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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Using dorsal surface for individual identification of dairy calves through 3D deep learning algorithms

Advances in machine learning techniques have allowed the development of computer vision systems (CVS) that can accurately predict several phenotypes of interest for livestock operations. In this context, 3D images taken from a top-down view are particularly useful for estimating body condition score, growth development, and body biometrics in cattle. Frequently, such CVS rely on identification (ID) systems, such as electronic tags, as a way to match animal ID and the predicted phenotype. However, the same 3D images used to predict body weight and other animal biometrics could be adopted for animal recognition as well. Such alternative would optimize CVS to recognize animal ID and monitor growth development simultaneously while leveraging the same hardware infrastructure. Furthermore, this strategy could be used to recognize animals with similar color patterns. Nonetheless, growing animals are continuously changing body shape, which could limit its use as an invariant feature for pattern recognition. Thus, the objectives of this study were: (1) to compare algorithms for different 3D object representations to identify individual animals; and (2) to evaluate how short-term changes in body shape due to animal growth affect the predictive performance of these algorithms. For objective 1, the algorithms were trained (n = 4,558) and tested (n = 1,139) using images from 38 Holstein calves. For objective 2, we designed three different experiments using images (n = 2,347) from five Holstein calves taken over six weeks during their growing period, always training and testing on different weeks. Each experiment evaluated how changing a different parameter of the image capturing procedure affected the predictive ability of the trained algorithms. In the first experiment, we varied the total number of images per animal in the training set; in the second experiment, we varied the number of weeks while keeping a fixed number of images in the training set; and in the third experiment, we skipped weeks between images in the training and test sets. The F 1 score for objective (1) was up to 0.804 when testing with the last frames of each video, and up to 0.959 when using random frames for testing. For objective (2), the F 1 score was up to 0.947 for the first experiment when using 130 images per animal; up to 0.979 for the second experiment when using all five weeks; and up to 0.917 when not skipping weeks between training and testing. In conclusion, these results show that deep learning algorithms can be used to identify individual animals through their dorsal area 3D surfaces, and, from our experiments using calves in their growing period, that they are robust enough to account for changes in body shape and size, making them a promising tool for animal recognition during growth.

3D neural networks↗
DOE OSTI2020

Atomic resolution convergent beam electron diffraction analysis using convolutional neural networks

Two types of convolutional neural network (CNN) models, a discrete classification network and a continuous regression network, were trained to determine local sample thickness from convergent beam diffraction (CBED) patterns of SrTiO 3 collected in a scanning transmission electron microscope (STEM) at atomic column resolution. Acquisition of atomic resolution CBED patterns for this purpose requires careful balancing of CBED feature size in pixels, acquisition speed, and detector dynamic range. The training datasets were derived from multislice simulations, which must be convolved with incoherent source broadening. Sample thicknesses were also determined using quantitative high-angle annular dark-field (HAADF) STEM images acquired simultaneously. The regression CNN performed well on sample thinner than 35 nm, with 70% of the CNN results within 1 nm of HAADF thickness, and 1.0 nm overall root mean square error between the two measurements. The classification CNN was trained for a thicknesses up to 100 nm and yielded 66% of CNN results within one classification increment of 2 nm of HAADF thickness. Our approach depends on methods from computer vision including transfer learning and image augmentation.

36 MATERIALS SCIENCE↗
DOE OSTI2020

All Hands On Deck: Raising the Bar on Whole Building Performance-Based Code Compliance and Above-Code Programs

In 2019 a large-scale national effort was launched with support from U.S. Department of Energy, Pacific Northwest National Laboratory and Northwest Energy Efficiency Alliance (NEEA). The project aimed to develop a long-term vision and a roadmap for achieving a practical application of whole building performance-based code and above code program compliance in commercial buildings. The work was aided by engaging over 70 stakeholders across the country representing jurisdictions, above-code programs such as LEED and ENERGY STAR Multifamily Program, members of the ASHRAE Standard 90.1 and other Standard committees, IBPSA and software developers. The performance path requires whole building energy modeling and is gaining momentum. Many see it as the future of the commercial energy codes and the main pathway for achieving zero energy buildings. The paper describes the findings from a stakeholder survey funded by NEEA on the current state of performance-based path, its market penetration, trends, implementation practices and adoption challenges. It discusses the patterns revealed by experience of the states with decades-long history of performance-based compliance such as Florida and California and opportunities for taking advantage of the synergies between code and above-code programs. In addition, the paper provides an overview of the tools and resources developed as part of the effort including the compliance form for ASHRAE Standard 90.1 Section 11 and Appendix G, modeling submittal review manual, and modeler and reviewer qualification requirements. The tools and resources would help remove market barriers, deliver immediate benefits to jurisdictions and rating authorities, and achieve energy efficiency through whole-building performance path.

TSPR, Performance Based Complinace, Washington Sta↗
DOE OSTI2022

Integrated Monitoring of Macroalgae Farms Using Acoustics and UUV Sensing

The vision of this project was to develop an integrated system for autonomous underwater vehicle (AUV) monitoring of offshore kelp farms using acoustic, environmental, and optical sensors. This project supports the overall MARINER goals of developing an offshore kelp aquaculture industry to produce low-carbon or carbon-neutral biofuels. The project commenced in the spring of 2018 and used laboratory experiments to test the efficacy of acoustic sensors for monitoring kelp farm lines and growing kelp biomass. Sensors were then integrated onto two AUVs as well as an autonomous surface vehicle in order to establish the optimal type, price point, and vehicle to most efficiently monitor kelp farm structures, kelp biomass, and the surrounding environment. Multiple field deployments of these vehicles and sensors in Massachusetts, New Hampshire, and Maine confirmed that farm lines and kelp could be visualized and that monitoring the spatial patterns of environmental variables was possible. The COVID-19 pandemic severely limited fieldwork activities and laboratory testing, with deployments around kelp farms not occurring again until January 2021. Time spent away from the field focused on data visualization and the development of a low-cost, vessel-based sensor system. Unfortunately, the low-cost system experienced a disk failure during its first deployment and due to multiple resignations from the project team further engineering and development was not possible. Additionally, final results from acoustic sensor testing in 2021 were also not able to be completed due to the resignation of the postdoc leading the analysis. Despite these setbacks, multiple avenues for further development of optical imagery processing from the 360-degree Kelpcam camera and testing of the low-cost sensor system may be possible.

09 BIOMASS FUELS↗
DOE OSTI2026

ObstacleSense: Low-Power Neuromorphic Vision for Corridor Obstacle Awareness in Low-Level ADAS

The automotive industry’s pursuit of Level 5 autonomy is constrained by substantial perception-compute power requirements, often reaching 1, 000 + watts in full autonomy stacks. Reducing this energy burden requires rethinking perception not only at the high-end autonomy level, but also at the foundational Advanced Driver Assistance Systems (ADAS) level where low-power, safety-critical sensing can have broad impact. Neuromorphic vision provides a promising starting point: HD Dynamic Vision Sensors (DVS) can operate below 100 mW at the sensor level by reporting only asynchronous brightness changes. However, low-power sensing alone is insufficient if downstream perception reintroduces dense, energy-intensive computation. In particular, many event-driven object-detection pipelines still rely on CNN backbones, while purely spiking alternatives often trade away accuracy or ignore deployment constraints. We introduce ObstacleSense, a highly compact, CNN-free hybrid ANN–SNN framework for Level 0–1 forward-corridor obstacle awareness. Instead of performing full-scene object detection with a convolutional feature backbone, ObstacleSense targets the safety-critical question of whether the ego corridor is occupied and how far the nearest obstacle is. The architecture combines polarity-conditioned event encoding, lightweight temporal spiking dynamics, axial spatial mixing, and coarse-to-fine range estimation within a regular fixed-grid compute pattern. This design avoids the dense CNN backbone commonly used in event-based detection while maintaining a small state footprint suitable for eventual small-FPGA deployment. Before hardware mapping, we evaluate the software implementation using a model-side power proxy derived from MACs, weight and activation traffic, and spiking state updates under shared FP16 assumptions. On simulated CARLA event corpora, the deployment-oriented model achieves 0.9464 objectness F1, 0.9978 grid-level mAP, and 0.8987 m distance Mean Absolute Error at an estimated 1.92 mW proxy cost, while maintaining performance on unseen generalization test sequences.

Johnson-Scott, Zac [ORNL]↗
DOE OSTI2018

A Scalable HPC Insider Threat Monitoring System

Cyber Equations, Inc. is reporting on the results of its DOE SBIR Phase I project for the development of a Scalable HPC Insider Threat Monitoring System. This final report to the Department of Energy for Task DE-FOA-0001618 covers the period from 02/21/2017 to 02/20/2018 and summarizes the larger research accomplishments, which can be divided in theoretical and laboratory experiments. One of the many challenges for the U.S. Department of Energy (DOE) is the support of the high-performance computing (HPC) centers while providing quality information services, strategic vision, leadership, technical expertise, and capabilities necessary to enable the DOE to deliver effective, efficient, economical, and secure HPC services. In order to realize a secure DOE laboratory enterprise, DOE facilities operations must counter a wide range of cyber insider threats, from unsophisticated adversaries up to advanced, nation state sponsored actors. The DOE laboratories also presents an enormous insider attack surface of an ever-growing number of computers, networks, and devices all running a myriad of software. In addition, the DOE laboratories are connected to a variety of external organizations which multiplies the possibility of insider threat. During this SBIR project Cyber Equations developed a strategic combination of supervised and unsupervised machine learning techniques to control scale and classify whether entities, such as hosts, actors, or documents, are showing suspicious insider behavior. We conducted experiments using real cyber data to show that changes in observable behavior (e.g., changes in outbound traffic patterns) are indicative of attacks executing (e.g., insiders exfiltrating data, bots calling home, or an attack spreading). The features and methods we use, have been used in the anomaly detection research and development community for a significant amount of time and robust implementations are openly available. K-Means clustering, Decision Trees, and Support Vector Machine based classifiers are readily available through the R language and open source H20 machine learning framework. The Cyber Equations approach is novel in its use of these classifiers combined with Hadoop based parallel processing technology. The five stages of processing used by CyberInsider improve scalability and reduce the high number of false positives normally associated with network anomaly detection. In summary, the Cyber Equations has successfully accomplished all of its Phase I tasks and demonstrated the feasibility of the Insider Threat Monitoring System.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Poster Abstract: Leveraging Large Language Models to Reveal Interpretable Cooling Behaviors from Smart Thermostat Data

Frequent heatwaves and hot summers increasingly challenge occupant comfort, health, and energy grid stability. Addressing these challenges requires a detailed understanding of household cooling behaviors, such as thermostat adjustments and adaptive responses to extreme conditions. Traditional analyses often rely on aggregated numerical metrics that overlook subtle but important household-specific variations. In this study, we introduce a generalizable methodology that integrates large language models (LLMs) with vision capabilities to enable scalable and detailed analysis of residential thermostat data. Using Ecobee's Donate Your Data (DYD) dataset—which provides five-minute records of indoor temperatures, thermostat setpoints, and HVAC runtimes—we focus on two U.S. cities with contrasting summer climates : Austin (TX) and Phoenix (AZ). Because raw time-series data are not well suited for direct LLM analysis, we transform them into visual representations, such as daily indoor temperature trajectories and weekly runtime histograms, to better capture behavioral variations. Leveraging LLMs' visual interpretation, we extract descriptive behavioral features, including temperature preferences, time-of-day cooling orientation, anticipatory versus reactive heatwave responses, and behavioral consistency. These semantic features support unsupervised clustering to identify distinct occupant archetypes at scale, revealing differences—such as morning-centric anticipatory coolers versus households that shift toward warmer setpoints during heatwaves—that can inform demand response, resilience planning, and health-aware interventions. By converting raw numerical data into interpretable behavioral patterns, this methodology enables scalable and practical analysis of occupant behavior, supporting actionable insights for comfort, resilience, and energy management.

Nihar, Kopal↗
DOE OSTI2024

Demonstration and Automation of Reflected Target Optical Measurement for Heliostats

Accurate optical surfaces are a primary driver of concentrated solar power plant performance. Errors in pointing and tracking mirrors, the canting of individual mirror facets, and the surface slope of the mirror itself can be caused by errors during assembly, transportation, wind loading, gravity, and many other sources. The tools that exist to measure these error sources today largely rely on fringe deflectometry (SOFAST, QDec, others), or photogrammetry with targets attached to the mirror surface. Since 2022, NREL has been developing a measurement method called the Reflected Target Non-intrusive Assessment (ReTNA) system. This system differs from most established methods in that we perform deflectometry with a pattern of coded targets, identified in space with photogrammetry. Reflected target systems have several advantages over traditional fringe deflectometry systems. Firstly, they can be operated in bright or ambient lighting, a challenge for fringe systems that use a projector and screen. Reflected target systems also can use a much lighter and less expensive target than projector-based systems. Lastly, 2D slope measurement can be solved from a single image, which leads to several advantages for accommodating faster measurements and smaller sized targets. These advantages make ReTNA particularly well-suited for applications where there are space or lighting constraints, like performing heliostat quality assurance on an assembly line. It's also useful when a lightweight, flexible system is needed, like for heliostat developers to quickly measure a new heliostat design at different orientations, to observe gravitational effects on the mirror surface shape. In the last year, significant improvements were made to this tool to make it more useful for these applications. These improvements were focused around validation of the ReTNA measurement system, and automation of the setup and measurement process. First, we present an improved ReTNA layout, for use on the heliostat assembly line. Next, we detail the various changes to the ReTNA software and computer vision methods to automate data collection in this new setup, and lessons learned from this process. The goal with this new setup is to perform a full heliostat surface characterization without removing the mirror from the assembly line. Lastly, we share results from several ReTNA validation studies undertaken over the last year. These include repeated ReTNA measurement on demonstration mirror facets, comparisons with other optical measurement tools, and some studies aimed at quantifying the uncertainty of ReTNA measurement under various constraints (mirror-target spacing, camera resolution, etc.). These results are compared with 2024 HelioCon performance targets, and our planned next steps for the ReTNA measurement system are presented.

CSP↗
DOE OSTI2019

Light Material Interactions in Energy Conversion (Final Report)

The scientific vision of the “Light-Material Interactions in Energy Conversion” Energy Frontier Research Center (LMI-EFRC) is embodied in our mission statement: ‘to tailor the morphology, complex dielectric structure, and electronic properties of matter so as to sculpt the flow of sunlight and heat, enabling light conversion to electrical energy with unprecedented efficiency’. Founded in 2009, the LMI-EFRC has become a national resource for fundamental optical principles and phenomena relevant to solar energy conversion, addressing new scientific opportunities that enable efficient utilization of the entire visible and infrared solar resource. The LMI-EFRC is directed by Prof. Ralph Nuzzo and centered at its lead institution, the California Institute of Technology. The Center is an integrated and collaborative partnership of research leaders in photonics and photonic materials, comprising 14 faculty investigators that span six academic departments at five institutions, including Profs. Harry Atwater, Andrei Faraon, Nathan Lewis, and Austin Minnich at Caltech; Profs. Ralph Nuzzo, Paul Braun and John Rogers at the University of Illinois at Urbana-Champaign; Prof. Jennifer Lewis at Harvard University; Profs. Mark Brongersma, Jennifer Dionne, and Shanhui Fan at Stanford University; and Profs. Paul Alivisatos, Eli Yablonovitch, and Xiang Zhang at Lawrence Berkeley National Laboratory. The Center is organized into four research groups (RGs) that address scientific themes related to light-matter interactions, with each team spanning multiple institutional partners and designed to meet our scientific objectives. Notable project outcomes that address the LMI Mission and 2014-2018 goals include: (1) RG1 “New Light Management Mechanisms” demonstrated tunable and active nanostructured patterned surfaces (‘metasurfaces’) for control of light absorption, reflection, and propagation. (2) RG2 “Solar Spectrum Control and Conversion” developed colloidal nanocrystals with designer optical properties for fundamental studies of solar spectrum control and conversion. (3) RG3 “Thermal Photon Harvesting” established photonic design principles to harness and control thermal photons in novel conversion systems. (4) RG4 “Programmable Assembly of Photonic and Electronic Architectures” developed powerful new methods for light- and matter-based additive fabrication of electronic and photonic architectures.

14 SOLAR ENERGY↗
DOE OSTI2022

An EvoDevo Study of Salmonid Visual Opsin Dynamics and Photopigment Spectral Sensitivity

Salmonids are ideal models as many species follow a distinct developmental program from demersal eggs and a large yolk sac to hatching at an advanced developmental stage. Further, these economically important teleosts inhabit both marine- and freshwaters and experience diverse light environments during their life histories. At a genome level, salmonids have undergone a salmonid-specific fourth whole genome duplication event (Ss4R) compared to other teleosts that are already more genetically diverse compared to many non-teleost vertebrates. Thus, salmonids display phenotypically plastic visual systems that appear to be closely related to their anadromous migration patterns. This is most likely due to a complex interplay between their larger, more gene-rich genomes and broad spectrally enriched habitats; however, the molecular basis and functional consequences for such diversity is not fully understood. This study used advances in genome sequencing to identify the repertoire and genome organization of visual opsin genes (those primarily expressed in retinal photoreceptors) from six different salmonids [Atlantic salmon ( Salmo salar ), brown trout ( Salmo trutta ), Chinook salmon ( Oncorhynchus tshawytcha ), coho salmon ( Oncorhynchus kisutch ), rainbow trout ( Oncorhynchus mykiss ), and sockeye salmon ( Oncorhynchus nerka )] compared to the northern pike ( Esox lucius ), a closely related non-salmonid species. Results identified multiple orthologues for all five visual opsin classes, except for presence of a single short-wavelength-sensitive-2 opsin gene. Several visual opsin genes were not retained after the Ss4R duplication event, which is consistent with the concept of salmonid rediploidization. Developmentally, transcriptomic analyzes of Atlantic salmon revealed differential expression within each opsin class, with two of the long-wavelength-sensitive opsins not being expressed before first feeding. Also, early opsin expression in the retina was located centrally, expanding dorsally and ventrally as eye development progressed, with rod opsin being the dominant visual opsin post-hatching. Modeling by spectral tuning analysis and atomistic molecular simulation, predicted the greatest variation in the spectral peak of absorbance to be within the Rh2 class, with a ∼40 nm difference in λ max values between the four medium-wavelength-sensitive photopigments. Overall, it appears that opsin duplication and expression, and their respective spectral tuning profiles, evolved to maximize specialist color vision throughout an anadromous lifecycle, with some visual opsin genes being lost to tailor marine-based vision.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

ESnet Requirements Review Program Through the IRI Lens: A Meta-Analysis of Workflow Patterns Across DOE Office of Science Programs (Final Report)

The Department of Energy (DOE) ensures America’s security and prosperity by addressing its energy, environmental, and nuclear challenges through transformative science and technology solutions. The DOE’s Office of Science (SC) delivers groundbreaking scientific discoveries and major scientific tools that transform our understanding of nature and advance the energy, economic, and national security of the United States. The SC’s programs advance DOE mission science across a wide range of disciplines and have developed the research infrastructure needed to remain at the forefront of scientific discovery. The DOE SC’s world-class research infrastructure — exemplified by the 28 SC scientific user facilities — provides the research community with premier observational, experimental, computational, and network capabilities. Each user facility is designed to provide unique capabilities to advance core DOE mission science for its sponsor SC program and to stimulate a rich discovery and innovation ecosystem. Research communities gather and flourish around each user facility, bringing together diverse perspectives. A hallmark of many facilities is the large population of students, postdoctoral researchers, and early-career scientists who contribute as full-fledged users. These facility staff and users collaborate over years to devise new approaches to utilizing the user facility’s core capabilities. The history of the SC user facilities has many examples of wildly inventive researchers challenging operational orthodoxy to pioneer new vistas of discovery; for example, the use of the synchrotron X-ray light sources for study of proteins and other large biological molecules. This continual reinvention of the practice of science — as users and staff forge novel approaches expressed in research workflows — unlocks new discoveries and propels scientific progress. Within this research ecosystem, the high-performance computing (HPC) and networking user facilities stewarded by SC’s Advanced Scientific Computing Research (ASCR) program play a dynamic cross-cutting role, enabling complex workflows demanding high performance data, networking, and computing solutions. The DOE SC’s three HPC user facilities and the Energy Sciences Network (ESnet) high-performance research network serve all of the SC’s programs as well as the global research community. Argonne Leadership Computing Facility (ALCF), the National Energy Research Scientific Computing Center (NERSC), and Oak Ridge Leadership Computing Facility (OLCF) conceive, build, and provide access to a range of supercomputing, advanced computing, and large-scale data-infrastructure platforms, while ESnet interconnects DOE SC research infrastructure and enables seamless exchange of scientific data. All four facilities operate testbeds to expand the frontiers of computing and networking research. Together, the ASCR facilities enterprise seeks to understand and meet the needs and requirements across SC and DOE domain science programs and priority efforts, highlighted by the formal requirements reviews (RRs) methodology. In recent years, the research communities around the SC user facilities have begun experimenting with and demanding solutions integrated with HPC and data infrastructure. This rise of integrated-science approaches is documented in many community and high-level government reports. At the dawn of the era of exascale science and the acceleration of artificial intelligence (AI) innovation, there is a broad need for integrated computational, data, and networking solutions. In response to these drivers, DOE has developed a vision for an Integrated Research Infrastructure (IRI): To empower researchers to meld DOE’s world-class research tools, infrastructure, and user facilities seamlessly and securely in novel ways to radically accelerate discovery and innovation.

42 ENGINEERING↗
DOE OSTI2018

Prefabricated High-Strength Rebar Systems with High-Performance Concrete for Accelerated Construction of Nuclear Concrete Structures

This project aimed to reduce the field construction times and fabrication costs of reinforced concrete (RC) nuclear structures through the use of: 1) high-strength steel deformed reinforcing bar (rebar); 2) prefabricated high-strength rebar (HSR) assemblies with headed anchorages; and 3) high-strength concrete (HSC). The focus of the project was not to develop new innovations on materials, but rather to fill a major knowledge gap on the effectiveness, code conformity, and viability (i.e., practicality, commercial availability) of existing high-strength materials in nuclear structures, especially in shear walls, their connections/joints, and around penetrations. These advances promote the Nuclear Energy Enabling Technologies Advanced Methods for Manufacturing (NEET-1) goals of accelerating deployment schedules by at least 6 months and reducing component fabrication costs by 20% or more. Toward this vision, the following objectives were identified and completed: Develop limit-benefit and cost-benefit analysis frameworks: The structural and economic limits and benefits of HSR and HSC in nuclear walls were evaluated. Importantly, it was found that modest increases in concrete strength up to 10.0 ksi and rebar yield strength up to 100 ksi result in the most economic designs and improved structural performance (Barbachyn et al., 2017a and Barbachyn et al., 2017b). Also, an industry survey was distributed to determine the cost benefits associated with prefabrication. Importantly, on-site worker-hours are significantly reduced, allowing for significantly accelerated construction schedules (Devine et al. 2018b). Develop an optimization methodology for structural design: An optimization study was deemed not necessary since the trivial solution with maximized use of high-strength materials and prefabrication always resulted in the largest cost and time savings. Instead, the benefits (and limits) of high-strength materials and prefabrication on construction costs and on-site construction time were investigated through a parametric study. Cost-reductions through the use of HSR and HSC were found to be greater than 40%. Also, on-site construction time of nuclear shear walls can be reduced by up to 83% through the use of prefabricated rebar assemblies. Conduct experimental evaluations of nuclear shear walls and wall-foundation joints: A large scale experimental program was completed on shear-critical RC cantilever deep beams. From these experiments, it was demonstrated that increasing rebar yield strength had a greater effect on increasing lateral strength than increasing the concrete compressive strength. Further, using HSR together with HSC resulted in the highest lateral strength and displacement ductility (Devine et al. 2018a). Additionally, large-scale reversed-cyclic lateral load tests were conducted on RC shear walls. It was demonstrated that through the utilization of HSR and HSC, a wall with 55% reduced vertical and horizontal rebar area was able to achieve 91% of the peak lateral strength of a conventional wall using state-of-practice materials with the same geometry. Further, trim reinforcement with headed anchorages around the penetrations proved to be effective. Develop validated numerical simulation models: Numerical modeling was completed in two phases during this project using 3 different finite element software packages. The first phase used existing measured data from previous experimental studies on stocky RC walls to validate simplified predictive models. Models using two different programs were validated based on a database of 38 stocky walls (Barbachyn et al., 2017a). The second phase occurred after experimental testing using a third and more detailed finite element package. Prediction improvements for the initial stiffness, lateral deformations, damage propagation, and failure mechanisms were targeted. The resulting detailed models more accurately predicted the peak lateral strength, initial and secant stiffnesses, lateral deformations, and cracking patterns of the wall test specimens subjected to reversed-cyclic lateral loading as compared to the simplified pretest models. Develop validated design procedures, tools, and criteria: This project evaluated the accuracy of existing closed-form design equations from U.S. building design standards and previous research (Barbachyn et al. 2017a and Devine et al. 2018a). It was shown that the current predictive equations for shear strength capacity need to be revisited as they provide significant scatter and gross overestimates, indicating the potential for unconservative designs. Further, for both the deep beam and shear wall specimens, the numerical models and flexural capacity predictions provided the most accurate strength predictions. Develop cost-effective field construction procedures: To promote the use of prefabricated rebar assemblies, the following construction conditions where prefabrication would be used instead of in-place rebar tying were identified from an industry survey: 1) to save on construction schedule; 2) to improve safety and/or quality control; 3) for areas with heavy rebar congestion; and 4) for structures with significant repetition in rebar layout. All of these conditions directly apply and benefit nuclear construction where construction costs and schedules are extensive, safety and quality are of utmost importance, the reinforcement is extremely dense, and the reinforcement layout is repetitive in many regions of the shear walls. Additionally, an experimental study was conducted to investigate the changes in bar spacing (with respect to code-required rebar spacing and placement tolerances) when a horizontally assembled prefabricated two-dimensional (2D) rebar mat or three-dimensional (3D) rebar cage is lifted (or tripped) to a vertical position. It was found that the bars involved in both the lateral translation of the rebar assemblies (i.e., movement of the assembly in its horizontal position) and the tripping process were most susceptible to large spacing changes, and thus, their positions within the assembly would need to be checked after the placement of the assembly in place within the formwork. Importantly, the spacings of all of the other bars in the assembly were not affected significantly by the lateral translation and tripping process, thereby eliminating the need to recheck their positions within the assembly (Devine et al. 2018b).

36 MATERIALS SCIENCE↗