Screening candidate supplementary cementitious materials under standard and accelerated curing through time-series surface resistivity measurements and change-point detection

Rios, Renee T.; Lolli, Francesca; Xie, Liyan; Xie, Yao; Kurtis, Kimberly E.

doi:10.1016/j.cemconres.2021.106538

Cited by 16 publications

(4 citation statements)

References 27 publications

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“…al [42] showed this approach to capture how variations in binder compositions using traditional pozzolans affect the microstructural development in concrete over time. This work was extended [43] to include ASTM C618 non-conforming materials and to both non-accelerated curing and accelerated curing, at 23°C and 38°C respectively. The elevated curing temperature was chosen to investigate if the onset of the pozzolanic reaction in Class F fly ashes, known to begin at around 28 days, could be accelerated.…”

Section: Microstructure and Durability Representationsmentioning

confidence: 99%

“…Using this procedure, the results of slope change-point detection are shown in Table 1. This data-driven approach on time-series experimental data [43] allows the rigorous determination of changes in material behavior in a quicker time frame than is the current industry standard. This approach is transferrable to additional surface resistivity data of emerging materials, which would promote the use of novel materials in concrete, as well as to other time-series measurements in civil engineering, such as those related to durability.…”

Section: Microstructure and Durability Representationsmentioning

confidence: 99%

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Advancing cement-based materials design through data science approaches

Rios¹,

Childs²,

Smith³

et al. 2021

RILEM Tech Lett

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The massive scale of concrete construction constrains the raw materials’ feedstocks that can be considered – requiring both universal abundance but also economical and energy-efficient processing. While significant improvements– from more efficient cement and concrete production to increased service life – have been realized over the past decades through traditional research paradigms, non-incremental innovations are necessary now to meet increasingly urgent needs, at a time when innovations in materials create even greater complexity. Data science is revolutionizing the rate of discovery and accelerating the rate of innovation for material systems. This review addresses machine learning and other data analytical techniques which utilize various forms of variable representation for cementitious systems. These techniques include those guided by physicochemical and cheminformatics approaches to chemical admixture design, use of materials informatics to develop process-structure-property linkages for quantifying increased service life, and change-point detection for assessing pozzolanicity in candidate supplementary cementitious materials (SCMs). These latent variables, coupled with approaches to dimensionality reduction driven both algorithmically as well as through domain knowledge, provide robust feature representation for cement-based materials and allow for more accurate models and greater generalization capability, resulting in a powerful design tool for infrastructure materials.

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Section: Microstructure and Durability Representationsmentioning

confidence: 99%

Section: Microstructure and Durability Representationsmentioning

confidence: 99%

Advancing cement-based materials design through data science approaches

Rios¹,

Childs²,

Smith³

et al. 2021

RILEM Tech Lett

View full text Add to dashboard Cite

show abstract

“…In recent years, the detection and prediction of changes in time series data obtained from observations of a monitored system have become a relevant research topic in various fields [1][2][3]. In particular, change-point detection has attracted considerable interest in the medical and neurological fields, where the accurate determination of changes in physiological parameters is particularly critical [4,5].…”

Section: Introductionmentioning

confidence: 99%

Behavioral Change Prediction from Physiological Signals Using Deep Learned Features

Diraco

Siciliano

Leone

2022

Sensors

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Predicting change from multivariate time series has relevant applications ranging from the medical to engineering fields. Multisensory stimulation therapy in patients with dementia aims to change the patient’s behavioral state. For example, patients who exhibit a baseline of agitation may be paced to change their behavioral state to relaxed. This study aimed to predict changes in one’s behavioral state from the analysis of the physiological and neurovegetative parameters to support the therapist during the stimulation session. In order to extract valuable indicators for predicting changes, both handcrafted and learned features were evaluated and compared. The handcrafted features were defined starting from the CATCH22 feature collection, while the learned ones were extracted using a temporal convolutional network, and the behavioral state was predicted through bidirectional long short-term memory auto-encoder, operating jointly. From the comparison with the state of the art, the learned features-based approach exhibits superior performance with accuracy rates of up to 99.42% with a time window of 70 s and up to 98.44% with a time window of 10 s.

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“…In recent years, the detection and prediction of changes in time series data obtained from observations of a monitored system has become a relevant research topic in various fields [1,2,3]. In particular, change-point detection has attracted considerable interest in medical and neurological fields, where the accurate determination of changes in physiological parameters is particularly critical [4,5].…”

Section: Introductionmentioning

confidence: 99%

Behavioral Change Prediction from Physiological Signals Using Deep Learned Features

Diraco¹,

Siciliano²,

Leone³

2022

Preprint

View full text Add to dashboard Cite

Predicting change from multivariate time series has relevant applications ranging from medical to engineering fields. Multisensory stimulation therapy in patients with dementia aims to change the patient’s behavioral state. For example, patients who exhibit a baseline of agitation may be paced to change their behavioral state to relaxed. This study aims to predict changes in behavioral state from the analysis of the physiological and neurovegetative parameters to support the therapist during the stimulation session. In order to extract valuable indicators for predicting changes, both handcrafted and learned features were evaluated and compared. The handcrafted features were defined starting from the CATCH22 feature collection, while the learned ones were extracted using a Temporal Convolutional Network, and the behavioral state was predicted through Bidirectional Long Short-Term Memory Auto-Encoder, operating jointly. From the comparison with the state-of-the-art, the learned features-based approach exhibits superior performance with accuracy rates of up to 99.42% with a time window of 70 seconds and up to 98.44% with a time window of 10 seconds.

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Screening candidate supplementary cementitious materials under standard and accelerated curing through time-series surface resistivity measurements and change-point detection

Cited by 16 publications

References 27 publications

Advancing cement-based materials design through data science approaches

Advancing cement-based materials design through data science approaches

Behavioral Change Prediction from Physiological Signals Using Deep Learned Features

Behavioral Change Prediction from Physiological Signals Using Deep Learned Features

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