Acoustic amenity analysis for high-rise building along urban expressway: Modeling traffic noise vertical propagation using neural networks

A method for theoretically calculating the coherence between sound pressure inside a rectangular room in a high-rise building and that outside the open window of the room is proposed. The traffic noise transmitted into a room is generally dominated by low-frequency components, to which active noise control (ANC) technology may find an application. However, good coherence between reference and error signals is essential for effective noise reduction and should be checked first. Based on traffic noise prediction methods, wave theory, and mode coupling theory, the results of this paper enable one to determine the potentials and limitations of ANC used to reduce such a transmission. Experimental coherence results are shown for two similar, empty rectangular rooms located on the 17th and 30th floors of a 34 floor high-rise building. The calculated results with the proposed method are generally in good agreement with the experimental results and demonstrate the usefulness of the method for predicting the coherence.

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Study on the Applications of Neural Networks for Processing Deformation Monitoring Data

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.501-504.2149 ◽

2014 ◽

Vol 501-504 ◽

pp. 2149-2153 ◽

Cited By ~ 2

Author(s):

Cai Yun Gao ◽

Xi Min Cui ◽

Xue Qian Hong

Keyword(s):

Neural Network ◽

Neural Networks ◽

Prediction Models ◽

Deformation Monitoring ◽

Monitoring Data ◽

Nonlinear Fitting ◽

High Rise ◽

High Rise Building ◽

Good Ability ◽

Neural Network Prediction

Accurately estimating the deformation of high-rise building is a very important work for surveyors, however it is very difficult to get an accurate and reliable predictor. In this paper, artificial neural network has been applied here because of its good ability of nonlinear fitting. On the basis of the high-rise building monitoring data, three prediction models including the BP, RBF and GRNN neural network prediction models were established, the comparative analysis for the prediction accuracy of the three models was obtained. The results show that neural network is capable for prediction, and GRNN possess higher capability in prediction and better adaptability in comparing with other two neural networks.

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Field measurement of reverberation time and average absorption of one high-rise building room by road traffic noise penetrating facade

INTER-NOISE and NOISE-CON Congress and Conference Proceedings ◽

10.3397/in-2021-2248 ◽

2021 ◽

Vol 263 (4) ◽

pp. 2875-2886

Author(s):

Jiping Zhang ◽

Zheming Wang ◽

Heng Ma ◽

Weike Wang

Keyword(s):

Field Measurement ◽

Road Traffic ◽

Traffic Noise ◽

Field Method ◽

Room Acoustics ◽

Road Traffic Noise ◽

Reverberation Time ◽

Field Weakening ◽

High Rise ◽

High Rise Building

The facade insulation (FI) is one choice for Road traffic noise (RTN) at a high-rise accommodation building nearby a motor road. The weakness of FI is from window, so the window insulation (WI) is also a prioritized descriptor. ISO16283-3 states a field method to measure FI using RTN. However, in room acoustics, besides FI, reverberation time (RBT) or indoor average sound absorption (IASB) are another two un-ignorable descriptors. When the value of IASB is small, the indoor noise is not only contributed from penetrating façade RTN, but also supplemented by the residual sound from high reverberation field, weakening FI. As a parallel to ISO16283-3, this paper suggests an engineering method to measure RBT and IASB of one high-rise building room close to a motor road by penetrating façade RTN. It can supply a convenient tool for the field measurement of RBT and IASB with RTN. At the end, we made a field measurement of RBT, IASB, and WI at a hotel room nearby a viaduct in Hangzhou of China, assistant to adjust RBT or IASB and WI so as to improve the sound quality of the hotel. Further, the method can extend to the lines of rail, aviation, and shipping.

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Traffic noise reduction at balconies on a high-rise building façade

The Journal of the Acoustical Society of America ◽

10.1121/1.3682052 ◽

2012 ◽

Vol 131 (3) ◽

pp. 2110-2117 ◽

Cited By ~ 16

Author(s):

Takashi Ishizuka ◽

Kyoji Fujiwara

Keyword(s):

Noise Reduction ◽

Traffic Noise ◽

High Rise ◽

High Rise Building ◽

Building Facade

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Measurements and prediction of road traffic noise along high-rise building façades in Athens

Noise Mapping ◽

10.1515/noise-2020-0001 ◽

2020 ◽

Vol 7 (1) ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Alexandra Sotiropoulou ◽

Ioannis Karagiannis ◽

Emmanouil Vougioukas ◽

Athanassios Ballis ◽

Aspasia Bouki

Keyword(s):

Vertical Distribution ◽

Road Traffic ◽

Traffic Noise ◽

Well Being ◽

Urban Morphology ◽

Design Stage ◽

Road Traffic Noise ◽

High Rise ◽

High Rise Building ◽

Building Facades

AbstractConstant exposure to traffic noise pollution can have significant impact on human health and well being. Occupants of high-rise buildings along noisy traffic arteries are severely affected. In an attempt to contribute to noise protection design of prospective high-rise buildings, traffic noise measurements and prediction using the CRTN (calculation of road traffic noise) model, were made along the façade of a high-rise building in central Athens. The aim was to test the accuracy of this model in predicting the vertical distribution (mapping) of traffic noise along such building façades, under the local urban characteristics of the Mediterranean capital. The predicted and measured noise levels were found to be highly coherent with each other, and their vertical distribution pattern, by and large, confirmed findings from earlier studies. Nevertheless, the predicted values had a tendency of underestimation, with a mean difference −2.2 dB(A) with reference to measured values. It is considered that this underestimation is associated mainly with a newly proposed feature of urban morphology, namely (local) geo-morphology. By and large, it can be inferred that the CRTN model is a useful tool, suitable for the prediction of traffic noise along high-rise building façades during their planning and design stage. The results represent a further step towards more general application of this model, as well as a contribution to the use of this model considering a wider number of urban features.

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