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Dr. Sakdirat Kaewunruen
University of Birmingham

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Journal article
Published: 06 August 2021 in Climate
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One of the top long-term threats to airport resilience is extreme climate-induced conditions, which negatively affect the airport and flight operations. Recent examples, including hurricanes, storms, extreme temperatures (cold/hot), and heavy rains, have damaged airport facilities, interrupted air traffic, and caused higher operational costs. With the development of civil aviation and the pre-COVID-19 surging demand for flights, the passengers’ complaints of flight delay increased, according to FoxBusiness. This study aims to discover the weather factors affecting flight punctuality and determine a high-dimensional scale of consequences stemming from weather conditions and flight operational aspects. Machine learning has been developed in correlation with the weather and statistical data for operations at Birmingham Airport as a case study. The cross-correlated datasets have been kindly provided by Birmingham Airport and the Meteorological Office. The scope and emphasis of this study is placed on the machine learning application to practical flight punctuality prediction in relation to climate conditions. Random forest, artificial neural network, support vector machine, and linear regression are used to develop predictive models. Grid-search and cross-validation are used to select the best parameters. The model can grasp the trend of flight punctuality rates well where R2 is 0.80 and the root mean square error (RMSE) is less than 15% using the model developed by random forest technique. The insights derived from this study will help Airport Authorities and the Insurance industry in predicting the scale of consequences in order to promptly enact and enable adaptative airport climate resilience plans, including air traffic rescheduling, financial resilience to climate variances and extreme weather conditions.

ACS Style

Sakdirat Kaewunruen; Jessada Sresakoolchai; Yue Xiang. Identification of Weather Influences on Flight Punctuality Using Machine Learning Approach. Climate 2021, 9, 127 .

AMA Style

Sakdirat Kaewunruen, Jessada Sresakoolchai, Yue Xiang. Identification of Weather Influences on Flight Punctuality Using Machine Learning Approach. Climate. 2021; 9 (8):127.

Chicago/Turabian Style

Sakdirat Kaewunruen; Jessada Sresakoolchai; Yue Xiang. 2021. "Identification of Weather Influences on Flight Punctuality Using Machine Learning Approach." Climate 9, no. 8: 127.

Journal article
Published: 04 August 2021 in Open Research Europe
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Background: To improve railway construction and maintenance, a novel digital twin that helps stakeholders visualize, share data, and monitor the progress and the condition during services is required. Building Information Modelling (BIM) is a digitalization tool, which adopts an interoperable concept that benefits the whole life-cycle assessment (LCA) of the project. BIM’s applications create higher performance on cost efficiency and optimal time schedule, helping to reduce any unexpected consumption and waste over the life cycle of the infrastructure. Methods: The digital twin will be developed using BIM embedded by the lifecycle analysis method. A case study based on Taipei Metro (TM) has been conducted to enhance the performance in operation and maintenance. Life cycles of TM will be assessed and complied with ISO14064. Operation and maintenance activities will be determined from official records provided by TM. Material flows, stocks, and potential risks in the LCA are analyzed using BIM quantification embedded by risk data layer obtained from TM. Greenhouse emission, cost consumption and expenditure will be considered for integration into the BIM. Results: BIM demonstrated strong potential to enable a digital twin for managing railway maintenance and resilience. Based on the case study, a key challenge for BIM in Taiwan is the lack of insights, essential data, and construction standards, and thus the practical adoption of BIM for railway maintenance and resilience management is still in the design phase. Conclusions: This study exhibits a practical paradigm of the digital twin for railway maintenance and resilience improvement. It will assist all stakeholders to engage in the design, construction, and maintenance enhancing the reduction in life cycle cost, energy consumption and carbon footprint. New insight based on the Taipei Mass Rapid Transit system is highly valuable for railway industry globally by increasing the lifecycle sustainability and improving resilience of railway systems.

ACS Style

Sakdirat Kaewunruen; Jessada Sresakoolchai; Yi-Hsuan Lin. Digital twins for managing railway maintenance and resilience. Open Research Europe 2021, 1, 91 .

AMA Style

Sakdirat Kaewunruen, Jessada Sresakoolchai, Yi-Hsuan Lin. Digital twins for managing railway maintenance and resilience. Open Research Europe. 2021; 1 ():91.

Chicago/Turabian Style

Sakdirat Kaewunruen; Jessada Sresakoolchai; Yi-Hsuan Lin. 2021. "Digital twins for managing railway maintenance and resilience." Open Research Europe 1, no. : 91.

Original research article
Published: 28 July 2021 in Frontiers in Built Environment
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The casual effect and synergy of high-speed rail development on the modal transport changes in supply chain and logistics have not been considered well during the initial phase of any rail project design and development. This has impaired the systems integration and connectivity among the modes of transport in a region. In the United Kingdom, High Speed 2, a large-scale railway project with a planned completion date in 2033, affects many transport stakeholders. The project influences the existing transport systems, but the transport systems integration design has not been well depicted, resulting in a pressing concern on systems connectivity and social value. This is evident by many public protests along the planned route of the project. Therefore, it is important to evaluate different aspects for any possible changes in supply chains caused by the development of high-speed rail networks. This paper is the world's first to provide the sensitivity analysis of supply chains via air-rail-road freight transportation and logistics stemming from the High Speed 2 case by the rigorous assessments into the capacity, performance and environmental changes that may follow the project’s implementation. The research proposes a new method for estimation of consequences from a new transport project construction. The research findings demonstrate slight beneficial changes in freight transportation and logistics with a high potential for development; and reveal the project’s weaknesses and opportunities for better systems integration and business synergy.

ACS Style

Rucheng Liu; Anton Stefanovich; Sakdirat Kaewunruen. Sensitivity of a High-Speed Rail Development on Supply Chain and Logistics via Air-Rail-Road Freight Transportation. Frontiers in Built Environment 2021, 7, 1 .

AMA Style

Rucheng Liu, Anton Stefanovich, Sakdirat Kaewunruen. Sensitivity of a High-Speed Rail Development on Supply Chain and Logistics via Air-Rail-Road Freight Transportation. Frontiers in Built Environment. 2021; 7 ():1.

Chicago/Turabian Style

Rucheng Liu; Anton Stefanovich; Sakdirat Kaewunruen. 2021. "Sensitivity of a High-Speed Rail Development on Supply Chain and Logistics via Air-Rail-Road Freight Transportation." Frontiers in Built Environment 7, no. : 1.

Journal article
Published: 21 July 2021 in Materials
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Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

ACS Style

Xu Huang; Mirna Wasouf; Jessada Sresakoolchai; Sakdirat Kaewunruen. Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning. Materials 2021, 14, 4068 .

AMA Style

Xu Huang, Mirna Wasouf, Jessada Sresakoolchai, Sakdirat Kaewunruen. Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning. Materials. 2021; 14 (15):4068.

Chicago/Turabian Style

Xu Huang; Mirna Wasouf; Jessada Sresakoolchai; Sakdirat Kaewunruen. 2021. "Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning." Materials 14, no. 15: 4068.

Case report
Published: 09 July 2021 in Sport Sciences for Health
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In 2011, the percentage of the American population affected by Noise Induced Hearing Loss (NIHL) was 15.3%. Unlike most forms of hearing loss, this can be prevented by limiting exposure to certain magnitudes of noises for varying amounts of time. The present study, using six calibrated smartphones, aims to assess the sound given off from dropping free weights in the gym, at the University of Birmingham, which can be contributing to NIHL. A relationship between drop weight and vibration is also constructed. For vibration, it is found that vibration level (m/s2) increases with the drop weight, whereas the average noise level for each drop weight only varies by a range of 4.4 dB between 102.7 and 98.3 dB. Note that all the sound levels recorded are over 85 dB, which is the range where NIHL can be contributed to. This study reminds us that measures need to be taken to reduce the sound level from the drops of loaded barbells in any sports and fitness centers.

ACS Style

Sakdirat Kaewunruen; Junhui Huang; Jordan Haslam. Insights into noise and vibration stemming from the gym’s heavy lifting. Sport Sciences for Health 2021, 1 -10.

AMA Style

Sakdirat Kaewunruen, Junhui Huang, Jordan Haslam. Insights into noise and vibration stemming from the gym’s heavy lifting. Sport Sciences for Health. 2021; ():1-10.

Chicago/Turabian Style

Sakdirat Kaewunruen; Junhui Huang; Jordan Haslam. 2021. "Insights into noise and vibration stemming from the gym’s heavy lifting." Sport Sciences for Health , no. : 1-10.

Dataset
Published: 17 June 2021
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Crowdsourcing vibration data stemming from different activities and transportation usages (by trains, by buses, by bicycles by walking). We present a comprehensive dataset that provides the pattern of five activities walking, cycling, taking a train, a bus or a taxi. The measurements are carried out by embedded sensor accelerometer in smartphones. The dataset offers dynamic responses of subjects carrying smartphones in varied styles as they performing the five activities through vibrations acquired by accelerometers. The dataset contains corresponding time stamps and vibrations in three directions longitudinal, horizontal, and vertical stored in an Excel Macro-enabled Workbook (xlsm) format can be used to train an AI model in a smartphone which has potentials to collect people’s vibration data and decides what movement is being conducted. Besides, with more data are received, the database can be updated and it can be fed to train the model with a larger dataset. The prevalent of the smartphone opens the door of crowdsensing which leads to the pattern of people talking public transports can be understood. Furthermore, the time consumed in each activity is available in the dataset. Therefore, with a better understanding of people using public transports, the service and schedule can be planned perceptively. Activities to obtain the dataset are jointly funded by H2020 and Hitachi Europe.

ACS Style

Sakdirat Kaewunruen; Junhui. Crowdsourcing vibration data stemming from different transportation usages. 2021, 1 .

AMA Style

Sakdirat Kaewunruen, Junhui. Crowdsourcing vibration data stemming from different transportation usages. . 2021; ():1.

Chicago/Turabian Style

Sakdirat Kaewunruen; Junhui. 2021. "Crowdsourcing vibration data stemming from different transportation usages." , no. : 1.

Dataset
Published: 17 June 2021
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Crowdsourcing vibration data stemming from different activities and transportation usages (by trains, by buses, by bicycles by walking). We present a comprehensive dataset that provides the pattern of five activities walking, cycling, taking a train, a bus or a taxi. The measurements are carried out by embedded sensor accelerometer in smartphones. The dataset offers dynamic responses of subjects carrying smartphones in varied styles as they performing the five activities through vibrations acquired by accelerometers. The dataset contains corresponding time stamps and vibrations in three directions longitudinal, horizontal, and vertical stored in an Excel Macro-enabled Workbook (xlsm) format can be used to train an AI model in a smartphone which has potentials to collect people’s vibration data and decides what movement is being conducted. Besides, with more data are received, the database can be updated and it can be fed to train the model with a larger dataset. The prevalent of the smartphone opens the door of crowdsensing which leads to the pattern of people talking public transports can be understood. Furthermore, the time consumed in each activity is available in the dataset. Therefore, with a better understanding of people using public transports, the service and schedule can be planned perceptively. Activities to obtain the dataset are jointly funded by H2020 and Hitachi Europe.

ACS Style

Sakdirat Kaewunruen; Junhui. Crowdsourcing vibration data stemming from different transportation usages. 2021, 1 .

AMA Style

Sakdirat Kaewunruen, Junhui. Crowdsourcing vibration data stemming from different transportation usages. . 2021; ():1.

Chicago/Turabian Style

Sakdirat Kaewunruen; Junhui. 2021. "Crowdsourcing vibration data stemming from different transportation usages." , no. : 1.

Dataset
Published: 17 June 2021
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Crowdsourcing vibration data stemming from different activities and transportation usages (by trains, by buses, by bicycles by walking). We present a comprehensive dataset that provides the pattern of five activities walking, cycling, taking a train, a bus or a taxi. The measurements are carried out by embedded sensor accelerometer in smartphones. The dataset offers dynamic responses of subjects carrying smartphones in varied styles as they performing the five activities through vibrations acquired by accelerometers. The dataset contains corresponding time stamps and vibrations in three directions longitudinal, horizontal, and vertical stored in an Excel Macro-enabled Workbook (xlsm) format can be used to train an AI model in a smartphone which has potentials to collect people’s vibration data and decides what movement is being conducted. Besides, with more data are received, the database can be updated and it can be fed to train the model with a larger dataset. The prevalent of the smartphone opens the door of crowdsensing which leads to the pattern of people talking public transports can be understood. Furthermore, the time consumed in each activity is available in the dataset. Therefore, with a better understanding of people using public transports, the service and schedule can be planned perceptively. Activities to obtain the dataset are jointly funded by H2020 and Hitachi Europe.

ACS Style

Sakdirat Kaewunruen; Junhui. Crowdsourcing vibration data stemming from different transportation usages. 2021, 1 .

AMA Style

Sakdirat Kaewunruen, Junhui. Crowdsourcing vibration data stemming from different transportation usages. . 2021; ():1.

Chicago/Turabian Style

Sakdirat Kaewunruen; Junhui. 2021. "Crowdsourcing vibration data stemming from different transportation usages." , no. : 1.

Journal article
Published: 24 May 2021 in Environmental Impact Assessment Review
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This paper unprecedentedly benchmarks the environmental and economic impacts of notable High-speed rail (HSR) networks. The goals are to (i) point out the environmental impacts from the HSR networks and (ii) evaluate the whole life cycle cost of HSR systems. The emphasis of this study is placed on five HSR networks from five countries to depict the effectiveness of sustainable transport policies in each particular country. Both life cycle assessment (LCA) and life cycle cost (LCC) models are adopted for a new critical framework capable of benchmarking the lifecycle sustainability of HSR networks. The new findings exhibit that CRC's system is the leader in energy-saving, who consumes only 67.55 GJ/km yearly, and emits lowest CO2 at an amount of 77,532.32 tCO2/km annually. These impressive results are stemmed from key enabling policies related to eco-friendly rolling stock design, sustainable construction, and green energy grids. With respect to the LCC analysis, the SCNF network takes advantage in the economy of scale and unleashes the lowest cost among other networks. It estimates that the SNCF network spends approximately 1,990,599.51 £/km annually at a % discount rate. The implications of these finding are discussed that the initial project has a high chance to be successful on economic than the late project due to an influence of the time value of money.

ACS Style

Panrawee Rungskunroch; Zuo-Jun Shen; Sakdirat Kaewunruen. Benchmarking environmental and economic impacts from the HSR networks considering life cycle perspectives. Environmental Impact Assessment Review 2021, 90, 106608 .

AMA Style

Panrawee Rungskunroch, Zuo-Jun Shen, Sakdirat Kaewunruen. Benchmarking environmental and economic impacts from the HSR networks considering life cycle perspectives. Environmental Impact Assessment Review. 2021; 90 ():106608.

Chicago/Turabian Style

Panrawee Rungskunroch; Zuo-Jun Shen; Sakdirat Kaewunruen. 2021. "Benchmarking environmental and economic impacts from the HSR networks considering life cycle perspectives." Environmental Impact Assessment Review 90, no. : 106608.

Journal article
Published: 20 May 2021 in Engineering Failure Analysis
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The main functions of railway sleeper are: to transfer loads from axles to foundation; to hold the rails at proper gauge; to restrain movements of rails. Therefore, it’s very important to maintain the geometry of railway sleepers. The long-term performance can be significantly influenced by time-dependent behaviour. The creep and shrinkage result in deformation which change rail gauge and influence the safety and reliability of track components. Time-dependent behaviour also leads to internal pressure increasing, which could cause cracking on prestressed concrete railway sleepers. In this paper, a numerical study and experiment are conducted to evaluate time-dependent behaviour on prestressed concrete sleeper. A theoretical calculation method is also introduced and the theoretical results are compared with the experimental and numerical results. The results of creep and shrinkage are presented and discussed in this paper.

ACS Style

Dan Li; Sakdirat Kaewunruen; Ruilin You. Time-dependent behaviours of railway prestressed concrete sleepers in a track system. Engineering Failure Analysis 2021, 127, 105500 .

AMA Style

Dan Li, Sakdirat Kaewunruen, Ruilin You. Time-dependent behaviours of railway prestressed concrete sleepers in a track system. Engineering Failure Analysis. 2021; 127 ():105500.

Chicago/Turabian Style

Dan Li; Sakdirat Kaewunruen; Ruilin You. 2021. "Time-dependent behaviours of railway prestressed concrete sleepers in a track system." Engineering Failure Analysis 127, no. : 105500.

Journal article
Published: 04 May 2021 in Engineering Failure Analysis
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The definition of twist is used in modern railways to determine the warpage of a particular track plane to identify track quality. In some cases, twist is intentionally introduced on tracks to facilitate motion in curves. Nevertheless, twists values above certain thresholds, twist faults, are a direct risk to safety and a potential cause for derailments. Twist faults are commonly observed in ballasted tracks, which consists of crushed rock particles and have low endurance to resist against dynamic track forces. In general, the deterioration in ballast structure has slow progress. However, in reality, there are some catalysts such as extreme events that can speed up the deterioration of the ballast bed. Extreme events have rare occurrences but a high potential to damage structures and environment in a short duration. Even though the adjective ‘rare’ is still used to define extreme events, a consensus among the environmental scientists on the increased frequency of extreme events could be found in the literature. In this study, the impacts of flooding, one of the most common extreme events, on the dynamic behavior of a turnout structure is investigated in terms of dynamic twist. The reason to select a turnout as a basis for the simulation is the asymmetrical structure of turnout that is expected to amplify twist values. A 3-dimensional finite element method (FEM) model was developed and many hypothetical scenarios ranging from various materials to vehicle speeds were tested in FEM environment. It should be emphasized that the developed model is the modified version of a previously validated model and therefore, validation of the model is done by a comparison with the parent model. The results of the simulations, first time, show that the performance of ‘fiber-reinforced foamed urethane’ (FFU) bearers is relatively poor in comparison to concrete bearers in terms of twist values. Results also demonstrate that partially damaged structures in the case of flooding is the most critical situation. Regarding the limitations in FEM modelling, it is recommended to halt any railway operations and avoid the approach of ‘reach the station first’ in emergency cases.

ACS Style

Mehmet Hamarat; Mayorkinos Papaelias; Sakdirat Kaewunruen. Train-track interactions over vulnerable railway turnout systems exposed to flooding conditions. Engineering Failure Analysis 2021, 127, 105459 .

AMA Style

Mehmet Hamarat, Mayorkinos Papaelias, Sakdirat Kaewunruen. Train-track interactions over vulnerable railway turnout systems exposed to flooding conditions. Engineering Failure Analysis. 2021; 127 ():105459.

Chicago/Turabian Style

Mehmet Hamarat; Mayorkinos Papaelias; Sakdirat Kaewunruen. 2021. "Train-track interactions over vulnerable railway turnout systems exposed to flooding conditions." Engineering Failure Analysis 127, no. : 105459.

Journal article
Published: 30 April 2021 in Infrastructures
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High speed rail (HSR) networks have been an essential catalyst in stimulating and balancing regional economic growth that ultimately benefits the society as a whole. Previous studies have revealed that HSR services sustainably yield superior social values for people, especially for adults and those of working age. This has become an advantage of HSR networks over other forms of public transportation. The Shinkansen network in Japan is one of most successful HSR models. Its services bring significant social advantages to the communities it serves, such as shorter travel times and increased job opportunities. Nevertheless, the societal impact of HSR networks depends on many factors, and the benefits of HSR could also be overrated. The goal of this research is to measure the socioeconomic impacts of HSR on people of all genders and age groups. The outcomes could lead to more suitable development of HSR projects and policies. This study investigates data sets for Japanese social factors over 55 years in order to determine the impacts of HSR. The assessment model has been established using Python. It applies Pearson’s correlation (PCC) technique as its main methodology. This study broadly assesses social impacts on population dynamics, education, age dependency, job opportunities, and mortality rate using an unparalleled dataset spanning 55 years of social factors. The results exhibit that younger generations have the most benefits in terms of equal educational accessibility. However, the growth of the HSR network does not influence an increase in the employment rate or labour force numbers, resulting in little benefit to the workforce.

ACS Style

Panrawee Rungskunroch; Anson Jack; Sakdirat Kaewunruen. Socioeconomic Benefits of the Shinkansen Network. Infrastructures 2021, 6, 68 .

AMA Style

Panrawee Rungskunroch, Anson Jack, Sakdirat Kaewunruen. Socioeconomic Benefits of the Shinkansen Network. Infrastructures. 2021; 6 (5):68.

Chicago/Turabian Style

Panrawee Rungskunroch; Anson Jack; Sakdirat Kaewunruen. 2021. "Socioeconomic Benefits of the Shinkansen Network." Infrastructures 6, no. 5: 68.

Opinion article
Published: 29 April 2021 in Frontiers in Built Environment
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In the past several years, global warming has caused essential issues that all sectors must respond immediately. The Paris Agreement has turned into a critical framework provoking public and private sectors worldwide (Dimitrov, 2016; Pye et al., 2017). One approach to solving this problem involves the use of green energies and reducing CO2 emitted from all sectors. Regarding the transportation sectors, it emitted CO2 above one-fourth of the global emission. A well-known problem with over emission is that some countries have inadequate public transportation and non-environmental policies. The low-fare service and high accessibility on public transit are major strategies to reduce emission from a private car (Krishnan et al., 2015; De Andrade and D’Agosto, 2016). These schemes eventually promote a long-term shift from self-vehicle to public transportation services. The United Kingdom government has been concerned with the global warming issue and provided new strategies to reduce CO2 emission in all sectors, such as launching new public transportation (Kaewunruen et al., 2018; Logan et al., 2020). Additionally, the United Kingdom’s railway network is considered the lowest CO2 emission per passenger over other public services. Regarding the global climate policies, the United Kingdom government has still intended to cut off the railway’s emission by replacing it with alternative fuels and changing the current diesel engine system toward the decarbonization concept, mainly reducing the emission from the operational process. Even though the CO2 emission from the railway’s life cycle predominantly comes from the railway infrastructure, the United Kingdom government and rail sectors exceptionally focus on reducing those emissions from the operational stage. In this research, the authors believe that only promoting strategies to reduce CO2 in the operational process cannot bring the United Kingdom government to reach its targets by 2050. In contrast, the government should also consider other effective and practical strategies. In order to understand the amount of CO2 emission from the railway network, the research deeply examines the entire life cycle analysis (LCA) through the high-speed rail (HSR)’s infrastructure. This research aims to provide future strategies and policies to cut the CO2 off the railway network. Furthermore, the decarbonization concepts and practical approaches to the railway system have been stated in this study. The Paris Agreement has been launched as a global environmental policy. It targets to keep global temperature below 1.5°C compared with the pre-industrial era (Dimitrov, 2016; Streck et al., 2016). The agreement has been involved in the transportation industry, especially on railway and HSR networks. Implementing environmental concepts becomes a challenging issue for researchers and engineers in the railway industry. The International Union of Railways (UIC)’s report states that transportation shared 24.7% of CO2 emission or 8 billion tCO2. The railway sector, well-known as the lowest CO2 emitter, has produced 26.64 million tCO2 across the European countries (Korea Ministry of Land, 2008; Kaewunruen et al., 2016; UIC, 2017). Also, many attempts to reduce global emission have been applied to the railway industry. There were several collaborations among railway industries, operators, policymakers, and other related sections to respond to governmental policies. Based on the United Kingdom’s targets to achieve net-zero by 2050 and reduce 80% of greenhouse gases (GHGs) emissions relative to 1990 levels (GOVUK, 2019; ORR, 2019). The principal of the net-zero refers to the balance between the emitted GHGs and their amount in the atmosphere. The discharged and taken out GHGs can be equivalent by using high technologies such as carbon capture and carbon storage (Bonsu, 2020). In fact, the GHGs are mainly composed of CO2; hence, the amount of emission is commonly measured in the CO2 unit. Moreover, the government launches the net-zero plan to produce low carbon industries across the United Kingdom. Nevertheless, the strategies are unable to bring the United Kingdom to reach its targets due to a lack of reducing CO2 emissions in other sections such as infrastructural emission. Department for Transport (DfT)’s report (2020) reveals that the decarbonization plan decreases 43% of emissions while increasing the country’s economy by 75%. It illustrates significantly advanced progress over other sectors. The Rail Safety and Standards Board (RSSB) states that the United Kingdom’s rail system has a high potential to provide net-zero carbon by 2050 (ORR, 2019; LSE, 2020). The statement confirms the United Kingdom’s rail network in response to European’s policy toward a decarbonizing framework. Following the global guidelines, the United Kingdom’s government has proposed a vision to remove all diesel trains from the network by 2040. According to the United Kingdom railway network, it has been widely linked across the country in a total of 16,209 km of distance (Stittle, 2004; Network Rail, 2020). The railway takes 2% market share of public transportation and 10% of passenger mile traveled in Great Britain. The transport sector’s emission shows at 28%, which the rail sector shared 3% of total transport’s emission (Power et al., 2016; ORR, 2019; RSSB, 2019). The rail network is expected to use electrical and other renewable sources as alternative energy. Moreover, an effective plan to reduce CO2 from the network is adopted in all related rail activities, that is, using zero-carbon self-powered vehicles, increasing energy efficiency, and reducing pollution emissions. The decarbonization concept refers to the termination of CO2 emission from fossil fuel (RSSB, 2019). The government plans to operate an entire network without CO2 emission by 2050. All diesel trains will not be allowed in the...

ACS Style

Panrawee Rungskunroch; Zuo-Jun Shen; Sakdirat Kaewunruen. Getting It Right on the Policy Prioritization for Rail Decarbonization: Evidence From Whole-Life CO2e Emissions of Railway Systems. Frontiers in Built Environment 2021, 7, 1 .

AMA Style

Panrawee Rungskunroch, Zuo-Jun Shen, Sakdirat Kaewunruen. Getting It Right on the Policy Prioritization for Rail Decarbonization: Evidence From Whole-Life CO2e Emissions of Railway Systems. Frontiers in Built Environment. 2021; 7 ():1.

Chicago/Turabian Style

Panrawee Rungskunroch; Zuo-Jun Shen; Sakdirat Kaewunruen. 2021. "Getting It Right on the Policy Prioritization for Rail Decarbonization: Evidence From Whole-Life CO2e Emissions of Railway Systems." Frontiers in Built Environment 7, no. : 1.

Journal article
Published: 16 April 2021 in Reliability Engineering & System Safety
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Not only has the railway accidental prevention been a prime focus, but it has also become a key challenge for the industry in recent years. For many decades, rail authorities have attempted to significantly improve rail safety, whilst facing various passengers’ risks and uncertainties. The overarching goal of this study is to develop a new posterior probability model to quantify uncertainties for benchmarking. This is the world's first to establish new insights from the benchmarking of risk and safety across different rail networks. The insights will point out the advantages and practicability of launching safety policies and reducing railway accidents for other rail networks. The new model has been developed using unparalleled long-term accidental data sets, including ‘a trailer an accident’ and ‘causes of the accident’. The investigation adopts a Bayesian approach (via Python) to codify the novel model. The new findings lead to the better understanding into the uncertainty of railway accidents. Five notable rail networks have been selected as case studies. This study has also compared the effectiveness of the decision tree and Petri-net models using the posterior probability and number of injuries and fatalities. Based on the benchmarking outcomes, Chinese and Japanese railway systems denote the lowest risk over other networks, followed by Spanish, French and South Korean rail networks. The study also demonstrates that the novel benchmarking criteria can effectively measure and compare any rail networks’ risk and uncertainties. Its adoption will lead to performance improvement in terms of safety, reliability and maintenance policies of railway networks globally.

ACS Style

Panrawee Rungskunroch; Anson Jack; Sakdirat Kaewunruen. Benchmarking on railway safety performance using Bayesian inference, decision tree and petri-net techniques based on long-term accidental data sets. Reliability Engineering & System Safety 2021, 213, 107684 .

AMA Style

Panrawee Rungskunroch, Anson Jack, Sakdirat Kaewunruen. Benchmarking on railway safety performance using Bayesian inference, decision tree and petri-net techniques based on long-term accidental data sets. Reliability Engineering & System Safety. 2021; 213 ():107684.

Chicago/Turabian Style

Panrawee Rungskunroch; Anson Jack; Sakdirat Kaewunruen. 2021. "Benchmarking on railway safety performance using Bayesian inference, decision tree and petri-net techniques based on long-term accidental data sets." Reliability Engineering & System Safety 213, no. : 107684.

Journal article
Published: 07 April 2021 in Vibration
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Various techniques have been developed to detect railway defects. One of the popular techniques is machine learning. This unprecedented study applies deep learning, which is a branch of machine learning techniques, to detect and evaluate the severity of rail combined defects. The combined defects in the study are settlement and dipped joint. Features used to detect and evaluate the severity of combined defects are axle box accelerations simulated using a verified rolling stock dynamic behavior simulation called D-Track. A total of 1650 simulations are run to generate numerical data. Deep learning techniques used in the study are deep neural network (DNN), convolutional neural network (CNN), and recurrent neural network (RNN). Simulated data are used in two ways: simplified data and raw data. Simplified data are used to develop the DNN model, while raw data are used to develop the CNN and RNN model. For simplified data, features are extracted from raw data, which are the weight of rolling stock, the speed of rolling stock, and three peak and bottom accelerations from two wheels of rolling stock. In total, there are 14 features used as simplified data for developing the DNN model. For raw data, time-domain accelerations are used directly to develop the CNN and RNN models without processing and data extraction. Hyperparameter tuning is performed to ensure that the performance of each model is optimized. Grid search is used for performing hyperparameter tuning. To detect the combined defects, the study proposes two approaches. The first approach uses one model to detect settlement and dipped joint, and the second approach uses two models to detect settlement and dipped joint separately. The results show that the CNN models of both approaches provide the same accuracy of 99%, so one model is good enough to detect settlement and dipped joint. To evaluate the severity of the combined defects, the study applies classification and regression concepts. Classification is used to evaluate the severity by categorizing defects into light, medium, and severe classes, and regression is used to estimate the size of defects. From the study, the CNN model is suitable for evaluating dipped joint severity with an accuracy of 84% and mean absolute error (MAE) of 1.25 mm, and the RNN model is suitable for evaluating settlement severity with an accuracy of 99% and mean absolute error (MAE) of 1.58 mm.

ACS Style

Jessada Sresakoolchai; Sakdirat Kaewunruen. Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning. Vibration 2021, 4, 341 -356.

AMA Style

Jessada Sresakoolchai, Sakdirat Kaewunruen. Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning. Vibration. 2021; 4 (2):341-356.

Chicago/Turabian Style

Jessada Sresakoolchai; Sakdirat Kaewunruen. 2021. "Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning." Vibration 4, no. 2: 341-356.

Journal article
Published: 22 March 2021 in Computers & Structures
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In a conventional railway system, timber sleepers have been widely used for ballasted railway tracks to carry passengers and transport goods. However, due to the limited availability of reliable and high-quality timbers, and restrictions on deforestation, the “interspersed” approach is adopted to replace ageing timbers with concrete sleepers. The replacement of ageing timber sleepers is frequently done over old and soft existing formations, which have been in service for so long, by installing new stiff concrete sleepers in their place. This method provides a cost-effective and quick solution for the second and third track classes to maintain track quality. Presently, railway track buckling, caused by extreme temperature, is a serious issue that causes a huge loss of assets in railway systems. The increase in rail temperature can induce a compression force in the continuous welded rail (CWR) and this may cause track buckling when the compression force reaches the buckling strength. According to the buckling evidences seen around the world, buckling usually occurs in ballasted track with timber sleepers and thus there is a clear need to improve the buckling resistance of railway tracks. However, the buckling of interspersed tracks has not been fully studied. This unprecedented study highlights 3D finite element modelling of interspersed railway tracks subjected to temperature change. The effect of the boundary conditions on the buckling shape is investigated. The results show that the interspersed approach may reduce the likelihood of track buckling. The results can be used to predict the buckling temperature and to inspect the conditions of interspersed railway tracks. The new findings highlight the buckling phenomena of interspersed railway tracks, which are usually adopted during railway transformations from timber to concrete sleepered tracks in real-life practices globally. The insight into interspersed railway tracks derived from this study will underpin the life cycle design, maintenance, and construction strategies related to the use of concrete sleepers as spot replacement sleepers in ageing railway track systems.

ACS Style

Chayut Ngamkhanong; Sakdirat Kaewunruen; Charalampos Baniotopoulos. Nonlinear buckling instabilities of interspersed railway tracks. Computers & Structures 2021, 249, 106516 .

AMA Style

Chayut Ngamkhanong, Sakdirat Kaewunruen, Charalampos Baniotopoulos. Nonlinear buckling instabilities of interspersed railway tracks. Computers & Structures. 2021; 249 ():106516.

Chicago/Turabian Style

Chayut Ngamkhanong; Sakdirat Kaewunruen; Charalampos Baniotopoulos. 2021. "Nonlinear buckling instabilities of interspersed railway tracks." Computers & Structures 249, no. : 106516.

Opinion article
Published: 24 February 2021 in Frontiers in Built Environment
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Furthermore, AI applications have been used in the field to detect and implement social distancing, the wearing of facial coverings and to detect whether workers are wearing their protective equipment (PPE) such as helmet or gloves (Wykle & Van Hecke, 2020). Also, it may even be possible to forecast the next epidemic from the COVID-19 history mobile network data. In these few lines we ask the question as to whether, in the context of COVID-19, the railway industry could employ new technology such as AI and related approaches (IoT, 5G, big data, AI) for protecting and underpinning railway safety. Acknowledging already the solutions and potential measures that been applied such as cleaning, disinfection, sanitisation, redesign, physical and social distancing, re-layout, ATP testing, or the air filtration and recycling air, we are attempting here to highlight the potential technology solutions in the industry for tackling COVID-19.The AI required massive data to learn and predict, which is available as raw data in the field but needs effort and structural strategies for gathering suitable data. The information is captured through indicators or indices, which creates the rules depending on the inputs. The necessary inputs can be collected from devices (such as sensor-based programming or CCTV), and the estimate can be calculated with the AI methods, generated from servers, for instance, at the railway stations. Detecting the possibly affected people is key to tackling the spre...

ACS Style

Hamad Alawad; Sakdirat Kaewunruen. 5G Intelligence Underpinning Railway Safety in the COVID-19 Era. Frontiers in Built Environment 2021, 7, 1 .

AMA Style

Hamad Alawad, Sakdirat Kaewunruen. 5G Intelligence Underpinning Railway Safety in the COVID-19 Era. Frontiers in Built Environment. 2021; 7 ():1.

Chicago/Turabian Style

Hamad Alawad; Sakdirat Kaewunruen. 2021. "5G Intelligence Underpinning Railway Safety in the COVID-19 Era." Frontiers in Built Environment 7, no. : 1.

Journal article
Published: 14 February 2021 in Sustainability
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Over the past centuries, millions of bridge infrastructures have been constructed globally. Many of those bridges are ageing and exhibit significant potential risks. Frequent risk-based inspection and maintenance management of highway bridges is particularly essential for public safety. At present, most bridges rely on manual inspection methods for management. The efficiency is extremely low, causing the risk of bridge deterioration and defects to increase day by day, reducing the load-bearing capacity of bridges, and restricting the normal and safe use of them. At present, the applications of digital twins in the construction industry have gained significant momentum and the industry has gradually entered the information age. In order to obtain and share relevant information, engineers and decision makers have adopted digital twins over the entire life cycle of a project, but their applications are still limited to data sharing and visualization. This study has further demonstrated the unprecedented applications of digital twins to sustainability and vulnerability assessments, which can enable the next generation risk-based inspection and maintenance framework. This study adopts the data obtained from a constructor of Zhongcheng Village Bridge in Zhejiang Province, China as a case study. The applications of digital twins to bridge model establishment, information collection and sharing, data processing, inspection and maintenance planning have been highlighted. Then, the integration of “digital twins (or Building Information Modelling, BIM) + bridge risk inspection model” has been established, which will become a more effective information platform for all stakeholders to mitigate risks and uncertainties of exposure to extreme weather conditions over the entire life cycle.

ACS Style

Sakdirat Kaewunruen; Jessada Sresakoolchai; Wentao Ma; Olisa Phil-Ebosie. Digital Twin Aided Vulnerability Assessment and Risk-Based Maintenance Planning of Bridge Infrastructures Exposed to Extreme Conditions. Sustainability 2021, 13, 2051 .

AMA Style

Sakdirat Kaewunruen, Jessada Sresakoolchai, Wentao Ma, Olisa Phil-Ebosie. Digital Twin Aided Vulnerability Assessment and Risk-Based Maintenance Planning of Bridge Infrastructures Exposed to Extreme Conditions. Sustainability. 2021; 13 (4):2051.

Chicago/Turabian Style

Sakdirat Kaewunruen; Jessada Sresakoolchai; Wentao Ma; Olisa Phil-Ebosie. 2021. "Digital Twin Aided Vulnerability Assessment and Risk-Based Maintenance Planning of Bridge Infrastructures Exposed to Extreme Conditions." Sustainability 13, no. 4: 2051.

Journal article
Published: 04 February 2021 in Sustainability
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Not only can waste rubber enhance the properties of concrete (e.g., its dynamic damping and abrasion resistance capacity), its rational utilisation can also dramatically reduce environmental pollution and carbon footprint globally. This study is the world’s first to develop a novel machine learning-aided design and prediction of environmentally friendly concrete using waste rubber, which can drive sustainable development of infrastructure systems towards net-zero emission, which saves time and cost. In this study, artificial neuron networks (ANN) have been established to determine the design relationship between various concrete mix composites and their multiple mechanical properties simultaneously. Interestingly, it is found that almost all previous studies on the ANNs could only predict one kind of mechanical property. To enable multiple mechanical property predictions, ANN models with various architectural algorithms, hidden neurons and layers are built and tailored for benchmarking in this study. Comprehensively, all three hundred and fifty-three experimental data sets of rubberised concrete available in the open literature have been collected. In this study, the mechanical properties in focus consist of the compressive strength at day 7 (CS7), the compressive strength at day 28 (CS28), the flexural strength (FS), the tensile strength (TS) and the elastic modulus (EM). The optimal ANN architecture has been identified by customising and benchmarking the algorithms (Levenberg–Marquardt (LM), Bayesian Regularisation (BR) and Scaled Conjugate Gradient (SCG)), hidden layers (1–2) and hidden neurons (1–30). The performance of the optimal ANN architecture has been assessed by employing the mean squared error (MSE) and the coefficient of determination (R2). In addition, the prediction accuracy of the optimal ANN model has ben compared with that of the multiple linear regression (MLR).

ACS Style

Xu Huang; Jiaqi Zhang; Jessada Sresakoolchai; Sakdirat Kaewunruen. Machine Learning Aided Design and Prediction of Environmentally Friendly Rubberised Concrete. Sustainability 2021, 13, 1691 .

AMA Style

Xu Huang, Jiaqi Zhang, Jessada Sresakoolchai, Sakdirat Kaewunruen. Machine Learning Aided Design and Prediction of Environmentally Friendly Rubberised Concrete. Sustainability. 2021; 13 (4):1691.

Chicago/Turabian Style

Xu Huang; Jiaqi Zhang; Jessada Sresakoolchai; Sakdirat Kaewunruen. 2021. "Machine Learning Aided Design and Prediction of Environmentally Friendly Rubberised Concrete." Sustainability 13, no. 4: 1691.

Journal article
Published: 30 January 2021 in Construction and Building Materials
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Track lateral stability is one of the most critical considerations for safe and reliable railway infrastructures. With increasing exposures to high temperatures globally, a greater expansion in continuous welded rails can induce a higher risk of track buckling, especially when track defects exist. In ballasted track structures, ballast layer holds sleepers in place and provides lateral support and stiffness to the track. Note that there are several factors influencing the lateral resistance of ballasted railway tracks. However, the effects of the progressive degradation of the ballast on the track’s lateral resistance have thus far never been fully investigated. Note that the fouling conditions can be due to the accumulation of ballast breakage or external contamination, such as subgrade intrusion or coal dust, and difficult to inspect in the field. It is evidenced that track buckling can incur even if the railway track and ballast seem to be in a good condition by visual inspection. Therefore, this paper presents a more realistic model to study Single Sleeper (Tie) Push Test (STPT) conditions using the Discrete Element Method (DEM) with the objective to evaluate ballasted track lateral resistance considering different fouling scenarios. Note that coal dust, acting as a lubricant, is considered as a fouling agent. The lateral force–displacement curves of sleepers are analysed. The lateral force is derived from the sleeper-ballast contact forces obtained from three main components: sleeper bottom friction, sleeper side friction, and sleeper end force. The fouling conditions are employed by adapting appropriate model parameters to the ballast layer that represents the fouled ballast condition by coal dust in the DEM simulations. Note that the fouling layer is considered to start from the bottom of the ballast layer and is applied all the way to the top to represent the completely fouled ballast layer condition. The results indicate that fouled ballast can significantly undermine the lateral stability of ballasted tracks by more than about 50%. Track lateral stiffness may be reduced significantly due to fouled ballast layer conditions that cannot be inspected visually in the field. This may reduce track restraint and increase the likelihood of track buckling even though the degraded ballast does not have direct contact with the sleeper. Finally, the study will enrich the development of inspection criteria for ballast lateral resistance and support conditions, improve safety and reliability of rail network, and mitigate the risk of delays due to track buckling leading to unplanned maintenance.

ACS Style

Chayut Ngamkhanong; Bin Feng; Erol Tutumluer; Youssef M.A. Hashash; Sakdirat Kaewunruen. Evaluation of lateral stability of railway tracks due to ballast degradation. Construction and Building Materials 2021, 278, 122342 .

AMA Style

Chayut Ngamkhanong, Bin Feng, Erol Tutumluer, Youssef M.A. Hashash, Sakdirat Kaewunruen. Evaluation of lateral stability of railway tracks due to ballast degradation. Construction and Building Materials. 2021; 278 ():122342.

Chicago/Turabian Style

Chayut Ngamkhanong; Bin Feng; Erol Tutumluer; Youssef M.A. Hashash; Sakdirat Kaewunruen. 2021. "Evaluation of lateral stability of railway tracks due to ballast degradation." Construction and Building Materials 278, no. : 122342.