Collaborative Research: Innovation in Sustainable Mass Timber Building Systems

合作研究：可持续大型木结构建筑系统的创新

• 批准号: 1762526
• 负责人: Eric Landis
• 金额: $ 21.99万
• 依托单位: University of Maine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1762526&HistoricalAwards=false
• 关键词: Collaborative; Research; Innovation; Sustainable; Mass

Mass timber technologies, with their promise of improved resource utilization and sustainability, have brought a new excitement to the U.S. building industry. These technologies are allowing a renewable resource to enter into areas of construction that were previously not feasible due to a variety of technical constraints. Despite the promise that mass timber holds, wood is ultimately a natural material with inherent variability and peculiarities. Before a new engineered wood product can be brought to market, extensive and expensive laboratory testing is required to establish its reliable properties. This project will enhance the opportunities for increased use of timber by understanding the fundamental mechanics necessary for accurate performance prediction, including response and behavior under extreme events. The work will exploit advances in both computational modeling and materials characterization techniques, such that structural performance can be reliably predicted. The outcomes of the work will benefit both the manufacturers of wood products, by allowing them to better optimize their fiber resource, and the architects and engineers who develop new structural systems. For these parties, the new predictive capability will streamline both structural/architectural innovation and product development. Existing ties with both the wood products industry and the architectural community will ensure appropriate dissemination of results.In order to realize the above outcomes, specific research activities will focus on a computational framework that will feature a novel mechanisms-based, isogeometric embedded-lattice model approximating the internal structure of mass timber. It will simulate the mechanical behavior at multiple scales including the effect of interfaces and defects. This computational model, which explicitly incorporates material morphology, will be calibrated through an experimental program that isolates and measures the micro- and meso-scale morphological features that dictate material behavior, and it will be validated through structural component-scale testing. Since the approach is designed to capture the physical mechanisms that dictate behavior, larger scale phenomena, such as size effects, will be predicted as a natural outcome of the simulation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

大规模木材技术有望提高资源利用率和可持续性，为美国建筑业带来了新的兴奋点。这些技术使可再生资源能够进入以前由于各种技术限制而不可行的建筑领域。尽管大规模木材有希望，但木材最终是一种具有固有变异性和特殊性的天然材料。在新的工程木材产品可以推向市场之前，需要进行广泛而昂贵的实验室测试，以确定其可靠的性能。该项目将通过了解准确性能预测所需的基本力学，包括极端事件下的响应和行为，增加木材使用的机会。 这项工作将利用计算建模和材料表征技术的进步，从而可以可靠地预测结构性能。 这项工作的成果将有利于木材产品制造商，使他们能够更好地优化其纤维资源，以及开发新结构系统的建筑师和工程师。 对于这些方面来说，新的预测能力将简化结构/架构创新和产品开发。与木制品行业和建筑界的现有联系将确保成果的适当传播。为了实现上述成果，具体的研究活动将集中在一个计算框架上，该框架将采用一种新的基于机制的等几何嵌入网格模型，近似大量木材的内部结构。它将在多个尺度上模拟力学行为，包括界面和缺陷的影响。该计算模型明确纳入了材料形态，将通过一个实验程序进行校准，该程序隔离和测量决定材料行为的微观和介观尺度形态特征，并将通过结构组件规模测试进行验证。由于该方法旨在捕捉决定行为的物理机制，因此更大尺度的现象，如尺寸效应，将被预测为模拟的自然结果。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。