基于LSTM的双向城市安全网络使用频域分解数据的条件向量
Han Yong Lee1, Insub Choi2, Byung Kwan Oh3
1Department of Architecture and Architectural Engineering, Yonsei University, Seoul, 03722, Korea.
Scientific reports
|August 25, 2025
概括
这项研究引入了使用LSTM模型预测邻近结构的建筑地震反应的新型网络,即便失去了传感器数据,也提高了结构健康监测 (SHM) 的准确性.
科学领域:
- 土木工程
- 结构工程
- 地震学
背景情况:
- 结构健康监测 (SHM) 系统对于地震安全至关重要,但由于传感器数据丢失而受到阻碍.
- 传感器故障,损坏或通信故障会损害现有SHM系统的可靠性.
研究的目的:
- 开发一个可靠的框架来预测缺乏传感器的环境中的结构反应.
- 在地震事件中提高结构健康监测的准确性和可靠性.
- 通过先进的数据恢复技术提高区域的抗震能力.
主要方法:
- 一个双向的城市安全网络使用长短期记忆 (LSTM) 模型被提出.
- 该框架将时间域位移数据和频域特征整合到条件向量中.
- 在地震负荷下对线性和非线性结构系统进行了评估.
主要成果:
- 与基线模型相比,拟议的方法显著提高了预测准确性,将根平均平方误差 (RMSE) 降低了高达27.13%.
- 条件向量增强了LSTM模型预测动态结构反应的能力.
- 该框架准确地预测了最大响应幅度,并捕获了依赖时间的非线性行为.
结论:
- 双向城市安全网络提供了一个可扩展的解决方案,用于在有限传感器数据的城市环境中恢复结构响应.
- 这种方法通过确保结构完整性的持续监测,提高了区域范围内的抗震能力.
- 这些发现突显了先进的机器学习技术在克服SHM限制方面的潜力.
相关概念视频
State Space to Transfer Function
302
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
302
Linear Approximation in Frequency Domain
131
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
131
Basic Continuous Time Signals
353
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
353
Vector Algebra: Method of Components
15.2K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
In many applications, the magnitudes and directions of...
15.2K
Uniform Depth Channel Flow: Problem Solving
125
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
125
Classification of Signals
886
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
886


