This paper presents a ground motion prediction (GMP) model using an artificial neural network (ANN) for shallow earthquakes, aimed at improving earthquake hazard safety evaluation. The proposed model leverages essential input variables such as moment magnitude, fault type, epicentral distance, and soil type, with the output variable being peak ground acceleration (PGA) at 5% damping. To develop this model, 885 data pairs were obtained from the Pacific Engineering Research Center, providing a robust dataset for training and validation. The ANN architecture comprises 4 nodes in the input layer, two hidden layers each containing 25 nodes, and a single-node output layer, resulting in 750 unknown weight and bias values that the model must optimize. Following the model assessment, a genetic algorithm (GA) was integrated with the ANN model to enhance its predictive capabilities. This integration aimed to forecast 20 potential earthquake scenarios, a crucial step in validating the model's effectiveness. The results were promising, as the ANN-GA successfully predicted earthquake occurrences in 15 out of 20 scenarios. These findings underscore the model's potential in accurately forecasting seismic events, thereby contributing to the development of more resilient infrastructure and better-informed urban planning strategies.
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http://dx.doi.org/10.1038/s41598-024-82171-7 | DOI Listing |
Environ Sci Technol
January 2025
School of Ocean Sciences, Bangor University, Menai Bridge, Anglesey LL59 5AB, U.K.
Accurate prediction of chlorophyll- (Chl-) concentrations, a key indicator of eutrophication, is essential for the sustainable management of lake ecosystems. This study evaluated the performance of Kolmogorov-Arnold Networks (KANs) along with three neural network models (MLP-NN, LSTM, and GRU) and three traditional machine learning tools (RF, SVR, and GPR) for predicting time-series Chl- concentrations in large lakes. Monthly remote-sensed Chl- data derived from Aqua-MODIS spanning September 2002 to April 2024 were used.
View Article and Find Full Text PDFMed Sci Sports Exerc
November 2024
Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC.
Introduction: Individuals with anterior cruciate ligament reconstruction (ACLR) often walk with a less dynamic vertical ground reaction force (vGRF), exemplified by a reduced first peak vGRF and elevated midstance vGRF compared to uninjured controls. However, the mechanism by which altered limb loading affects actual tibial plateau contact forces during walking remains unclear.
Methods: Our purpose was to use musculoskeletal simulation to evaluate the effects of first peak vertical ground reaction force (vGRF) biofeedback on bilateral tibiofemoral contact forces relevant to the development of post-traumatic osteoarthritis (OA) in 20 individuals with ACLR.
J Strength Cond Res
December 2024
School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, United Kingdom.
Kember, LS, Riehm, CD, Schille, A, Slaton, JA, Myer, GD, and Lloyd, RS. Residual biomechanical deficits identified with the tuck jump assessment in female athletes 9 months after ACLR surgery. J Strength Cond Res 38(12): 2065-2073, 2024-Addressing biomechanical deficits in female athletes after anterior cruciate ligament reconstruction (ACLR) is crucial for safe return-to-play.
View Article and Find Full Text PDFJ Strength Cond Res
September 2024
School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, United Kingdom.
Kember, LS, Riehm, CD, Schille, A, Slaton, JA, Myer, GD, and Lloyd, RS. Residual biomechanical deficits identified with the tuck jump assessment in female athletes 9 months after ACLR surgery. J Strength Cond Res XX(X): 000-000, 2024-Addressing biomechanical deficits in female athletes after anterior cruciate ligament reconstruction (ACLR) is crucial for safe return-to-play.
View Article and Find Full Text PDFSci Rep
January 2025
The Fourth Engineering Co., LTD, China Railway Fourth Bureau, Hefei, 230012, People's Republic of China.
Research investigating the complex mechanical properties and energy evolution mechanisms of frozen calcareous clay under the influence of multiple factors is crucial for optimizing the artificial ground freezing method in shaft sinking, thereby enhancing construction quality and safety. In this study, a four-factor, four-level orthogonal test was devised, taking into account temperature, confining pressure, dry density, and water content. The complex nonlinear curvilinear relationship between deviatoric stress, volume strain, and axial strain of frozen calcareous clay under different interaction levels was analyzed.
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