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Fatigue limit states often govern the design of shear connectors in steel‐concrete composite bridges. AASHTO LRFD bridge design specifications provides a linear equation in a semi‐logarithmic S‐N curve for predicting the fatigue life of shear connectors. However, this equation can be too conservative in some cases, as supported by the available experimental data. In this paper, artificial intelligence (AI) was incorporated into the prediction of the fatigue life of shear connectors. Six different machine learning (ML) algorithms were considered for this purpose. The predictions of ML algorithms were compared both with the available experimental data and the equation provided by AASHTO. The results showed that the fatigue life predicted by ML methods is more accurate than that predicted by the current equation of AASHTO. The results of this study showed that AI can be a proper alternative to the existing methods for predicting the fatigue life of shear connectors. Highlights: AI was used for the first time to evaluate fatigue life of shear connectors in bridges.AI predicted fatigue life of shear connectors more accurately than the current methods.GPR and DT algorithms were the best algorithms for assessing fatigue life of connectors.The current equations in AASHTO for fatigue may need to be redeveloped based on AI. [ABSTRACT FROM AUTHOR]

Abstract: Impedance cardiography is the most common clinically validated, noninvasive method for determining the timing of the opening of the aortic valve, an important event used for measuring preejection period, which reflects sympathetic beta‐adrenergic influences on the heart. Automatic detection of the exact time of the opening of the aortic valve (B point on the impedance cardiogram) has proven to be challenging as its appearance varies between and within individuals and may manifest as a reversal, inflection, or rapid slope change of the thoracic impedance derivative's (dZ/dt) rapid rise. Here, a novel automatic algorithm is proposed for the detection of the B point by finding the main rapid rise of the dZ/dt signal, which is due to blood ejection. Several conditions based on zero crossings, minima, and maxima of the dZ/dt signal and its derivatives are considered to reject any unwanted noise and artifacts and select the true B‐point location. The detected B‐point locations are then corrected by modeling the B‐point time data using forward and reverse autoregressive models. The proposed algorithm is validated against expert‐detected B points and is compared with different conventional methods; it significantly outperforms them by at least 54% in mean error, 30% in mean absolute error, and 27% in standard deviation of error. This algorithm can be adopted in ambulatory studies requiring beat‐to‐beat evaluation of cardiac hemodynamic parameters over extended time periods where expert scoring is not feasible. [ABSTRACT FROM AUTHOR]

The pre‐ejection period (PEP) is a valid index of myocardial contractility and beta‐adrenergic sympathetic control of the heart defined as the time between electrical systole (ECG Q wave) to the initial opening of the aortic valve, estimated as the B point on the impedance cardiogram (ICG). B‐point detection accuracy can be severely impacted if ICG cardiac cycles corrupted by motion artifact, noise, or electrode displacement are included in the analyses. Here, we developed new algorithms to detect and exclude corrupted ICG cycles by analyzing their level of activity. PEP was then estimated and analyzed on ensemble‐averaged clean ICG cycles using an automatic algorithm previously developed by the authors for the detection of B point in awake individuals. We investigated the algorithms' performance relative to expert visual scoring on long‐duration data collected from 20 participants during overnight recordings, where the quality of ICG could be highly affected by movement artifacts and electrode displacements and the signal could also vary according to sleep stage and time of night. The artifact rejection algorithm achieved a high accuracy of 87% in detection of expert‐identified corrupted ICG cycles, including those with normal amplitude as well as out‐of‐range values, and was robust to different types and levels of artifact. Intraclass correlations for concurrent validity of the B‐point detection algorithm in different sleep stages and in‐bed wakefulness exceeded 0.98, indicating excellent agreement with the expert. The algorithms show promise toward sleep applications requiring accurate and reliable automatic measurement of cardiac hemodynamic parameters. The pre‐ejection period (PEP) is a valid index of sympathetic beta‐adrenergic influences upon the heart measured from the simultaneously recorded electrocardiogram and impedance cardiogram (ICG). PEP accuracy can be severely impacted if ICG cardiac cycles corrupted by motion artifact, noise, or electrode displacement are included in the analyses. Here, we developed new algorithms to automatically detect and exclude corrupted ICG cycles by analyzing their level of activity and evaluated the algorithms' performance on sleep data affected by noise and artifacts. The algorithms performed well and showed promise toward sleep applications requiring accurate and reliable automatic measurement of cardiac hemodynamic parameters. [ABSTRACT FROM AUTHOR]