PECASE: Revolutionizing Surface Wave Methods for Engineering Analyses - from Deterministic and Incoherent to Probabilistic and Standardized (DIPS)

该教师早期职业发展（Career）计划奖由土木、机械和制造创新司和国际科学与教育办公室共同资助。在过去的十年中，表面波方法（SWMs）已经成为岩土工程现场调查的强大工具，其最终结果-小应变剪切模量/剪切波速（Vs）的地下剖面-被用作许多工程分析的关键输入参数。SWMs的使用越来越广泛，这是由于人们希望“到达”地球内部，并在不需要钻孔的情况下获取准确而有意义的工程参数。传统上，swm已被用于为每个测试站点提供单一的、确定性的Vs配置文件，而不考虑测量/分散不确定性以及它如何通过用于估计Vs的反演过程向前传播。然而，随着专业转向概率设计和基于性能的工程，swm无法量化Vs的不确定性已被暴露为未来进展的主要障碍。越来越多的研究人员和从业人员在使用SWMs时，并不了解诸如空间采样间隔、阵列孔径、源接近度和信噪比等采集参数如何影响其结果的不确定性。此外，只有坊间的建议（通常是相互矛盾的）来指导这些参数相对于期望的探索深度的选择。在岩土工程中，可能没有其他非标准化测试比SWMs应用更广泛。PI将在他的职业生涯中解决这些问题，将swm从确定性和不连贯的革命转变为概率和标准化（DIPS）。dip计划（旨在“消除”swm的“下跌”）包括：(1)量化swm中的测量/色散不确定性，以便基于蒙特卡罗的反演可以将这种不确定性向前传播到一套可接受的具有层厚度和速度置信区间的v剖面（即，从确定性推进到概率）；(2)制定适用于解决工程问题的swm标准（即，从不连贯的建议推进到连贯的标准）。DIPS计划的目标是在全国关键基准站点使用四种主要类型的swm，系统地改变采集参数，收集和分析一组独特的、大型的、自由共享的实验数据。将使用新提出的方法对每组采集参数进行有意义的色散不确定性评估。然后将审查分散估计的方法内变异性，并选择具有最低不确定性的一组参数，以确定标准的制定。通过对色散不确定性有意义的估计，蒙特卡罗反演将用于建立v层厚度和速度的置信区间，从而产生完全概率的结果，可以纳入随后的基于性能的分析。在此步骤之后，将检查方法间的可变性，以评估各种swm和井眼v测量之间的偏差。阿肯色大学（UA）将PI置于一个独特的位置，以执行DIPS计划，并将其与现有的教育计划相结合，旨在增加本科生的研究经验，使学生群体多样化，并发展国际合作。自他的本科研究项目以来，他一直致力于将swm的研究和教育结合起来。DIPS计划不仅允许学生参与本科研究，还将通过UA和意大利都灵理工大学（PDT）之间的预先存在的外汇协议，提供在国外度过一个学期的机会。PI在PDT的同事都是面波测试方面的专家，这个项目为我们的机构、教授和学生通过本科生研究人员参与国际教育、思想和文化交流开辟了一个独特的机会。参加PDT交流的学生招募将通过工程职业意识计划（ECAP）进行，这是UA工程学院的一个计划，结合了几个试点和经过验证的策略来招募，留住和毕业少数民族本科生。UA跟踪ECAP学生的进度，以评估项目的影响，提供一个预先存在的机制来跟踪具有国际PDT研究经验的ECAP学生的保留和成功。PI认为，这种经历将导致100%的保留率，并且这些未被充分代表的学生很有可能通过研究生学习继续他们的教育。

This Faculty Early Career Development (CAREER) Program award is jointly funded by the Civil, Mechanical and Manufacturing Innovation Division and the Office of International Science and Education. Surface wave methods (SWMs) have become fully entrenched as powerful tools in geotechnical site investigation over the past decade, and their end result - a subsurface profile of small-strain shear modulus/shear wave velocity (Vs) - is used as a key input parameter in many engineering analyses. The expanding use of SWMs is driven by the desire to "reach" within the earth and retrieve accurate and meaningful engineering parameters without the need for borings. Traditionally, SWMs have been used to provide a single, deterministic Vs profile for each site tested, without consideration given to measurement/dispersion uncertainty and how it propagates forward through the inversion process used to estimate Vs. However, as the profession moves toward probabilistic design and performance-based engineering, the inability to quantify uncertainty in Vs from SWMs has been exposed as a major impediment to future progress. An ever increasing number of researchers and practitioners are using SWMs without understanding how acquisition parameters such as spatial sampling interval, array aperture, source proximity, and signal-to-noise ratio influence the uncertainty of their results. Furthermore, only anecdotal recommendations exist (often conflicting) to guide the selection of these parameters relative to a desired depth of exploration. It is likely that no other non-standardized test is used in geotechnical engineering more widely than SWMs. The PI will address these issues in his career by revolutionizing SWMs from Deterministic and Incoherent to Probabilistic and Standardized (DIPS). The DIPS plan (aimed at "smoothing-out the dips" in SWMs) involves: (1) quantifying measurement/dispersion uncertainty in SWMs so that Monte Carlo-based inversions can be used to propagate this uncertainty forward into a suite of acceptable Vs profiles with confidence intervals on layer thickness and velocity (i.e., advancing from deterministic to probabilistic), and (2) developing standards for SWMs applied to solving engineering problems (i.e., advancing from incoherent recommendations to coherent standards). The DIPS plan is guided by the vision to collect and analyze a unique, large and freely-shared set of experimental data at key benchmark sites across the country using the four main types of SWMs with systematically varied acquisition parameters. Meaningful dispersion uncertainty will be evaluated for each set of acquisition parameters using newly-proposed methods. Intra-method variability in dispersion estimates will then be examined and the set of parameters with the lowest uncertainty selected to anchor the development of standards. With meaningful estimates of dispersion uncertainty, Monte Carlo inversions will be used to establish confidence intervals for Vs layer thickness and velocity, resulting in fully probabilistic results that can be incorporated into subsequent performance-based analyses. Following this step, inter-method variability will be examined in order to evaluate bias between various SWMs and borehole Vs measurements.The University of Arkansas (UA) has placed the PI in a unique position to carry out the DIPS plan and integrate it with existing educational programs aimed at increasing undergraduate research experiences, diversifying the student body, and developing international collaborations. The PI is committed to integrating research and education in SWMs, having been involved in this field since his own undergraduate research project. The DIPS plan will not only allow for students to become involved in undergraduate research, but will also provide an opportunity to spend a semester abroad through a preexisting foreign exchange agreement between UA and the Politecnico di Torino (PDT) in Italy. The PI's colleagues at PDT are experts in surface wave testing, and this project opens up a unique chance for our institutions, professors and students to participate in international exchange of education, ideas and culture through undergraduate researchers. Recruitment of students to participate in the PDT exchange will occur through the Engineering Career Awareness Program (ECAP), a UA College of Engineering program that combines several piloted and proven strategies to recruit, retain and graduate minority undergraduate students. UA tracks the progress of ECAP students to assess the impact of the program, providing a pre-existing mechanism to track the retention and success of ECAP students with international PDT research experiences. The PI believes that this experience will lead to 100 percent retention and a high likelihood that these underrepresented students will continue their education via graduate studies.