Wyniki wyszukiwania - BazTech

1

Wykorzystanie sztucznej inteligencji do predykcji parametrów ciągłych oznaczanych w badaniach bezpieczeństwa pożarowego systemów ociepleń metodą SBI

Kopciał Paweł, Malinowski Paweł, Niziurska Małgorzata

Materiały Budowlane

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2024

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nr 7

63--66

2

Prediction of microstructural evolution in fly ash-modified cementitious system: A computational study

Yadeta Andualem E., Goyal Pradeep K., Sarkar Raju

Materials Science Poland

|

2023

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Vol. 41, No. 4

68--77

EN

The intricate interaction between supplementary cementitious materials (SCMs) and cementitious systems profoundly influences the performance and sustainability of cementitious composites. This study explores the microstructural evolution of fly ash (FA)-modified cement paste by employing a three-dimensional cement hydration and microstructure development (CEMHYD3D) modeling package. Through comprehensive simulations, the influence of varying FA content on hydration phase evolution and pore structure within the cementitious system is revealed. As the proportion of FA within the cementitious mixtures increases, there is a substantial enhancement in the rate of hydration. Notably, the incorporation of FA introduces a significant augmentation in the hydration rate, a phenomenon with potential implications for the long-term performance of FA-modified cementitious materials. The prediction results also highlight that increasing FA substitution in cement leads to finer and more interconnected pore networks due to the pozzolanic reaction. These perceptions hold significant implications for optimizing cementitious mixes and advancing sustainable construction practices. The model-predicted results have been validated with experiments, and they are successful in predicting the microstructural evolution in FA-modified cement paste. In summary, the prediction model bridges the theoretical and practical implementation gaps by providing a thorough understanding of the microstructural evolution of FA-modified cement paste. Furthermore, it provides invaluable guidance for tailoring FA-blended cement compositions, thus promoting their enhanced performance and sustainability in the realm of cementitious materials.

3

Temperature and Humidity Data Evaluation of Tight Sportswear During Motion Based on Intelligent Modeling

Cheng Pengpeng, Wang Jianping, Zeng Xianyi, Bruniaux Pascal, Chen Daoling

Fibres & Textiles in Eastern Europe

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2023

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Vol. 31, no. 3

1--8

EN

A neural network structure of Long Short Term Memory (LSTM) is proposed which could be used to predict the temperaturę and humidity of other key parts from the temperature and humidity data of some parts of the human body when wearing tight sportswear, so as to realize the temperature and humidity data prediction of all key points of the human body. The temperaturę and humidity of different people wearing tights were collected by DHT sensors. The experimental results show that the LSTM neural network structure proposed has higher prediction accuracy than other algorithms, and the model evaluates the feasibility of temperature and humidity data of tights in a state of motion, which facilitates the study of dynamic thermal and humid comfort and reduces the time cost of analyzing the temperature and humidity distribution and changing the law during human movement. It will effectively promote the study of temperature and humidity changes when people wear sports tights, provide theoretical reference for the study of human skin temperature in the field of sports medicine, and provide practical guidance for the application of human skin temperature changes in sports clothing production, diagnosis and prevention of sports injuries.

4

Medium- and long-term prediction of polar motion using weighted least squares extrapolation and vector autoregressive modeling

Lei Yu, Zhao Danning, Guo Min

Artificial Satellites : Journal of Planetary Geodesy

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2023

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Vol. 58, No. 2

42--55

EN

This article presents the application of weighted least squares (WLS) extrapolation and vector autoregressive (VAR) modeling in polar motion prediction. A piecewise weighting function is developed for the least squares (LS) adjustment in consideration of the effect of intervals between observation and prediction epochs on WLS extrapolation. Furthermore, the VAR technique is used to simultaneously model and predict the residuals of xp, yp pole coordinates for WLS misfit. The simultaneous predictions of xp, yp pole coordinates are subsequently computed by the combination of WLS extrapolation of harmonic models for the linear trend, Chandler and annual wobbles, and VAR stochastic prediction of the residuals (WLS+VAR). The 365-day-ahead xp, yp predictions are compared with those generated by LS extrapolation+univariate AR prediction and LS extrapolation+VAR modeling. It is shown that the xp, yp predictions based on WLS+VAR taking into consideration both the interval effect and correlation between xp and yp outperform those generated by two others. The accuracies of the xp predictions are 13.97 mas, 18.47 mas, and 20.52 mas, respectively for the 150-, 270-, and 365-day horizon in terms of the mean absolute error statistics, 36%, 24.8%, and 33.5% higher than LS+AR, respectively. For the yp predictions, the 150-, 270-, and 365-day accuracies are 15.41 mas, 21.17 mas, and 21.82 mas respectively, 27.4%, 11.9%, and 21.8% higher than LS+AR respectively. Moreover, the absolute differences of the WLS+VAR predictions and observations are smaller than the differences from LS+VAR and LS+AR, which is practically important to practical and scientific users, although the improvement in accuracies is no more than 10% relative to LS+VAR. The further comparison with the predictions submitted to the 1st Earth Orientation Parameters Prediction Comparison Campaign (1st EOP PCC) shows that while the accuracy of the predictions within 30 days is comparable with that by the most accurate prediction techniques including neural networks and LS+AR participating in the campaign for xp, yp pole coordinates, the accuracy of the predictions up to 365 days into the future are better than accuracies by the other techniques except best LS+AR used in the EOP PCC. It is therefore concluded that the medium- and long-term prediction accuracy of polar motion can be improved by modeling xp, yp pole coordinates together.

5

Prediction model of the pandemic spreading based on weibull distribution

Guľáš Ľuboš, Talian Matej, Szabo Stanislav, Semrádová Beáta

Management Systems in Production Engineering

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2022

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nr 2 (30)

179--186

EN

Pandemics have the potential to cause immense disruption of our everyday activities and has impact on the communities and societies mainly through the restrictions applied to the business activities, services, manufacturing, but also education, transportation etc. Therefore, it is important to create suitable prediction models to establish convenient methods for the planning of the operations and processes to cope with the difficulty. In this paper, the prediction model for the spread of the viral disease in term of the estimated maximal weekly confirmed cases and weekly deaths using the Weibull distribution as a theoretical model for statistical data processing is presented. The theoretical prediction model was applied and confirmed on the data available for the whole world and compared to the situation in Europe and Slovakia for the pandemic waves and can be used for the more precise prediction of the pandemic situation and to enhance planning of the activities and processes regarding to the restrictions applied during the worsening pandemic situation.

6

Method for assessing the impact of rainfall depth on the stormwater volume in a sanitary sewage network

Kozłowski Edward, Kowalski Dariusz, Kowalska Beata, Mazurkiewicz Dariusz

Archives of Civil and Mechanical Engineering

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2022

|

Vol. 22, no. 1

art. no. e8, 2022

EN

Sanitary sewage network is relatively rarely considered as the cause of urban floods. Its hydraulic overload can result not only in flooding, but also sanitary contamination of subcatchments. Stormwater is the main reason for this overload. In contrast to the stormwater or combined sewer system, these waters infiltrate into the network in an uncontrolled way, through ventilation holes of covers or structural faults and lack of tightness of manholes. Part of stormwater infiltrates into the soil, where it leaks into pipelines. This greatly hinders assessing the quantity of stormwater influent into the sanitary sewer system. Standard methods of finding correlation between rainfall and the intensity of stormwater flow are ineffective. This is confirmed, i.a. by the studies performed in an existing network, presented in this paper. Only when residuals analysis was performed using the ARIMA and ARIMAX methods, the authors were able to develop a mathematical model enabling to assess the influence of rainfall depth on the stormwater effluent from the sewage network. Owing to the possibility of using the rainfall depth forecasts, the developed mathematical model enables to prepare the local water and sewerage companies for the occurrence of urban floods as well as hydraulic overload of wastewater treatment plants.

7

Analiza zmian wybranych wskaźników jakości wody w systemie wodociągowym

Dobrowolska Agnieszka, Kowalska Beata

Gaz, Woda i Technika Sanitarna

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2021

|

Nr 5

7--11

PL

Analiza zmian wskaźników jakości wody umożliwia ocenę zagrożenia wystąpienia pogorszenia się jej jakości u odbiorców, pozwala na podjęcie działań naprawczych w sytuacji przekroczenia dopuszczalnych wartości tych wskaźników, a także umożliwia opracowanie modeli prognostycznych służących przewidywaniu zmian wybranych wskaźników jakości, na podstawie zgromadzonych w dłuższym okresie danych pomiarowych. W pracy przeprowadzono analizę zmian wybranych wskaźników jakości wody w istniejącym systemie dystrybucji wody w wybranych punktach oraz określano związki między nimi. Okres badań trwał 8 miesięcy, próbki pobierane były 4 razy w miesiącu. Określono korelacje między wszystkimi parami wskaźników jakości wody, przedstawiono modele predykcji dla przyjętych założeń. Obliczenia wykonano wykorzystując program STATISTICA 13.1 firmy SoftStart Polska.

EN

The analysis of changes in water quality parameters enables to assess the risk related to deterioration of the quality of water supplied to consumers. It also allows taking corrective actions in the case of exceeding the permissible values of these parameters and developing prognostic models to predict changes of selected quality parameters, based on the measurement data collected over a longer period of time. This paper presents an analysis of changes in water quality parameters in the existing water distribution system at the selected points and the relationships between these parameters. The research period lasted 8 months, whereas the samples were collected four times a month. The correlations between all pairs of water quality parameters were determined, and prediction models for the adopted assumptions were presented. The calculations were performed using the STATISTICA 13.1 software by SoftStart Polska.

8

Blood glucose prediction model for type 1 diabetes based on artificial neural network with time-domain features

Alfian Ganjar, Syafrudin Muhammad, Anshari Muhammad, Benes Filip, Atmaji Fransiskus Tatas Dwi, Fahrurrozi Imam, Hidayatullah Ahmad Fathan, Rhee Jongtae

Biocybernetics and Biomedical Engineering

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2020

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Vol. 40, no. 4

1586--1599

EN

Predicting future blood glucose (BG) levels for diabetic patients will help them avoid potentially critical health issues. We demonstrate the use of machine learning models to predict future blood glucose levels given a history of blood glucose values as the single input parameter. We propose an Artificial Neural Network (ANN) model with time-domain attributes to predict blood glucose levels 15, 30, 45 and 60 min in the future. Initially, the model's features are selected based on the previous 30 min of BG measurements before a trained model is generated for each patient. These features are combined with time-domain attributes to give additional inputs to the proposed ANN. The prediction model was tested on 12 patients with Type 1 diabetes (T1D) and the results were compared with other data-driven models including the Support Vector Regression (SVR), K-Nearest Neighbor (KNN), C4.5 Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost) and eXtreme Gradient Boosting (XGBoost) models. Our results show that the proposed BG prediction model that is based on an ANN outperformed all other models with an average Root Mean Square Error (RMSE) of 2.82, 6.31, 10.65 and 15.33 mg/dL for Prediction Horizons (PHs) of 15, 30, 45 and 60 min, respectively. Our testing showed that combining time-domain attributes into the input data resulted in enhanced performance of majority of prediction models. The implementation of proposed prediction model allows patients to obtain future blood glucose levels, so that the preventive alerts can be generated before critical hypoglycemic/ hyperglycemic events occur.

9

Future Graduate Salaries Prediction Model Based On Recurrent Neural Network

Siłka Jakub, Wieczorek Michał, Woźniak Marcin

Annals of Computer Science and Information Systems

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2020

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Vol. 21

427--430

EN

Prediction models are widely applied in several fields. In this study we present a discussion on using Recurrent Neural Network as predictor for salaries of future graduates. The model is based on feature analysis which leads to input values of the predictor. We have analyzed several compositions and ideas. As a result we have selected Recurrent Neural Network to be the most accurate. Presented results confirm this selection and show high precision.

10

Fusion of clinical data: A case study to predict the type of treatment of bone fractures

Haq Anam, Wilk Szymon, Abelló Alberto

International Journal of Applied Mathematics and Computer Science

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2019

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Vol. 29, no. 1

51--67

EN

A prominent characteristic of clinical data is their heterogeneity—such data include structured examination records and laboratory results, unstructured clinical notes, raw and tagged images, and genomic data. This heterogeneity poses a formidable challenge while constructing diagnostic and therapeutic decision models that are currently based on single modalities and are not able to use data in different formats and structures. This limitation may be addressed using data fusion methods. In this paper, we describe a case study where we aimed at developing data fusion models that resulted in various therapeutic decision models for predicting the type of treatment (surgical vs. non-surgical) for patients with bone fractures. We considered six different approaches to integrate clinical data: one fusion model based on combination of data (COD) and five models based on combination of interpretation (COI). Experimental results showed that the decision model constructed following COI fusion models is more accurate than decision models employing COD. Moreover, statistical analysis using the one-way ANOVA test revealed that there were two groups of constructed decision models, each containing the set of three different models. The results highlighted that the behavior of models within a group can be similar, although it may vary between different groups.

11

Logit business failure prediction in V4 countries

Durica Marek, Valaskova Katarina, Janoskova Katarina

Engineering Management in Production and Services

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2019

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Vol. 11, no. 4

54--64

EN

The paper presents the creation of the model that predicts the business failure of companies operating in V4 countries. Based on logistic regression analysis, significant predictors are identified to forecast potential business failure one year in advance. The research is based on the data set of financial indicators of more than 173 000 companies operating in V4 countries for the years 2016 and 2017. A stepwise binary logistic regression approach was used to create a prediction model. Using a classification table and ROC curve, the prediction ability of the final model was analysed. The main result is a model for business failure prediction of companies operating under the economic conditions of V4 countries. Statistically significant financial parameters were identified that reflect the impending failure situation. The developed model achieves a high prediction ability of more than 88%. The research confirms the applicability of the logistic regression approach in business failure prediction. The high predictive ability of the created model is comparable to models created by especially sophisticated artificial intelligence approaches. The created model can be applied in the economies of V4 countries for business failure prediction one year in advance, which is important for companies as well as all stakeholders.

12

Intelligent Forecasting of Automatic Train Protection System Failure Rate in China High-speed Railway

Kang Renwei, Wang Junfeng, Cheng Jianfeng, Chen Jianqiu, Pang Yanzhi

Eksploatacja i Niezawodność

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2019

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Vol. 21, no. 4

567--576

EN

Intelligent and personalized dynamic maintenance and spare parts configuration of high-speed railway have been the main trend to guarantee the safety capability of trains. In this paper, a new Automatic Train Protection (ATP) system failure rate calculation method is proposed, and the delay time and embedded dimension are determined by C-C algorithm. Then the phase space is reconstructed from one-dimensional time series to high-dimensional space. Based on chaotic characteristics of failure rate, a short-term intelligent forecasting model of failure rate of ATP system is established. The actual failure statistics from 2010 to 2018 are used as samples to train and test the validity of the model. From prediction results, it shows that the proposed chaos prediction model has an accuracy of 99.71%, which is better than the support vector machine model. Through the intelligent prediction of failure rate, this paper solves the maintenance inflexibility and imbalance of supply and demand of spare parts configuration.

PL

Inteligentna i spersonalizowana dynamiczna konserwacja i konfiguracja części zamiennych pociągów kolei dużych prędkości stanowią ostatnio główny trend w zakresie zapewniania bezpieczeństwa pociągów. W niniejszym artykule zaproponowano nową metodę obliczania intensywności uszkodzeń systemu Automatycznej Ochrony Pociągu (ATP), a czas opóźnienia i wymiar zanurzenia określano za pomocą algorytmu CC. Następnie, przestrzeń fazową przekształcono z jednowymiarowego szeregu czasowego do przestrzeni wielowymiarowej. Opierając się na chaotycznych charakterystykach intensywności uszkodzeń, utworzono model krótkoterminowego inteligentnego prognozowania awaryjności systemu ATP. Do uczenia modelu i weryfikacji jego trafności wykorzystano rzeczywiste dane statystyczne dotyczące awarii pociągów z lat 2010–2018. Z wyników prognoz wynika, że proponowany model predykcji, oparty na teorii chaosu, cechuje się dokładnością na poziomie 99,71%, czyli wyższą niż model maszyny wektorów nośnych. Dając możliwość inteligentnej predykcji intensywności uszkodzeń, niniejsza praca rozwiązuje problem braku elastyczności w utrzymaniu ruchu pociągów oraz braku równowagi między podażą a popytem na części zamienne.

13

Cykl życia chłodni kominowych

Korentz J., Nowogońska B., Darłak J., Bączkowski J.

Materiały Budowlane

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2018

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nr 4

58--60

PL

W artykule przedstawiono model predykcji zmian właściwości użytkowych obiektów budowlanych. Został on zastosowany do oceny stanu technicznego dziewięciu chłodni eksploatowanych od lat sześćdziesiątych ubiegłego wieku na terenie Elektrowni Turów. Modelem tym opisano i zilustrowano przebieg zmian właściwości użytkowych chłodni kominowych w czasie ich dotychczasowej eksploatacji. Przeprowadzone analizy wykazały, że stan techniczny chłodni był i jest na odpowiednim poziomie.

EN

The article presents a model prediction of changes in the performance of buildings. This model was used to assess the technical condition of nine cold stores operated since the 1960s at the Turow Power Plant. This model describes and illustrates changes of the performance of cooling towers during its previous operation. The analyses carried out showed that the technical condition of the cold store was and is maintained at an appropriate level.

14

Comparative Analysis of Object-Oriented Software Maintainability Prediction Models

Zighed N., Bounour N., Seriai A. D.

Foundations of Computing and Decision Sciences

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2018

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Vol. 43, No. 4

359--374

EN

Software maintainability is one of the most important aspects when evaluating the quality of a software product. It is defined as the ease with which the existing software can be modified. In the literature, several researchers have proposed a large number of models to measure and predict maintainability throughout different phases of the Software Development Life Cycle. However, only a few attempts have been made for conducting a comparative study of the existent proposed prediction models. In this paper, we present a detailed classification and conduct a comparative analysis of Object-Oriented software maintainability prediction models. Furthermore, we considered the aforementioned proposed models from three perspectives, which are architecture, design and code levels. To the best of our knowledge, such an analysis that comprises the three levels has not been conducted in previous research. Moreover, this study hints at certain fundamental basics concerning the way of how measure the maintainability knowing that at each level the maintainability will be measured differently. In addition, we will focus on the strengths and weaknesses of these models. Consequently, the comparative study yields that several statistical and machine learning techniques have been employed for software maintainability prediction at code level during the last decade, and each technique possesses its specific characteristic to develop an accurate prediction model. At the design level, the majority of the prediction models measured maintainability according to the characteristics of the quality models. Whereas at the architectural level, the techniques adopted are still limited and only a few of studies have been conducted in this regard.

15

Prediction of surface roughness of end milling for cycloidal gears based on orthogonal tests

Luo S. M., Liao L. X., Mo J. Y.

Engineering Transactions

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2018

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Vol. 66, iss. 3

339--352

EN

End milling method is applied to machining of cycloidal gears to improve the cutting quality and efficiency. The influence of milling parameters on the surface roughness is investigated based upon orthogonal tests with the four factors and four levels, as well as analysis of range and variance. A model to predict the surface roughness is built up on basis of the probability statistics and multivariate nonlinear regression analysis. Significance tests are conducted on the prediction model, and the interactive effect of these parameters on the surface roughness is figured out so as to propose optimization schemes. The results show that the shaft inclination angle has the biggest impact on the surface roughness, followed by the feed per tooth, the radial feed and the spindle speed. The prediction model of surface roughness is proved to have high prediction accuracy. This study aims to provide references for the improvement of machining quality of cycloidal gears and optimization of milling parameters.

16

Stan drogi jako ważny czynnik bezpieczeństwa ruchu

Radzikowski T., Zieliński J., Skakuj M.

Drogownictwo

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2018

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nr 12

392--400, III s. okł.

PL

Autorzy przeanalizowali oddziaływanie czynników potencjalnie wpływających na liczbę zdarzeń drogowych na sieci dróg wojewódzkich w województwie warmińsko-mazurskim, obejmującej około 1 800 km dróg. Wzięli pod uwagę parametry związane z natężeniem ruchu, stanem technicznym nawierzchni jezdni, lokalizacją skrzyżowań, geometrycznym ukształtowaniem dróg, występowaniem alej drzew w przekroju drogi i w skrajni drogi, przechodzeniem drogi przez obszar ze zwartą zabudową. Do wyznaczenia odcinków potencjalnie niebezpiecznych skorzystali z modelu predykcji stopnia bezpieczeństwa ruchu drogowego opracowanego na Uniwersytecie Virginia Tech. Do weryfikacji modelu wykorzystali dane o zdarzeniach drogowych pochodzące z lat 2014–2016. Opracowali wzorce wizualizacji pozwalające w sposób wielostronny prezentować istotne dla zarządców informacje związane z zapewnieniem bezpieczeństwa na drogach.

EN

The authors analysed the impact of factors potentially influencing the number of road incidents on the regional road network in Warmińsko-Mazurskie Voivodeship, covering about 1 800 km of roads. They took into account parameters related to traffic volume, technical condition of road surface, location of intersections, geometric shape of roads, presence of avenues of trees in the road cross-section and at the road gauge, crossing the road through an area with densely built-up areas. They used the road safety prediction model developed at Virginia Tech University to determine potentially dangerous sections. To verify the model, they used data on traffic incidents from 2014–2016. They developed visualization templates that allow to present in a multifaceted way the information important for managers related to ensuring road safety on the roads.

17

Measurement data processing with the use of art networks

Mrówczyńska M., Sztubecki J.

Civil and Environmental Engineering Reports

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2018

|

Vol. 28, no. 2

186--195

EN

ART (Adaptive Resonance Theory) networks were invented in the 1990s as a new approach to the problem of image classification and recognition. ART networks belong to the group of resonance networks, which are trained without supervision. The paper presents the basic principles for creating and training ART networks, including the possibility of using this type of network for solving problems of predicting and processing measurement data, especially data obtained from geodesic monitoring. In the first stage of the process of creating a prediction model, a preliminary analysis of measurement data was carried out. It was aimed at detecting outliers because of their strong impact on the quality of the final model. Next, an ART network was used to predict the values of the vertical displacements of points of measurement and control networks stabilized on the inner and outer walls of an engineering object.

PL

Sieci neuronowe ART (ang. Adaptive Resonance Theory) zostały opracowane w latach 90 ubiegłego wieku, jako nowe podejście w rozwiązywaniu problemów klasyfikacji i rozpoznawaniu obrazów. Sieci ART należą do grupy sieci rezonansowych, których uczenie prowadzone jest w trybie nie nadzorowanym. W artykule przedstawiono podstawowe zasady budowy i uczenia sieci neuronowych ART wraz z możliwością aplikacji tego rodzaju sieci do rozwiązywania zagadnień predykcji i przetwarzania danych pomiarowych, w szczególności pozyskanych w wyniku prowadzonego monitoringu geodezyjnego. W pierwszym etapie procesu budowy modelu predykcyjnego wykonano wstępną analizę danych pomiarowych związaną z wykrywaniem obserwacji odstających ze względu na ich istotny wpływ na ostateczną jakość modelu. Następnie wykorzystując sieć ART wyznaczono przewidywane wartości przemieszczeń pionowych dla punktów sieci pomiarowo-kontrolnej, zastabilizowanych na wewnętrznych i zewnętrznych ścianach obiektu budowlanego, na których zauważono liczne spękania.

18

Nonlinear damage accumulation of concrete subjected to variable amplitude fatigue loading

Chen Y., Chen X., Bu J.

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2018

|

Vol. 66, nr 2

157--163

EN

To account for the load sequence effect, damage fatigue models with nonlinearity in propagation and accumulation have been developed. This paper reviews five classical nonlinear fatigue models used to predict the life times of concrete under variable amplitude loadings. Experimental results from literature are used to validate the five classical prediction models. It can be found that Hilsdorf and Kesler model yields unsafe or conservative predictions, and the other four models are more suitable for predicting life times of concrete. In this paper, the author used a new nonlinear damage model based on the nonlinear continuum damage mechanics to predict fatigue life of concrete. The model considers fatigue limit, loading parameters, the unseparable characteristics for the damage parameter and the load sequence effect. The validity of the nonlinear fatigue damage model is checked against tests from literature.

19

On Woven Fabric Sound Absorption Prediction

Prasetiyo I., Desendra G., Hermanto M. N., Adhika D. R.

Archives of Acoustics

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2018

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Vol. 43, No. 4

707--715

EN

For building applications, woven fabrics have been widely used as finishing elements of room interior but not in particular aimed for sound absorbers. Considering the micro perforation of the woven fabrics, they should have potential to be used as micro-perforated panel (MPP) absorbers; some measurement results indicated such absorption ability. Hence, it is of importance to have a sound absorption model of the woven fabrics to enable us predicting their sound absorption characteristic that is beneficial in engineering design phase. Treating the woven fabric as a rigid frame, a fluid equivalent model is employed based on the formulation of Johnson-Champoux-Allard (JCA). The model obtained is then validated by measurement results where three kinds of commercially available woven fabrics are evaluated by considering their perforation properties. It is found that the model can reasonably predict their sound absorption coefficients. However, the presence of perturbations in pores give rise to inaccuracy of resistive component of the predicted surface impedance. The use of measured static flow resistive and corrected viscous length in the calculations are useful to cope with such a situation. Otherwise, the use of an optimized simple model as a function of flow resistivity is also applicable for this case.

20

Assessment of Heterogeneous Road Traffic Noise in Nagpur

Lokhande S. K., Pathak S. S., Kokate P. A., Dhawale S. A., Bodhe G. L.

Archives of Acoustics

|

2018

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Vol. 43, No. 1

113--121

EN

The objective of the study is to assess the noise scenario and evaluate prediction model for heterogeneous traffic conditions. In the past few years, road traffic of Nagpur has increased significantly due to the rapid increase in the number of vehicles. Noise levels are monitored at six different squares, characterized as interrupted traffic flow due to traffic signals, high population density and heavy traffic where the major sources of noise are engines, exhausts, tires interacting with the road, horns, sound of gear boxes, breaks, etc. The A-weighted time-average sound levels (LAeq, T) are measured at the different time of day during peak and off-peak traffic hours. To assess the traffic noise more precisely, the noise descriptors such as L10, L50, L90, LAeq, T, TNI (Traffic Noise Index), NPL (Noise Pollution Level) and NC (Noise Climate) are used. In the present study, the Federal Highway Administration (FHWA) noise prediction model is used for prediction of noise levels and it is observed that one-hour duration measured LAeq, T ranged from 71 to 76 dB(A) and 71.6 to 76.3 dB(A) during peak and off peak hours respectively. Due to the heavy traffic the peak hour Sound Exposure Levels (LAE) at all locations are exceeding permissible limit of 70 dB(A) prescribed by the World Health Organization (W.H.O). Off-peak traffic hour noise levels are within permissible limit except at two locations, Jagnade and HB town square. Significant correlation was obtained when best fit lines generated between measured and predicted values gives R2 of 0.455 for all time intervals. Chi-Square test (X2) was also computed to investigate the noise levels at different squares. The results show that the inhabitants of Nagpur city are exposed to high transportation noise during daytime.