This aspect is mainly relevant to freight trains. By taking this noise-based track access charges into account when evaluating the Life-Cycle-Cost of the railway system, decreasing noise emissions do have a direct influence on those LCC.
As simple as this direct connection between noise depended track access charges seems, the differentiation of different aspects of cost for a railway is challenging by itself. There are different parts of the system that could be considered. First of all for railways, as for most mobility offers, there is the issue that a clear differentiation between a business point of view and an economic point of view from the society is not possible. As noise pollution would usually be classified as an economic topic, meaning that it would concern mainly society in comparison to single businesses, whereas track access charges are on the contrary a business aspect. For the railway system there are different stakeholders, which have costs and also charge other stakeholders. To stick to the terms of track access charges, track access charges are charged by Infrastructure Manager to Railway Undertakings. Track access charges usually should cover the investment cost and the maintenance of the infrastructure, but investments in railway infrastructure are often subsidised by governments and the example of noise depended track access charges shows that also other costs than investments and maintenance can be considered. Other stakeholders are for example suppliers or passengers / freight loaders. Between every stakeholder there are usually profit margins included or as mentioned government subsidies could have an influence. In addition these interrelations can differ significantly within Europe. Therefore, it is not the inclusion of noise depended track access charges into the KPI LCC that is the most challenging part, but instead to define a proper LCC definition for the European railway system and its components. As for noise depended track access charges they will be considered as being a representation of external costs for the railway system because of noise pollution and therefore being included as one part of the LCC calculations.
Noise Level Monitoring was done for a total of 8 hours for each outer platform. The monitoring results are presented for every 2 hours according to the peak hours selected. The Noise Reading was taken for every 15 seconds interval on platform no. 1 of Lucknow Junction Railway station and Lucknow NR railway station.
Prior to this project Pandrol had limited methods for predicting the noise and vibration of railway bridges and viaducts, mostly depending on previously measured data. This meant that predictions for novel bridges still in the design stages were difficult. Assessment of the effectiveness of reducing noise by the use of resilient fastening systems could only be achieved with costly time-consuming noise and vibration surveys. Now a model exists and has been delivered to Pandrol (Thompson, Jones & Bewes, 2005) that can provide noise predictions in minutes. As well as aiding Pandrol’s engineers in the design of a fastening system for a bridge, the model also means that Pandrol can quickly respond to customer’s requests for state-of-the-art low noise design knowledge and understanding. At the time of writing this thesis the model has already been used in a real application (Pandrol Rail Fastenings Limited, 2004) and has prevented the over-design of a fastening system for application on Arsta Bridge, Sweden.
Although there are studies which analyze passenger and freight transportation efficiency separately (e.g. Hilmola, 2008; Yu & Lin, 2008), this paper aims to conduct overall efficiency assessment of European railways, thus all variables are integrated into a single model. Also, some authors included additional inputs and outputs, such as number of locomotives, number of passengers carried, freight tons transported (e.g. Hilmola, 2008; Kutlar et al., 2013), passenger and freight train-kilometers (Oum & Yu, 1994; Yu & Lin, 2008). These variables are not included in this study, since only factors which affect railway transportation process the most and which represent revenue measures are observed. Descriptive statistics of inputs and outputs selected for the time period observed are presented in Table 2.
Turkish Regulation on Assessment and Management of Environmental Noise , which was enforced in 2010 as a part of the alignment processes with Environmental Noise Directive  of European Union (EU) legislation, stated that necessary precautions should be taken to preserve physical and mental health of people in the incident of exposure to environmental noise. In order to achieve this goal, exposure rates for environmental noise should be determined by preparing noise maps, acoustic reports, and environmental noise exposure level evaluation reports for the main railway lines. Then, the public should be informed about environmental noise and its effects by considering reported data. Preparation of action plans for prevention and reduction of noise is required for locations where exposure to environmental noise levels may cause harmful effects on human health. Deadlines for preparing noise maps and action plans are specified in the Turkish Regulation  which is lately in 2014. Noise maps and action plans are prepared for settlements and areas outside the residential areas by municipalities and the Transport, Maritime Affairs and Communications Ministry. Noise maps and action plans are reviewed every five years and they are revised if deemed necessary.
Key issue of the project was the preparation of a well-founded data base for the state-of-the-art of noise emission (exterior noise) from European railway vehicles. In this context, values from acoustic type tests of newly homologated railway vehicles, collected on the basis of the TSI Noise 2006 and the TSI Noise 2011, were collected. First, a comparative assessment of the measuring and operation conditions of both TSI Noise versions is necessary in order to judge, whether the data collected on the basis of the TSI Noise 2011 can be deemed to be comparable. For this purpose, both TSI guidelines were compared with regard to measuring quantities, measuring
Division, ASME-HK & BSL and issued to the participants who registered and attended compulsory sessions 7, 8, 9 and 10, and then successfully passing the session 18 end-of-course appraisal and assessment.
An Architectural Acoustics Design Certificate will be endorsed by HKIOA,
4.1 Measurements campaign
The measurement campaign has been performed be- tween 21 pm of June 6 th 2001 and 2 am of the following
day. Measurements during evening and night time al- lowed us to capture signals with minimal ambient noise disturbances. It is worth noting that some of the mea- surements occurred when strong wind gusts took place. Noise signals were recorded with a class I micro- phone, as prescribed by the standards EN 60651/1994 and EN 60804/1994. The microphone was accurately calibrated before and after each series of measurements. We performed the noise measurements in four diﬀerent locations, referred to as P M 1-P M 4, at increasing dis- tance of the railway track. All receptors are located to the north of the track along a line perpendicular to it. The relative elevation and distance of the receptors from the track is given in table 1. 18 diﬀerent noise signals were acquired. For each event we could also register the length of the train, the type of train and its velocity.
This Study shows how the noise due to railway varies with different peak hours in day as well as night. For Lucknow Junction railway station the highest noise was recorded during the day time period from 10:30 AM to 12:30 PM and for Lucknow NR railway station the highest noise was recorded during the night time period from 09:00 PM to 11:00 PM. The Modelling results also satisfies the on- site measurements of railwaynoise where the noise contours at 5 m intervals show peak noise at Night for NR railway station and for day Lucknow Junction shows highest noise.
Table 1 reports the diffuse-field related equivalent continuous A-weighted sound pressure levels obtained with the manikin manufacturer’s diffuse-field frequency response according to the procedure explained in paragraph 3.3. The diffuse- field frequency response has been selected because of the suggestions made up by Ianniello , Dajani et al.  and Brammer et al. . The noise exposure levels reported in Table 1 are extremely variable: from a minimum value of 50 dB(A) to a maximum value of 87 dB(A). Moreover, in 16 cases (17%) the level of 80 dB(A) is exceeded (this happens always and only in the telephone central office I). By these data we can conclude that the risk of hearing loss could exist for some workers in certain conditions.
Laboratory tests have been used in a variety of ways since March/Autumn 2006 until recently. There has been some variation in the number of assessment tasks and their weightings between March/Autumn 2006 and March/Autumn 2008. For example, there were four laboratory tests applied in 2006 for both sessions. In addition, three make-up tests were designed for students who obtained less than seventy per cent in any given test (zero was awarded to students for any test mark less than seventy per cent). In 2007 for both sessions, there were six laboratory tests (three compulsory and two optional tests) in which any test mark that less than sixty per cent was awarded zero and the tests due in laboratory classes. Changes in the number of laboratory tests and opportunity to re-sit the test have been applied in March/Autumn 2008 where the best of three test marks were chosen out of four laboratory tests. As applied in 2007, students who obtained less than seventy per cent were given the opportunity to re-sit the test in the following week with a different data set and completed during laboratory classes. Nevertheless, students who failed the re-test have further been examined by the subject coordinator to clarify any problems they experienced. The reason for having a minimum mark of sixty or seventy per cent in each test was primarily to enhance student competency in the topics examined. In particular, students who were awarded zero were expected to demonstrate their competency through a retest that was offered in the following week. This test retest approach has come to form the basis of the support system to identifying students at risk typically those who needed the encouragement to do a retest. The students who failed to sit retests after having been given feedback were often found to have issues such as anxiety, lack of confidence, depression, obsessive or other difficulties. Tests typically comprise the analyses of data sets in addition to the understanding of theoretical concepts.
As stated earlier in this Chapter, if risks are high, risk reduction measures must be applied or maintenance work must be considered to reduce the occurrence probabilities or control the possible consequences. If risks are negligible, no actions are required， but the information produced needs to be recorded for audit purposes. However, the acceptable and unacceptable regions are usually divided by a transition region. Risks that fall in this transition region need to be reduced to as low as reasonably practicable (ALARP). In other words, “cost-effective” measures should be applied. In this case, selecting the optimal maintenance strategy among many alternatives based on cost and safety analysis is a multi-criteria decision making (MCDM) problem, which can usually be solved by optimisation techniques. The literature search indicates that traditional cost-benefit analysis based on simple comparisons cannot be applied to this process. This study also presents a risk-based maintenance decision making model by using the TOPSIS technique which synthesises the risk and cost models to produce the preference degree of each maintenance option. Once preference degrees of all maintenance options in hand are produced, the best option can be chosen. In this model, both the risk associated with a railway asset system and the costs incurred in each maintenance option are mapped onto a utility space and assessed in accordance with the respective constraints. The proposed decision making model could be an effective tool to get a better understanding of risks associated with railway asset systems and make better maintenance decisions at the right time for managing the risks under various conditions.
Unsteady surface pressure spectra for the cove filler case are shown in Figure 30 . Only the Kulite sensors, P 2 and P 3 are left exposed to the flow in the presence of the cove filler. These two sensors are located near the slat cusp and the slat trailing edge, respectively. Unfortunately, P 2 sensor was apparently damaged during the experiments; thus the spectra for this location are missing for suspect cases. During the experiments, the sensors underneath the cove filler also measured some pressure fluctuations, indicating that the cove filler was not perfectly sealed to the cove wall. However, a sealant could not be used as it added a finite thickness between the cove filler and slat, resulting in a slight but noticeable change of the geometry at the slat trailing edge. As expected, the tonal peaks associated with the shear layer instability do not exist as the region of flow separation is drastically reduced in size. The suppression effects are significant at the low angles of attack, while only slight suppression is observed at P 2 for St s = 0.2 at the highest angle of attack. Without measuring the unsteady fluctuating pressure on the cove filler surface, only very limited information is available about the dominant noise sources contributing to the near field acoustic signature at P 2 and P 6 .
suitability for nearby recreation had lower predictive power. Thus, the more sophisticated metrics did not help in establishing stronger general associations between green and annoyance. Pronounced effects of NDVI and LU-green on road traffic noise annoyance were also reported by Dzhambov et al. (2018b) and Gidlof-Gunnarsson and Öhrstr öm (2007) , respectively. Interestingly, LU-green was equally or even more asso- ciated with annoyance than LU-natural. Apparently water bodies only little affected annoyance in our study. Possibly the range of blue spaces covered here was too small to reveal this effect, with too few people in the study sample residing within proximity to blue spaces. Also the accuracy of 3–8 m of the underlying VECTOR25 data set might have played a (minor) role. However, also Leung et al. (2017) found that view on green reduces annoyance more than view on water bodies. We could not reproduce the particularly strong effect of visible vegetation from home reported by Van Renterghem (2019) . However, this is likely due to the fact that (self-reported) view includes nearby vegetation (e.g., trees, shrubs), which was not accounted for by our metric. In fact, Mueller et al. (2020) found reduced road traffic noise annoyance in urban areas to be associated with residential tree cover density.
The interviews were semi-structured and face-to-face. The same interview protocol was used for both the providers and the clients. This ensured that the same category of information was gathered, and that a comparison of the data could be made. In order to validate that the correct data is transcribed, the interviews were sent back to the interviewees. If this was not the case the interviewees were asked to indicate if information should be changed. Two groups were interviewed: (1) the providers of innovations and (2) clients performing tests in other infrastructure sectors. From the providers three main stakeholder groups were distinguished: Engineering firms, contractors and suppliers. These stakeholders have provided information on how providers test innovation in the railway sector. They were chosen by means of a power and interest grid based on the innovation strategy of ProRail. The second group interviewed were the infrastructure clients, consisting of road, water, airline and drink water infrastructure. The clients were interviewed to identify if the same problems exist as are found with the providers, but also to find out if solutions for these problems have already been developed. Furthermore, it shows a broader perspective of testing as a client. In total twelve interviews were held. The full extent of choosing these stakeholders is discussed in appendix D.
Abstract: The railway industry focus in the past years was to research, find and develop methods to mitigate noise and vibration resulted from wheel/rail contact along track infrastructure. This resulted in a wide range of abatement measures that are available for the professionals of the industry today. However, although there are many options in the market, their practical implementations depend upon general constraints that affect most technological application in the engineering world. The progression of these technologies have facilitated the selection of more adequate methods for each best case scenario, but further studies are ought to be made to proper assess if each one is fit for their purpose. Every method implementation must be analyzed through budget and timeframe limitations, which includes building, maintenance and inspection costs and time allocation, while also aiming to meet different benefits, such as environmental impact control and wear of the whole infrastructure. There are several situations and facilities in a railway project design that need noise and vibration mitigation methods and each design allocates different priorities for each one of them. Traditionally the disturbance caused by railways to the community are generated by wheel/rail contact sound radiation that expresses in different ways, depending on the movement of the rolling stock and track alignment, such as rolling noise, impact noise and curve noise. More specifically, in special trackworks such as turnouts, the main area of this study, there are two noises types that must be evaluated: impact noise and screeching noise. With respect to the second, it is similar to curve squeals and, being such, its mitigation methods are to be assigned as if it was to abate curve squeal in turnouts and crossings. The impact noise on the other hand, emerges from the sound made by the rolling stock moving through joints and discontinuities (i.e. gaps) that composes these special components of a railway track. A life cycle analysis is therefore substantial for this reality and in this case will be applied to Squeal and Impact Noise on Special Trackwork. The evaluation is based on a valid literature review and the total costs were assumed by industry reports to maintain coherency. The period for a life cycle analysis is usually of 50 years, hence it was the value assumed. As for the general parameters, an area with high density of people was considered to estimate the values for a community with very strict limits for noise and vibration.
– Possible to account for delays (stochastic assessment)
• Capacity assessment is very much dependent on the size of the network considered
• Improvement (decrease) in network capacity consumption observed in case when some junctions are upgraded
Noise level in the hearing zones of dentists and dental auxiliaries
The noise dosimeters (Spark TM
706, Larson Davis, Provo, UT, USA) followed the Occupational Safety and Health Admin- istration criteria including an exchange rate of 5 decibels; the frequency weighting was A; the response was slow; the criteria level was 90 dBA; and the threshold was 80 dBA. The 80 dBA threshold dosimeter was used to measure the noise that em- ployees identified during a walk around and whose exposure may exceed 85 dBA on a TWA . After that, it was attached to the subjects’ collar in order to determine the personal noise dose during working periods. The noise dosimeter readout was in percent noise dose exposure (percent dose) and the equiva- lent continuous A-weighted sound level in decibels (dBA) for each minute during the period sampled. Noise level was presented in decibel A-scale (dBA) which referred to a human hearing threshold and was calculated for an 8-hour TWA for each period of work, while impulsive noise levels were present- ed in decibel C-scale (dBC).