Siddiqui 2026a - Revision history

Scipediacontent: Scipediacontent moved page Review 109271889040 to Siddiqui 2026a

2026-09-25T10:56:57Z

Scipediacontent moved page Review 109271889040 to Siddiqui 2026a

Scipediacontent at 10:54, 25 September 2026

2026-09-25T10:54:19Z

Scipediacontent at 10:53, 25 September 2026

2026-09-25T10:53:40Z

Scipediacontent: Scipediacontent moved page Draft content 461759277 to Review 109271889040

2026-09-25T10:29:31Z

Scipediacontent moved page Draft content 461759277 to Review 109271889040

Scipediacontent: Created page with " == Abstract ==
Accurately obtaining the mechanical properties of embankment soil is the core component for achieving precise calculation and mastering the operational sta..."

2026-09-25T10:29:00Z

Created page with " == Abstract == <p>Accurately obtaining the mechanical properties of embankment soil is the core component for achieving precise calculation and mastering the operational sta..."

New page

== Abstract ==

<p>Accurately obtaining the mechanical properties of embankment soil is the core component for achieving precise calculation and mastering the operational status of the embankment. To achieve precise identification of multiple key material parameters of the embankment soil, this paper proposes an inversion method that integrates monitoring displacement information with intelligent algorithms. This approach comprehensively utilizes monitoring data from multiple monitoring points and different times to collaboratively invert multiple key parameters of the embankment soil. The study establishes a finite element model of the embankment based on the Cvisc (Burgers-Mohr) viscoelastic-plastic constitutive theory, which describes the creep behavior of soil. Orthogonal experimental design is employed to generate multiple parameter combinations of the soil, and numerical simulations are conducted to obtain displacement increments at various monitoring points over time, thereby forming a training sample set. On this basis, using the displacement increment sequences from multiple monitoring points at different times as input, a Back Propagation (BP) neural network improved by the Whale Optimization Algorithm (WOA) is trained to establish a nonlinear mapping model from displacement sequences to material parameters. Subsequently, the material parameters of the soil are inverted based on the actual displacement sequences from multiple monitoring points at different times. This method is applied to the Tongma Embankment for parameter inversion. Verification shows that the displacements calculated using the inverted parameters are in good agreement with the monitoring values, with an average relative error of approximately 2.8%. The results indicate that this method can effectively invert multiple parameters of the soil, thereby providing a reliable basis for precisely understanding the mechanical properties of the soil and the state of the embankment.OPEN ACCESS Received: 17/10/2025 Accepted: 28/11/2025 Published: 21/09/2026</p>

== Document ==
<pdf>Media:Draft_content_461759277-6813-document.pdf</pdf>

@@ Line 2: / Line 2: @@
 == Abstract ==
-<p>Accurately obtaining the mechanical properties of embankment soil is the core component for achieving precise calculation and mastering the operational status of the embankment. To achieve precise identification of multiple key material parameters of the embankment soil, this paper proposes an inversion method that integrates monitoring displacement information with intelligent algorithms. This approach comprehensively utilizes monitoring data from multiple monitoring points and different times to collaboratively invert multiple key parameters of the embankment soil. The study establishes a finite element model of the embankment based on the Cvisc (Burgers-Mohr) viscoelastic-plastic constitutive theory, which describes the creep behavior of soil. Orthogonal experimental design is employed to generate multiple parameter combinations of the soil, and numerical simulations are conducted to obtain displacement increments at various monitoring points over time, thereby forming a training sample set. On this basis, using the displacement increment sequences from multiple monitoring points at different times as input, a Back Propagation (BP) neural network improved by the Whale Optimization Algorithm (WOA) is trained to establish a nonlinear mapping model from displacement sequences to material parameters. Subsequently, the material parameters of the soil are inverted based on the actual displacement sequences from multiple monitoring points at different times. This method is applied to the Tongma Embankment for parameter inversion. Verification shows that the displacements calculated using the inverted parameters are in good agreement with the monitoring values, with an average relative error of approximately 2.8%. The results indicate that this method can effectively invert multiple parameters of the soil, thereby providing a reliable basis for precisely understanding the mechanical properties of the soil and the state of the embankment.OPEN ACCESS Received: 17/10/2025 Accepted: 28/11/2025 Published: 21/09/2026</p>
+<p>Electrocardiogram (ECG) signal processing plays a critical role in the early
+detection and diagnosis of cardiovascular diseases; however, reliable automated interpretation remains challenging due to noise contamination,
+inter-patient variability, and nonstationary signal characteristics. This
 == Document ==
 <pdf>Media:Review_109271889040_3808_153. TSP_RIMNI_82393.pdf</pdf>

← Older revision		Revision as of 10:53, 25 September 2026
Line 7:		Line 7:

	== Document ==		== Document ==
−	<pdf>Media:~~Draft_content_461759277-6813-document~~.pdf</pdf>	+	<pdf>Media:Review_109271889040_3808_153. TSP_RIMNI_82393.pdf</pdf>

← Older revision	Revision as of 10:56, 25 September 2026
(No difference)