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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Assessment of Machine Learning for Ultrasonic Nondestructive Evaluation of Alkali–Silica Reaction in Concrete

Alkali–silica reaction (ASR) is a type of material degradation in concrete structures that leads to concrete cracking and rebar corrosion, thereby reducing the material’s structural integrity and the overall structure’s lifetime and raising safety concerns. Ultrasonic nondestructive evaluation (NDE) has been proven to be a valuable technique for assessing concrete properties and monitoring ASR progression in concrete. However, the deployment and analysis of ultrasonic NDE and its data requires specialized expertise, often relying on the engineer’s subjective interpretation. With the surge in computational power, artificial intelligence (AI) and machine learning (ML) algorithms have become popular in automating NDE data analysis. Various industrial sectors are increasingly adopting ML algorithms for NDE data analysis with a growing emphasis on AI–assisted automation. Regulatory agencies are also preparing for this technological shift, anticipating corresponding revisions in standards. Thus, there is an urgent need to identify the capabilities and limitations of current ML technologies for the evaluation of concrete material properties and damage status. Furthermore, the effects of various factors on ML model performance must be thoroughly investigated. The study summarized herein evaluated the effectiveness of two ML models (i.e., support vector regression (SVR) and deep neural network (DNN)) in predicting concrete material damage induced by ASR based on the long-term ultrasonic monitoring data. Four distinct concrete specimens were cast with artificially induced ASR, and over a period exceeding 500 days, ultrasonic signals and expansion data were continuously collected. For the SVR model, wave velocity and 12 other wave features were extracted from the ultrasonic signals, with 6 out of 13 features selected as input for the model. Different combinations of training and testing datasets were designed to explore factors influencing prediction performance, including the range of data within training and testing sets, in addition to various signal preprocessing methodologies. These findings suggest the importance of using a training dataset with a broader data range compared with testing datasets for improved model performance alongside consistent signal preprocessing across datasets.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Performance Comparison of Machine Learning Models for Ultrasonic Nondestructive Evaluation of Alkali-Silica Reaction in Concrete

Alkali-silica reaction (ASR) causes concrete degradation, leading to cracking, rebar corrosion, and reduced structural integrity, which raises safety concerns. Ultrasonic nondestructive evaluation (NDE) effectively assesses concrete properties and monitors ASR progression. However, its deployment and analysis require specialized expertise and subjective interpretation. As computational power increases, artificial intelligence (AI) and machine learning (ML) algorithms are increasingly being used to automate NDE data analysis across various industries for AI-assisted automation. Regulatory agencies are adapting to this technological shift, prompting a need to evaluate current ML technologies’ capabilities and limitations in assessing concrete material properties and damage. This report presents a comparative analysis of four ML regression models for predicting concrete material damage induced by ASR expansion using long-term ultrasonic data monitoring. The models investigated include linear regression (LR), support vector regression (SVR), shallow neural networks (NN), and deep neural networks (DNN). LR, SVR, and shallow NN models use features extracted from ultrasonic signals, whereas the DNN model processes time-domain ultrasonic signals and frequency spectra directly. The study systematically compared the models’ performance from various perspectives, including model input, prediction performance, and generalization ability. The findings indicate significant variability in model performance, with some ML algorithms achieving very high or very low prediction accuracy depending on the preprocessing and feature engineering (extraction and selection) applied. Key insights include the observation that shallow ML models (LR, SVR, and shallow NNs) require meticulous preprocessing and feature extraction to achieve high accuracy. In contrast, the DNN model, although it bypasses the need for feature engineering, necessitates extensive preprocessing to mitigate noise and computational demands. The SVR model emerged as the top performer among the shallow models, and the DNN model exhibited superior performance on specific datasets but struggled with generalization across specimens from different batches. Additionally, the SVR model is sensitive to temperature variations, whereas the DNN model is robust in this regard. Using recurrent neural networks is recommended for future ASR expansion prediction studies. Recurrent neural networks’ inherent ability to capture temporal dependencies and long-term patterns makes them well suited for analyzing sequential ultrasonic monitoring data. Overall, the results and conclusions of this study could provide insights into the capabilities and effectiveness of ML when applied to ultrasonic NDE data and help identify best practices for using ML for ultrasonic NDE of concrete material properties.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Synthesis and Crystal Structures of Alkali (Na, Rb, Cs) Iron Phosphate Crystals

Three alkali-containing diphosphate crystals of A FeP 2 O 7 (A = Na, Rb, Cs) were synthesized through melt-crystallization. The crystal structures were determined using single-crystal X-ray diffraction (XRD) data. The synthesized diphosphates crystallized within the P 2 1 /c space group, and the structural parameters were compared to literature data of other A FeP 2 O 7 crystals within the P 2 1 /c space group.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Time-Evolved Hetero-Alkali Interphases Enable Long-Life Sulfide-Based Anode-Free Solid-State Batteries

Sulfide-based anode-free solid-state batteries (AFSSBs) offer compelling advantages in terms of energy density and safety, yet their practical implementation is severely hindered by undesirable interfacial reactions between sulfide solid electrolytes (SEs) and freshly plated lithium (Li), as well as non-uniform Li plating/stripping behavior. Herein, an effective interfacial stabilization strategy by incorporating sodium bis(fluorosulfonyl)imide (NaFSI) additive into the Li5.4PS4.4Cl1.6 (LPSC) is investigated. Unlike conventional Li-based additives that form static passivation layers, NaFSI introduces a transient hetero-alkali chemistry that kinetically governs interphase evolution during fresh Li plating. NaFSI induces a timesequenced interphase evolution: an initial NaF/LiF-rich layer that suppresses early sulfide reduction, followed by a LiF/Li3N-rich layer that optimizes Li⁺ transport during repeated anode-free cycling. This evolved robust and fast ion conducting layer mitigates interfacial impedance growth, enhances Li + transport kinetics, and suppresses localized Li growth and filamentary shorting. As a result, the anode-free full cell with NaFSI modified LPSC as the interlayer exhibits an excellent cycling stability over 500 cycles at 0.2 C with a capacity retention of 77.6%, whereas the cell with bare LPSC suffers from rapid capacity decay after 100 cycles, retaining only 32.1% of its initial capacity. This work establishes dynamic heteroalkali additive chemistry as a general strategy to kinetically program solid-solid interphases, guiding the interface design in anode-free solid-state batteries.

25 ENERGY STORAGE↗
DOE OSTI2025

Synthesis and characterization of uranium trichloride in alkali-metal chloride media

Given a growing interest in uranium salts for pyrochemical processing of used fuel and uranium-fueled molten salt reactors, the synthesis of uranium trichloride in alkali-metal chloride media was investigated in a series of four experiments. Specifically, uranium metal powder and uranium hydride powder were prepared and separately blended with ammonium chloride and lithium chloride – potassium chloride eutectic in two runs, while the same powders were separately blended with ammonium chloride and sodium chloride in two additional runs. Each of the lithium chloride – potassium chloride containing blends was slowly heated to 923 K, while those containing sodium chloride were heated to 1123 K. During each heat up, the ammonium chloride sublimed into gaseous ammonia and hydrogen chloride, leading to the chlorination of uranium metal or uranium hydride and the formation of molten salt solutions of the respective chlorides. Experimental conditions were incorporated in the runs to promote formation of uranium trichloride over uranium tetrachloride in the respective media. Molten samples of each run product were taken and characterized via chemical analyses, diffractometry, and microscopy. The final products from each run were dark dense ingots of the respective salt systems with uranium concentrations ranging from 44 to 51 wt%. Chemical analyses and diffractometry identified the predominant presence of uranium trichloride in these systems; however, a possible minor presence of uranium tetrachloride could not be conclusively dismissed.

D Herrmann, Steven↗
DOE OSTI2022

The role of alkali metal exchange in zeolite-templated carbon synthesis

Despite great efforts to achieve ideal atomistic packing of carbon in the pore networks of even the largest pore zeolites, templating inaccuracies are ubiquitous in all presently synthesized zeolite-templated carbon (ZTC) materials. Idealized models suggest that the long-sought schwarzite-like structures could in principle be obtained by zeolite templating if the appropriate zeolite template were chosen (e.g., faujasite for D surface schwarzites) and if perfect template fidelity (insertion of a pristine layer of pure carbon directly on the surface of the zeolite) could be achieved. A requirement to achieve such structures is increased carbon density within the zeolite. We report the investigation of a series of alkali metal cation-exchanged zeolites to determine how the periodic trends in the group 1 elements influence zeolite templating, with a specific focus on the metric of structural packing density (SPD) as resolved by ex situ thermogravimetry. In a survey based on controlled synthesis temperature, time, and flow conditions, an increasing SPD was observed with decreasing cation size, an effect that is consistent with the increasing strength of cation-π interactions. Furthermore, this effect could be promising for future work to increase the SPD of ZTCs for the synthesis of closed-tube, schwarzite-like carbonaceous solids.

03 NATURAL GAS↗