic B. 2018. Virtual environments for visualizing structural health monitoring sensor networks, data, and metadata. Sensors. 18(1):243.
  • Niklewski J, Isaksson T, Frühwald Hansson E, Thelandersson S. 2018. Moisture conditions of rain-exposed glue-laminated timber members: the effect of different detailing. Wood Mat Sci Eng. 13(3):129–140.
  • North C, Shneiderman B. 2000. Snap-together visualization: can users construct and operate coordinated visualizations? Int J Hum Comput Stud. 53(5):715–739.
  • Patterson RE, Blaha LM, Grinstein GG, Liggett KK, Kaveney DE, Sheldon KC, Havig PR, Moore JA. 2014. A human cognition framework for information visualization. Comput Graph. 42:42–58.
  • Pinker S. 1990. A theory of graph comprehension. In: Freedle R, editor. Artificial intelligence and the future of testing. Hillsdale, NJ: Lawrence Erlbaum Associates; p. 73–126.
  • Reingold EM, Charness N, Pomplun M, Stampe DM. 2001. Visual span in expert chess players: evidence from eye movements. Psychol Sci. 12(1):48–55.
  • Riggio M, Anthony RW, Augelli F, Kasal B, Lechner T, Muller W, Tannert T. 2014. In situ assessment of structural timber using non-destructive techniques. Mater Struct. 47(5):749–766.
  • Riggio M, Dilmaghani M. 2020. Structural health monitoring of timber buildings: a literature survey. Build Res Inf. 48(8):817–837.
  • Roth W-M, McGinn MK. 1998. Inscriptions: A social practice approach to “representations”. Rev Educ Res. 68:35–59.
  • Schmidt E, Riggio M. 2019. Monitoring moisture performance of cross-laminated timber building elements during construction. Buildings. 9(6):144.
  • Schmidt EL, Riggio M, Barbosa AR, Mugabo I. 2019. Environmental response of a CLT floor panel: lessons for moisture management and monitoring of mass timber buildings. Build Environ. 148:609–622.
  • Serrano E, Enquist B, Vessby J. 2010. Vertical relative displacements in a medium-rise CLT-building. In: Cruz PJS, editor. ICSA 2010-1st International Conference on Structures & Architecture. Guimaraes, Portugal: Taylor & Francis; p. 113–113.
  • Shah P, Freedman EG. 2011. Bar and line graph comprehension: an interaction of top-down and bottom-up processes. Topics Cogn Sci. 3(3):560–578.
  • Shmulsky R, Jones PD. 2011. Wood and water. In: Forest products and wood science: an introduction, 6th ed. Chichester, West Sussex, UK: Wiley Blackwell. p. 141–174. doi:10.1002/9780470960035.ch.
  • Shah P, Hoeffner J. 2002. Review of graph comprehension research: implications for instruction. Educ Psychol Rev. 14:47–69.
  • Shneiderman B, Hochheiser H. 2001. Universal usability as a stimulus to advanced interface design. Behav Inf Technol. 20(5):367–376.
  • Sinha A, Udele KE, Cappellazzi J, Morrell JJ. 2020. A method to characterize biological degradation of mass timber connections. Wood Fiber Sci. 52(4):419–430.
  • Straub D, Chatzi E, Bismut E, Courage WMQ, Dohler M, Nielsel MH, Kohler J, Lombaert G, Omenzetter P, Pozzi M, et al. 2017. Value of information: A roadmap to quantifying the benefit of structural health monitoring. 12th International Conference on Structural Safety & Reliability (ICOSSAR 2017); Vienna, Austria.
  • Straub D, Faber M. 2005. Risk based inspection planning for structural systems. Struct Saf. 27(4):335–355. doi:10.1016/j.strusafe.2005.04.001.
  • Toratti T. 1992. Creep of timber beams in a variable environment [doctoral dissertation]. Helsinki University of Technology.
  • Vazquez-Ingelmo A, Garcia-Penalvo FJ, Theron R. 2019. Information dashboards and tailoring capabilities-a systematic literature review. IEEE Access. 7:109673–109688.
  • Wang J, Karsh E, Finch G, Chen M. 2016. Field measurement of vertical movement and roof moisture performance of the wood innovation and design centre. World Conference on Timber Engineering (WCTE); August 22–25; Vienna, Austria.
  • Wang M, Wu B, Chen NS, Spector JM. 2013. Connecting problem-solving and knowledge-construction processes in a visualization-based learning environment. Comput Educ. 68:293–306.
  • Yi JS. 2012. Implications of individual differences on evaluating information visualization techniques. Int J Hum Comput Stud. 45(6):619–637.
  • Yuan B, Wang M, Kushniruk AW, Peng J. 2017. Deep learning towards expertise development in a visualization-based learning environment. J Educ Technol Soc. 20(4):233–246.
  • Yun TJ, Jeong HY, Kinnaird P, Choi S, Kang N, Abowd GD. 2010. Domestic energy displays: an empirical investigation. ACEEE Summer Study on Energy Efficiency in Buildings; Pacific Grove, California, USA; vol. 7; p. 348–360.
  • Zabel RA, Morrell JJ. 1992. Wood microbiology: decay and its prevention. San Diego, CA: Academic Press.
  • Ziemkiewicz C, Ottley A, Crouser RJ, Chauncey K, Su SL, Chang R. 2012. Understanding visualization by understanding individual users. IEEE Comput Graph Appl. 32(6):88–94.
  • Zikmund-Fisher BJ, Fagerlin A, Ubel PA. 2005. What's time got to do with it? Inattention to duration in interpretation of survival graphs. Risk Anal Int J. 25(3):589–595.
  • Zonta D, Glisic B, Adriaenssens S. 2014. Value of information: impact of monitoring on decision-making. Struct Control Health Monit. 21(7):1043–1056. DOI:10.1002/stc.1631.

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