这是用户在 2024-5-11 18:17 为 https://app.immersivetranslate.com/pdf-pro/678ef61e-8637-4a4a-8ab7-f8a925aedfce 保存的双语快照页面,由 沉浸式翻译 提供双语支持。了解如何保存?
2024_05_11_e52253d9f0913c4e1941g

Building structure-borne noise measurements and estimation due to train operations in tunnel

Xuming Li , Yekai Chen , Chao Zou , Hao Wang , Bokai Zheng , Jialiang Chen School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, Guangdong 510641, China
华南理工大学土木工程与交通学院, 广东510641广州
School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou, Guangdong 510006, China
广东工业大学土木与交通工程学院, 广东省广州市, 广东省, 510006
Beijing Oriental Yuhong Waterproof Technology Co., Ltd, Beijing 101111, China
北京东方宇宏防水科技有限公司, 北京101111, 中国
Beiijao Zane Rail Technology (Beijing) Co., Ltd, Beijing 101111, China
Beiijao Zane Rail Technology (Beijing) Co., Ltd, 北京 101111, 中国
Tsinghua University, Beijing 100084, China
清华大学, 北京100084, 中国

H I G H L I G H T S

  • Measurement for studying the generation and dissipation of structure-borne noise.
    用于研究结构噪声的产生和耗散的测量。
  • Obtaining reverberation time in frequencies based on STFT and Schroeder integral.
    基于STFT和Schroeder积分获得频率的混响时间。
  • Deep learning-based approach for structure-borne noise prediction was proposed.
    提出了一种基于深度学习的结构噪声预测方法。
  • Spatial distribution of indoor vibrations and sound field is inversely related.
    室内振动的空间分布与声场成反比。

A R T I C L E I N F O

Editor: Anastasia Paschalidou
编辑:阿纳斯塔西娅·帕斯卡利杜

Keywords: 关键字:

Structure-borne noise 结构噪声
Vibration and noise correlation
振动和噪声相关性
Acoustics-vibration coupling
声学-振动耦合
Measurement 测量
Noise estimation 噪声估算

G R A P H I C A L A B S T R A C T

A B S T R A C T
The perception of structure-borne noise is particularly salient when train passes through the tunnel under the buildings, which has a negative impact on human health. In the process of constructing buildings along metro lines, it is crucial to estimate indoor structure-borne noise levels in order to enhance design and prevent any negative impact on human comfort. This study conducted measurements of structure-borne noise, reverberation time, and train-induced vibrations in Guangzhou, China to investigate the generation, propagation, and dissipation mechanisms of structure-borne noise. An approach based on Short-Time Fourier Transform and Schroeder integral was proposed for obtaining frequency-dependent reverberation time. Additionally, a deep learningbased approach incorporating indoor vibrations, frequency-dependent reverberation time, and room parameters as inputs was proposed based on Genetic Algorithm-Artificial Neural Network. The estimated structure-borne noise levels demonstrated good agreement with measured values, indicating the feasibility of the approach. The finding of this research facilitates a clear comprehension of the generation, distribution, and dissipation mechanisms of indoor structure-borne noise for engineers while also enabling convenient acquisition of indoor structure-borne noise. The estimated noise levels can be effectively utilized during building design processes along metro lines to mitigate adverse impacts on human comfort.
当火车通过建筑物下方的隧道时,对结构噪声的感知尤为突出,这对人体健康产生了负面影响。在地铁沿线建造建筑物的过程中,估计室内结构噪声水平至关重要,以增强设计并防止对人类舒适度产生任何负面影响。本研究以广州市为研究对象,对结构噪声、混响时间和列车引起的振动进行了测量,研究了结构噪声的产生、传播和耗散机制。提出了一种基于短时傅里叶变换和施罗德积分的求频混响时间方法。此外,提出了一种基于遗传算法-人工神经网络的基于室内振动、频率相关混响时间和房间参数作为输入的深度学习方法。估计的结构噪声水平与测量值吻合良好,表明该方法的可行性。本研究的发现有助于工程师清楚地理解室内结构噪声的产生、分布和耗散机制,同时也能够方便地获取室内结构噪声。在地铁沿线的建筑设计过程中,可以有效地利用估计的噪音水平,以减轻对人类舒适度的不利影响。
List of symbol 符号列表 Time discrete variable of STFT
STFT的时间离散变量
Initial value to control the upper limit of the logistic
控制物流上限的初始值
Total number of the data sample of ANN
ANN数据样本总数
regression curve 回归曲线 RT60 Time required for the room to dissipate of sound
房间 消散声音所需的时间
Final value to control the upper limit of logistic regression
控制逻辑回归上限的最终值
energy (s)
curve Floor bay area  地板湾区
B Parameter to control the slope of logistic regression curve
用于控制逻辑回归曲线斜率的参数
Sound absorption area of room
房间 吸音区
c Sound speed in air
空气 中的声速
Surface area of the room
房间 的表面积
D Number of the neurons in the input layer
输入层中的神经元数
Time (s)
Decay function 衰减函数 Maximum time (s) 最长时间
Young's modulus of concrete (MPa)
杨氏混凝土模量 (MPa)
Room reverberant time (s)
房间混响时间
Frequency Transfer function between structure-borne noise and
结构噪声和
Anti-phase resonance frequency of the elastic building
弹性建筑 的反相共振频率
vibration
Output parameter of ANN
ANN的输出参数
Transfer function of structure-borne noise of different
不同结构噪声的传递函数
real value location
predicted value
预测值
Transfer function of building vibration of different location
不同位置建筑振动的传递函数
Average of all the real values
所有实际值的平均值
Vibration velocity (m/s)
振动速度 (m/s)
Nonlinear activation function of ANN
人工神经网络的非线性激活函数
Volume of the room
房间 体积
Room impulse response 房间脉冲响应 Wave speed of concrete
混凝土 的波速
Number of the neurons in the hidden layer
隐藏层中的神经元数量
, fitting parameter of ANN
,ANN的拟合参数
Constant between 0 and 10
常量介于 0 和 10 之间
Floor vibration radiated sound power
地板振动辐射声功率
Building height (m) 建筑高度(米) Input parameter of ANN
ANN的输入参数
Room height (m) 房间高度(米) Parameter to control the center of logistic regression curve
用于控制逻辑回归曲线中心的参数
Slope of the Schroeder integral curve
Schroeder 积分曲线的斜率
Input signal of STFT
STFT的输入信号
A-weighting adjustment at the -octave band center
-倍频程频段中心的 A 加权调整
Two-dimensional function of time and frequency
时间和频率的二维函数
frequency Average sound absorption coefficient
平均吸声系数
Acceleration level ( , re: )
加速度等级 ( , re:
Air density
A-weighting sound pressure level , re:
A-加权声压级 ,回复:
Concrete density
混凝土密度
Sound pressure level , re:
声压级 , re:
Time discrete variable 时间离散变量
Velocity level ( , re: )
速度水平 ( , re:
Angular frequency, equal to
角频率,等于
Velocity level ( , re:
速度水平 ( , re:
Window function 窗口函数
Velocity level (dB, re:
速度水平 (dB, re:
Average radiation ration for the floor bay
地板托架的平均辐射比

1. Introduction 1. 引言

Building structure-borne noise is generated by structural vibrations induced by various sources, such as traffic, metro trains, and construction activities (e.g., pile driving impacts and machine vibrations), which can have detrimental effects on both vibration-sensitive equipment and human health (Venkataraman et al., 2022; Li et al., 2024; Qiu et al., 2024). Ground-borne vibration has a predominant impact on human comfort (Liu et al., 2020a) within buildings and vibration-sensitive equipment (Liu et al., 2023; Avci et al., 2020; Li et al., 2023a; Ma et al., 2024). In terms of human perception, noise is considerably more discernible than vibrations (Li et al., 2022; Sadeghi and Vasheghani, 2021). For instance, in the case of train-induced vibrations, the auditory perception of rumbling caused by metro trains is more pronounced compared to vibrations. However, the structure-borne noise generated by metro trains is almost impossible to eliminate, unlike other types of environmental noise due to the long-term transportation demand (Li et al., 2023a; Peplow et al., 2021). Therefore, it is imperative to investigate the propagation characteristics and estimation of structure-borne noise in rooms for the purpose of meeting the requirements of human health and comfort in building constructions along railways (He et al., 2022; Liu et al., 2020b).
建筑结构噪声是由各种来源引起的结构振动产生的,例如交通、地铁列车和建筑活动(例如,打桩冲击和机器振动),这可能对振动敏感设备和人类健康产生不利影响(Venkataraman 等人,2022 年;Li 等人,2024 年;Qiu 等人,2024 年)。地面振动对建筑物和振动敏感设备内的人体舒适度(Liu et al., 2020a)具有主要影响(Liu et al., 2023;Avci 等人,2020 年;Li 等人,2023a;马 等人,2024 年)。就人类感知而言,噪音比振动更容易辨别(Li et al., 2022;Sadeghi 和 Vasheghani,2021 年)。例如,在列车引起的振动的情况下,与振动相比,地铁列车引起的隆隆声的听觉感知更为明显。然而,由于长期的运输需求,与其他类型的环境噪声不同,地铁列车产生的结构噪声几乎不可能消除(Li et al., 2023a;Peplow 等人,2021 年)。因此,为了满足铁路沿线建筑施工中人类健康和舒适性的要求,必须研究室内结构噪声的传播特性和估计(He et al., 2022;Liu等人,2020b)。
Several researchers have conducted studies on the analysis of structure-borne noise (Tao et al., 2022; Smith, 2011; Thompson, 2008; Nelson, 1987; Kurzweil, 1979). The findings support the development of subsequent prediction methods, which can be categorized into empirical approach (Thompson, 2008; Kurzweil, 1979; Vér and Beranek, 2005; Quagliata et al., 2018; Chinese code of Ecology and Environment, 2018), numerical approach (Sadeghi et al., 2023; Vogiatzis, 2012; Colaço et al., 2017a; Nagy et al., 2006; Fiala et al., 2007), and analytical approach (e Sousa and Gibbs, 2011; Kang et al., 2022; Shen and Oldham, 1982), respectively.
一些研究人员对结构噪声的分析进行了研究(Tao 等人,2022 年;史密斯,2011 年;汤普森,2008 年;Nelson,1987 年;Kurzweil,1979年)。这些发现支持了后续预测方法的发展,这些方法可以分为经验方法(Thompson,2008;Kurzweil,1979 年;Vér 和 Beranek,2005 年;Quagliata等人,2018;中国生态环境规范,2018)、数值方法(Sadeghi et al., 2023;Vogiatzis,2012 年;Colaço等人,2017a;Nagy 等人,2006 年;Fiala 等人,2007 年)和分析方法(e Sousa 和 Gibbs,2011 年;Kang 等人,2022 年;Shen 和 Oldham,1982 年)。
Empirical approach is commonly employed in the preliminary stage of building construction to estimate and assess the sound pressure levels in buildings, which exhibits similar definitions across various standards or research studies. An empirical model was provided by RIVAS D 1.6 to describe the relationship between sound pressure levels and velocity levels (RIVAS Del.1.6, 2012), as expressed:
在建筑施工的初步阶段,通常采用实证方法来估计和评估建筑物的声压级,这在各种标准或研究中都有类似的定义。RIVAS D 1.6提供了一个经验模型来描述声压级和速度级之间的关系(RIVAS Del.1.6,2012),如:
where is sound pressure levels (re: ), is velocity levels (re: ) in -octave band center frequency with a range of . The subscript ' ' and ' ' represent the sound pressure and velocity. The similar formulations have been provided by Chinese code HJ 453-2018 (Chinese code of Ecology and Environment, 2018) and Federal Transit Administration report FTA Guidelines (Quagliata et al., 2018), as expressed in Eq. (2) and Eq. (3), respectively.
其中 是声压级 (Re: ), -倍频程带中心频率中的速度级 (Re:), 范围为 。下标 ' ' 和 ' ' 表示声压和声速。中国代码HJ 453-2018(中国生态与环境法规,2018)和联邦运输管理局报告FTA指南(Quagliata et al., 2018)提供了类似的表述,分别如方程(2)和方程(3)所示。
Where is A-weighting sound pressure levels (re: ), is velocity levels (re: ) in 1/3-octave bands center frequency with a range of is A-weighting adjustment at the octave band center frequency. The above formulas are applicable to the working conditions of small rooms. It is worth noting that the formula of HJ 453-2018 is specifically applicable to the conditions of relatively small rooms. Compared to Eq. (1) and Eq. (3), the structure-borne noise
其中 是 A 加权声压级 (re: ), 是 1/3 倍频程中心频率中的速度级 (re: ),范围是 倍频程中心频率的 A 加权调整。以上公式适用于小房间的工作条件。值得注意的是,HJ 453-2018的公式特别适用于相对较小的房间的条件。与式(1)和式(3)相比,结构噪声

is overestimated by Chinese code HJ 453-2018 in order to ensure that the building design is not adversely affected by noise disturbances. However, this excessively cautious approach led to unnecessary waste of building materials and escalated labor expenses. Besides, an empirical model proposed by Beranek (Vér and Beranek, 2005) was employed by Tao et al. (2022) in a low-rise over-track building to estimate the structure-borne noise through the following formulation:
被中国规范HJ 453-2018高估,以确保建筑设计不受噪音干扰的不利影响。然而,这种过于谨慎的做法导致了不必要的建筑材料浪费和劳动力成本的增加。此外,Tao et al. (2022) 在一栋低层轨道建筑中采用了 Beranek (Vér and Beranek, 2005) 提出的经验模型,通过以下公式估计结构噪声:
where is room reverberant time, is the volume of the room, is the floor vibration radiated sound power, is the average radiation ration for the floor bay, is the velocities, is the air density, is the floor bay area, and is the sound speed in air. The results indicated a significant increase in error at higher frequencies, reaching nearly within the range of . Despite its expedience in determining structure-borne sound pressure levels in rooms, the empirical approach exhibits relatively lower accuracy.
其中 ,是房间的混响时间, 是房间的体积, 是地板振动辐射的声功率, 是地板间隔的平均辐射比, 是速度, 是空气密度, 是地板间隔面积, 是空气中的声速。结果表明,在较高频率下,误差显著增加,几乎 达到 。尽管在确定房间内结构声压级方面是权宜之计,但经验方法的精度相对较低。
Numerical approach is the commonly employed method to establish the room sound field, with the boundary element method (BEM) (Bernhard et al., 1987; Wrobel, 2002; Wu, 2002) being recognized as the most accurate technique for estimating noise from vibrations. BEM can be applied to structures of arbitrary shapes and easily integrated with finite element method (FEM) models of buildings (Nagy et al., 2006). Jean and Guigou-Carter (2021) using the 2.5 dimensions FEM/BEM model to calculate the ground-borne vibration and conduct parametric studies. Fiala et al. (2007) proposed a fast algorithm for acoustic computations based on the spectral finite element method; the results show that the dominant frequency bands of the train-induced structure-borne noise were basically determined by the first acoustic resonance of the room. A FEM model was established by Sadeghi and Vasheghani (2021) to investigate the impact of building structural and acoustic parameters on structure-borne noise.
数值方法是建立房间声场的常用方法,采用边界元法(BEM)(Bernhard等人,1987;Wrobel,2002 年;Wu,2002)被公认为估计振动噪声的最准确技术。BEM可以应用于任意形状的结构,并易于与建筑物的有限元法(FEM)模型集成(Nagy等人,2006)。Jean 和 Guigou-Carter (2021) 使用 2.5 维 FEM/BEM 模型计算地面振动并进行参数化研究。Fiala等人(2007)提出了一种基于频谱有限元方法的声学计算快速算法;结果表明:列车诱发结构噪声的主频带基本由房间的第一次声共振决定;Sadeghi 和 Vasheghani (2021) 建立了一个 FEM 模型,以研究建筑结构和声学参数对结构噪声的影响。
However, despite the simplicity and directness of the traditional numerical method, it lacks attractiveness in terms of practical estimation. The large size of the matrices for numerical calculation makes great computational costs. Improved numerical approaches have been developed by researchers in the field. Nagy et al. (2006) introduced modifications to the numerical calculations using the Rayleigh integralbased method, making it more suitable for practical applications without the need for constructing and inverting large matrices. Colaço et al. (2017b) enhanced calculation efficiency by combining a 2.5dimensional FEM with the method of fundamental solutions (MFS) model for structure-borne noise analysis.
然而,尽管传统数值方法简单直接,但在实际估计方面缺乏吸引力。用于数值计算的矩阵尺寸很大,计算成本很高。该领域的研究人员已经开发了改进的数值方法。Nagy et al. (2006) 使用基于瑞利积分的方法对数值计算进行了修改,使其更适合实际应用,而无需构建和反演大型矩阵。Colaço et al. (2017b) 通过将 2.5 维 FEM 与基本解方法 (MFS) 模型相结合进行结构噪声分析,提高了计算效率。

Analytical approach commonly involves the calculation of acoustic radiation responses for both slabs and beams. Shen and Oldham (1982) computed the directivity patterns of typical building elements based on the Galerkin method. Luo et al. (2021) proposed a computational technique based on the cellular automata (CA) method for analyzing the sound radiation characteristics of arbitrary shape structures. A theoretical model was proposed by Yang et al. (2021), which can be employed for estimating the sound radiation from a semi-infinite unbaffled long enclosure with the ground. The fundamental aspect of acoustic-vibration coupling lies in fluid-structure interaction. Due to the complexity of air fluid propagation in buildings, the analytical method is not commonly employed in practical application engineering.
分析方法通常涉及计算板和梁的声辐射响应。Shen和Oldham(1982)基于Galerkin方法计算了典型建筑构件的方向性模式。Luo等人(2021)提出了一种基于元胞自动机(CA)方法的计算技术,用于分析任意形状结构的声音辐射特性。Yang et al. (2021) 提出了一个理论模型,该模型可用于估计来自与地面的半无限无挡板长外壳的声音辐射。声-振动耦合的基本方面在于流固耦合。由于空气流体在建筑物中传播的复杂性,该分析方法在实际应用工程中并不常用。
The apparent contradiction between the accuracy and efficiency of structure-borne noise estimation poses a challenge in achieving an optimal balance. Alternatively, employing deep-learning based approaches may be considered as a potential method for reconciling the conflict (Li et al., 2023b). Redonnet et al. (2024) employed deep learning to predict airfoil self-noise, showcasing its superiority over a widely-used semi-empirical prediction tool. Zhang et al. (2021) predicted the traffic noise using the recurrent neural network, which can help the regulation and policy makers to make early decisions. Mostafavi and Cha (2023) proposed a novel high-performance deep learningbased feedforward active noise controller to attenuate constructionrelated noise. Liang et al. (2022) proposed a deep learning-based approach for the identification of traffic noise sources and their occurrence at different time intervals. The measurement and prediction results demonstrated that lower floors of buildings were more susceptible to structure-borne noise induced by underground railways, while higher floors experienced a greater level of airborne noise caused by road traffic. These practical applications demonstrate the efficacy of noise prediction using neural networks, thereby providing valuable insights for predicting structure-borne noise caused by train-induced vibrations. Consequently, this study utilizes on-site measurements and employs a deep-learning based approach to accurately estimate the structure-borne noise generated by building vibrations.
结构噪声估计的准确性和效率之间存在明显的矛盾,这给实现最佳平衡带来了挑战。或者,采用基于深度学习的方法可以被认为是调和冲突的潜在方法(Li et al., 2023b)。Redonnet et al. (2024) 采用深度学习来预测翼型自噪声,展示了其优于广泛使用的半经验预测工具。Zhang et al. (2021) 使用递归神经网络预测了交通噪声,这可以帮助监管和政策制定者及早做出决策。Mostafavi 和 Cha (2023) 提出了一种新型的基于深度学习的高性能前馈有源噪声控制器,用于衰减与建筑相关的噪声。Liang et al. (2022) 提出了一种基于深度学习的方法,用于识别交通噪声源及其在不同时间间隔的发生。测量和预测结果表明,建筑物的较低楼层更容易受到地下铁路引起的结构噪声的影响,而较高楼层的建筑物则更容易受到道路交通引起的空气噪声的影响。这些实际应用证明了使用神经网络进行噪声预测的有效性,从而为预测由列车引起的振动引起的结构噪声提供了宝贵的见解。因此,本研究利用现场测量并采用基于深度学习的方法来准确估计建筑物振动产生的结构噪声。
Objectives of this research aim: (1) to investigate the characteristics and interrelationships of building vibrations and structure-borne noise induced by train-induced vibrations; (2) to estimate the structure-borne noise within building rooms while acquiring building vibrations, using the approach that is more accurate than empirical formulations. The approach can be applied when the spatial parameters of the room (including area, height, and location in the room), acoustical parameter of the room (frequency-dependent reverberation time), and excitation parameter (velocity levels) are available. Accurate estimation of noise response enables a more comprehensive assessment of effective measures for noise control.
本研究目的:(1)研究列车振动引起的建筑振动和结构噪声的特征和相互关系;(2)使用比经验公式更准确的方法,在获取建筑振动的同时估计建筑室内的结构噪声。当房间的空间参数(包括房间的面积、高度和位置)、房间的声学参数(与频率相关的混响时间)和激励参数(速度水平)可用时,可以应用该方法。准确估计噪声响应,可以更全面地评估噪声控制的有效措施。
(a) Floor (a) 地板
(c) Parking garage at basement
(c) 地下室停车库
(b) Southeastern view (b) 东南视图
(d) Northeastern view (d) 东北面景观
Fig. 1. Schematic representation of measurement site scene.
图 1.测量现场场景示意图。
Fig. 2. Measurement setup.
图 2.测量设置。
The technical route of this research involves conducting on-site measurements of the building vibrations and structure-borne noise at various locations within the building. The application of the transfer functions between building vibrations and structure-borne noise was utilized to investigate vibration-acoustic transfer issues related to traininduced vibrations. Additionally, the room reverberation time in the frequency domain was obtained using a method based on the Short-Time Fourier Transform (STFT) (Portnoff, 1980) combined with Schroeder integral (Schroeder, 1965), which is employed to investigate the indoor sound field mechanism of structure-borne noise and serves as one of the inputs for the deep-learning based model. Subsequently, the Genetic Algorithm (Holland, 1992) - Artificial Neural Network (Yegnanarayana, 2009) (GA-ANN) approach was employed to estimate structure-borne noise based on the acquired building vibrations.
这项研究的技术路线包括对建筑物内不同位置的建筑物振动和结构噪声进行现场测量。利用建筑振动与结构噪声之间的传递函数来研究与列车致振动相关的振动-声学传递问题。此外,使用基于短时傅里叶变换(STFT)(Portnoff,1980)和Schroeder积分(Schroeder,1965)的方法获得了频域中的房间混响时间,该方法用于研究结构噪声的室内声场机制,并作为基于深度学习的模型的输入之一。随后,采用遗传算法(Holland,1992)-人工神经网络(Yegnanarayana,2009)(GA-ANN)方法,根据获得的建筑物振动来估计结构噪声。
The contributions of this study are the utilization of STFT and Schroeder integral for determining reverberation time in the frequency domain, followed by employing a deep learning-based approach to estimate structure-borne noise. This methodology yields more precise estimations within a shorter timeframe compared to conventional empirical formulas.
本研究的贡献是利用STFT和Schroeder积分来确定频域中的混响时间,然后采用基于深度学习的方法来估计结构噪声。与传统的经验公式相比,这种方法可以在更短的时间内产生更精确的估计。

2. Measurement of structure-borne noise
2. 结构噪声的测量

2.1. Measurement site and program
2.1. 测量站点和程序

A teaching building in Guangzhou, China, was selected as the measurement site for assessing building vibrations and structure-borne noise, where the discernible rumble of the metro trains could be perceived. The building is located directly above two tunnels through which the metro train operates at a constant speed of . The photos and schematic representation of the measurement site scene are depicted in Fig. 1, where the main test room is highlighted by a yellow dotted line frame.
中国广州的一栋教学楼被选为评估建筑振动和结构噪声的测量地点,在那里可以感知地铁列车的隆隆声。该建筑位于两条隧道的正上方,地铁列车通过这些隧道以恒定的速度 运行。测量现场场景的照片和示意图如图 1 所示,其中主测试室由黄色虚线框突出显示。
The measurements were conducted during clear midnights and early morning to mitigate the impact of adverse weather conditions and human-induced noise, ensuring minimal interference from rain, strong wind, and human activities. During the measurements, all doors and windows were kept closed to eliminate potential external factors that could introduce interference.
测量是在晴朗的午夜和清晨进行的,以减轻恶劣天气条件和人为噪音的影响,确保将雨水、强风和人类活动的干扰降至最低。在测量过程中,所有门窗都保持关闭状态,以消除可能引入干扰的潜在外部因素。
As shown in Fig. 2, to investigate the structure-borne noise characteristics induced by train operations, microphones were mounted at the floor center (indicated by a yellow triangle) from the 1 st to the 5 th
如图2所示,为了研究列车运行引起的结构噪声特性,从1号到5号,在地板中心(用黄色三角形表示)安装麦克风
(a) Microphone (a) 麦克风
(b) Instruments setting (b) 仪器设置
(c) Accelerometer (c) 加速度计
Fig. 3. Instrumentations.
图 3.仪器。

Time (s) 时间
Frequency ) 频率
Frequency ( ) 频率 (
Frequency  频率
Power/Frequency (
功率/频率 (

Time  时间
Frequency  频率
Ftequency ( ) 英尺 (
Frequency  频率
Time ( ) 时间 (
Frequency (  频率 (
Fig. 4. Time history and spectrogram of a typical train pass-by event.
图 4.典型火车通过事件的时间历史和频谱图。
floors, as well as in the basement. The microphones were also mounted adjacent to the column (green triangle) and wall (blue triangle) for additional measurements conducted on the 1st floor and basement. Correspondingly, the accelerometers were placed beneath the microphones at their respective locations on the floors to obtain the vibrations. The basement covers a floor space equal to that of the entire building, which is around . The total area of the rooms on the 1st through 4th floors amounts to , whereas those on the 5 th floor occupy an area of .
地板,以及地下室。麦克风还安装在柱子(绿色三角形)和墙壁(蓝色三角形)附近,以便在一楼和地下室进行额外测量。相应地,加速度计被放置在麦克风下方的地板上的相应位置,以获得振动。地下室占地面积相当于整栋建筑的建筑面积,大约 是。1 楼至 4 楼的房间总面积为 ,而 5 楼的房间面积为

2.2. Instrumentations and signal processing
2.2. 仪器仪表和信号处理

The instrumentation employed for measurement is illustrated in Fig. 3, including the Rion UC-59 microphone (Fig. 3(a)) and the JM3873
用于测量的仪器如图 3 所示,包括 Rion UC-59 麦克风(图 3(a))和 JM3873
(a) Comparison of different floors
(a) 不同楼层的比较
(c) Comparison of different locations
(c) 不同地点的比较
(b) Comparison of different room areas
(b) 不同房间面积的比较
(d) Comparison of different locations
(d) 不同地点的比较
floor
地板
(Basement) (地下室)
Fig. 5. Measured velocity levels at different locations.
图 5.测量不同位置的速度水平。
wireless data acquisition system (Fig. 3(b)). As shown in Fig. 3(b), the microphone (blue rectangular) is connected with a tripod adapter to ensure that the instrument is at a height of above the floor during measurements. The accelerometer (yellow rectangular) was mounted on the floor under the microphone at the corresponding location. Both the microphones and accelerometers have time synchronizers and storage cards. All instruments were calibrated based on the laptop's time prior to measurement initiation, ensuring simultaneous data recording. The sampling frequency was set to , which provided spectral information up to the Nyquist frequency of . The dominant frequency range requirement mentioned in HJ 453-2018 (Chinese code of Ecology and Environment, 2018) for structure-borne noise analysis is . Thus, the setting of sampling frequency was enough for the measurements and analysis.
无线数据采集系统(图3(b))。如图 3(b) 所示,麦克风(蓝色矩形)与三脚架适配器连接,以确保仪器在测量过程中处于 高于地面的高度。加速度计(黄色矩形)安装在相应位置麦克风下方的地板上。麦克风和加速度计都有时间同步器和存储卡。所有仪器在测量开始前都根据笔记本电脑的时间进行校准,确保同时记录数据。采样频率设置为 ,这提供了高达奈奎斯特频率的 频谱信息。HJ 453-2018(中国生态环境规范,2018)中提到的结构噪声分析的主要频率范围要求是 。因此,采样频率的设置足以进行测量和分析。
Sound pressure and acceleration time signals were downloaded from the wireless unit, which was read by a MATLAB script to compute spectrogram and frequency spectra. The signals of a typical train pass-by are shown in Fig. 4, different colors in the figure correspond to different locations. The spectrograms reveal that the dominant frequencies of both velocity and sound pressure fall within the range of . The frequency spectra are analyzed in terms of the envelope, where the figure displays the minimum and maximum values obtained for each frequency. The solid lines represent the average values. In subsequent analysis, the average value is utilized to facilitate a clear comparison.
从无线单元下载声压和加速度时间信号,由MATLAB脚本读取以计算频谱图和频谱。典型列车经过的信号如图4所示,图中不同的颜色对应不同的位置。频谱图显示,速度和声压的主频率都在 的范围内。根据包络分析频谱,其中图显示了每个频率获得的最小值和最大值。实线表示平均值。在随后的分析中,使用平均值来促进清晰的比较。

2.3. Building vertical vibration response due to train operation
2.3. 列车运行引起的建筑垂直振动响应

The building vibration spectra of the average of all recorded train pass-by events are depicted in Fig. 5, with a reference velocity of , which is obtained from the Chinese code HJ 453-2018 (Chinese code of Ecology and Environment, 2018). The ambient vibration predominantly contributes to the vibration energy within the frequency range of , possibly due to operational activities in the pumping house and electricity distribution room located within the testing building during measurements. However, for train-induced vibrations, which are of interest, frequencies ranging from 30 to exhibit relatively lower magnitudes of ambient vibration that can be disregarded during analysis. As shown in Fig. 5 (a), the trend of vibration amplitude reduction of building floor centers with increasing floor levels is observed as the frequency increases. The basement exhibits significantly lower vibrations compared to the superstructure at low frequencies, while the low-frequency vibrations from the 1st to 4th floors exhibit a tendency towards consistency. The attenuation of building propagation is less pronounced for low-frequency vibrations compared to high-frequency vibrations. The high-frequency vibration experiences a more significant degree of attenuation as a result of its shorter wavelength. The vibrations of load-bearing structures are consistent on relatively higher floors, which is concluded from the authors' previous experimental and analytical studies (Zou et al., 2018; Zou et al., 2022; Zou et al., 2017). Therefore, the vibration energy received on the test rooms on the fourth and fifth floors is deemed to exhibit consistency, and the comparison of velocity levels of different room areas is shown in Fig. 5 (b). However, the smaller size floors have a smaller response due to that the floor of smaller size has greater dynamic stiffness with the same thickness. The vibrations of different building components are illustrated in Fig. 5 (c) and (d), with the floor exhibiting the largest vibration while the structural column has the smallest vibration. The structural column serves as the axial propagating subject of vertical
所有记录的列车通过事件的平均值的建筑振动谱如图5所示,参考速度为