Prediction of Environmental Costs of Construction Noise and Vibration at the Preconstruction Phase

Hong, Taehoon; Ji, Changyoon; Park, Joowan; Leigh, Seung Bok; Seo, Dong Yeon

doi:10.1061/(asce)me.1943-5479.0000313

Cited by 23 publications

(5 citation statements)

References 30 publications

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“…In particular, noise from construction projects is considered a principal pollutant that causes harmful effects to neighboring residents and the surrounding environment [7,9,14,15]. Chronic exposure to noise can lead to significant health problems, such as depression [5,8,16,17], hearing impairment [5,18,19], cardiovascular disease [3,20], impaired cognitive performance [1,4], interference of communication [2], general understanding [4], hypertension [2,8,10,20], mental disturbances [3,4,8,16,18], short-term memory impairments [1,2,4], and sleep disorders [2,3,20]. Such damages are not only restricted to residents, but may also cause economic losses to construction companies through cost overruns and schedule delays [8,10,14,21].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Distance Measurement Methods for Construction Noise Prediction Using Case-Based Reasoning

et al. 2019

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Concerns over environmental issues have recently increased. Particularly, construction noise in highly populated areas is recognized as a serious stressor that not only negatively affects humans and their environment, but also construction firms through project delays and cost overruns. To deal with noise-related problems, noise levels need to be predicted during the preconstruction phase. Case-based reasoning (CBR) has recently been applied to noise prediction, but some challenges remain to be addressed. In particular, problems with the distance measurement method have been recognized as a recurring issue. In this research, the accuracy of the prediction results was examined for two distance measurement methods: The weighted Euclidean distance (WED) and a combination of the Jaccard and Euclidean distances (JED). The differences and absolute error rates confirmed that the JED provided slightly more accurate results than the WED with an error ratio of approximately 6%. The results showed that different methods, depending on the attribute types, need to be employed when computing similarity distances. This research not only contributes an approach to achieve reliable prediction with CBR, but also contributes to the literature on noise management to ensure a sustainable environment by elucidating the effects of distance measurement depending on the attribute types.

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Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Distance Measurement Methods for Construction Noise Prediction Using Case-Based Reasoning

et al. 2019

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“…Most of the research on estimating the noise patterns of construction sites is conducted based on noise propagation models specified in international standards such as ISO 9613-1 and 2 (Di et al, 2018;Gilchrist et al, 2003;Hong et al, 2015;Liu et al, 2021). The researchers have mainly analyzed how construction site noise spreads to the outside to respond to civil complaints that may occur during construction (Cai et al, 2015;Gannoruwa and Ruwanpura, 2007;Gulliver et al, 2015;Hong et al, 2021b;Lee et al, 2008;Santos de Oliveira et al, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Real-Time Noise Sensing at Construction Sitesbased on Spatial Interpolation for Effective Reduction Measures

Lee¹,

Moon²,

Hwang³

et al. 2022

Proceedings of the 29th EG-ICE International Workshop on Intelligent Computing in Engineering

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Construction site noiseneeds to be properly managed because itaffectsthe health and safety of workers and nearby residents. Therefore, the authors attemptedto develop areal-time noise informationmapping system forconstruction sitesthat can support the establishment of noise reduction measures by practitionersusingspatial interpolation. Fieldconstraints were identified to ensure that the noise-estimation model has high accuracy without disturbing workers.Spatial interpolation was utilized to develop the noise-estimation model,and the performance was evaluated by installing sensors in an experimental environment according tothefield constraints. The model showed optimal performance,with maximum and minimum accuracies of 97.5% and 92.4%, respectively, when using eight sensingpoints as inputs. The research resultswerevisualized through the Unity 3D Engine for the convenience of field workers. Through the results of this study, practitioners will be able to easily understand information on high-noise areas that need to be managed, thereby minimizing the cost and time required for on-site noise management.

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“…With the development of information technology and computer network technology, use the existing coal mine monitoring system and the company's internal information network, realize the real-time transmission of monitoring information on INTRANET/INTERNET, and use database and data warehouse technology to save effective data on the local server, so in addition to the dispatching rooms of the bureau, mine, and well, provide real-time relevant underground dynamic working condition information for authorized institutions at all levels, and send the information to the remote service center through INTERNET, information is processed by different tools, in order to provide good decision support information. However, the production process control of the coal mine industry is a very special field, there are hundreds of monitoring points arranged by various coal mining enterprises, there are a lot of data collected and accumulated by each monitoring point year after year, it is generally difficult for humans to understand these data and their relationships, not to mention the timely summary and prediction [2]. As shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Prediction of operation vibration state of coal mine mechatronics equipment based on data mining

Jia¹,

Deb²,

Sajja³

2022

J. vibroeng.

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In order to explore the prediction of the operation vibration state of coal mine mechatronics equipment, the author proposes a method based on data mining in response to the problems of large amount of data in the operating state of coal mine electromechanical equipment, low data utilization, and slow speed of single-machine massive data mining, using Map Reduce technology, a dual Map Reduce mining prediction framework is proposed, establish a data mining prediction model for the running state of dual Map Reduce, using MapReduce1 to extract features of monitoring data, use MapReduce2 to predict and analyze feature data. Finally, by building the Hadoop platform, reveal the relationship between Hadoop cluster nodes and parallel processing speed, the efficiency of the data mining prediction framework is verified: Perform an experimental comparative analysis of the single prediction model and the proposed AGB combined prediction model, the prediction accuracy of the AGB combined prediction model is verified.

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Prediction of Environmental Costs of Construction Noise and Vibration at the Preconstruction Phase

Cited by 23 publications

References 30 publications

Performance Evaluation of Distance Measurement Methods for Construction Noise Prediction Using Case-Based Reasoning

Performance Evaluation of Distance Measurement Methods for Construction Noise Prediction Using Case-Based Reasoning

Real-Time Noise Sensing at Construction Sitesbased on Spatial Interpolation for Effective Reduction Measures

Prediction of operation vibration state of coal mine mechatronics equipment based on data mining

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