This page has only limited features, please log in for full access.

Unclaimed
Jessada Sresakoolchai
Department of Civil Engineering, University of Birmingham, Birmingham B15 2TT, UK

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 06 August 2021 in Climate
Reads 0
Downloads 0

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: 21 July 2021 in Materials
Reads 0
Downloads 0

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.

Journal article
Published: 07 April 2021 in Vibration
Reads 0
Downloads 0

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: 14 February 2021 in Sustainability
Reads 0
Downloads 0

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
Reads 0
Downloads 0

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: 16 September 2020 in Applied Sciences
Reads 0
Downloads 0

Global warming is a critical issue nowadays. Although the railway system is considered as green transportation, it cannot be denied that railway tunnels have a significant environmental impact during construction and maintenance. At the same time, asset management of a project becomes more popular in project analysis. Therefore, this study aims to analyse life-cycle cost (LCC) and life-cycle assessment (LCA) for the Xikema No. 1 high-speed railway tunnel in China to consider the environmental impact of rail tunnel construction. The initial capital costs of tunnel and rail construction, operation, and maintenance costs have been separately considered in terms of the life-cycle cost analysis and net present value (NPV) with various discount rates. The LCA analysis has presented the CO2 emissions and energy consumption over the construction and operation processes into consideration. The CO2 emissions and energy consumption caused by material production, maintenance, and material transportation have been accounted for. The results show that the materials used during the construction process contribute to about 97.1% of CO2 emissions of the life-cycle while CO2 emissions caused by the operation and maintenance process are relatively small compared with the construction process. Moreover, the maintenance process consumes over 55% of the life-cycle energy. The energy consumption of the tunnel construction process is approximately 44.3%. At the same time, the construction contributes to the main proportion of LCC due to relatively low cost in the operation and maintenance stages.

ACS Style

Sakdirat Kaewunruen; Jessada Sresakoolchai; Shuonan Yu. Global Warming Potentials Due to Railway Tunnel Construction and Maintenance. Applied Sciences 2020, 10, 6459 .

AMA Style

Sakdirat Kaewunruen, Jessada Sresakoolchai, Shuonan Yu. Global Warming Potentials Due to Railway Tunnel Construction and Maintenance. Applied Sciences. 2020; 10 (18):6459.

Chicago/Turabian Style

Sakdirat Kaewunruen; Jessada Sresakoolchai; Shuonan Yu. 2020. "Global Warming Potentials Due to Railway Tunnel Construction and Maintenance." Applied Sciences 10, no. 18: 6459.

Journal article
Published: 17 May 2020 in Transportation Research Interdisciplinary Perspectives
Reads 0
Downloads 0

At present, PPP (Public-Private Partnership) plays an important role in infrastructure project development. This is attributable to the fact that many governments around the world have a budget constraint and may try to prioritize their budget for other developments in need. Allowing a private sector to participate in investment is an important step towards the cost-saving for a government. The private sector has an advantageous opportunity in the investment partnership, and public users tend to have superior service. Thai government envisages potential advantages of PPP and adopts this practice in various projects. However, the new mega project linking 3 airports is one of the first highspeed rail projects in Thailand of which the Thai government has insufficient experience. There are serious concerns whether the PPP adoption could enable a viable option. Therefore, this study aims to analyze benefits and risks of PPP adoption in the High-Speed Rail Project Linking 3 Airports in Thailand. Lifecycle assessment has been carried out by breaking down the project into various phases. Field data have been gathered from different sources such as an official website, feasibility study reports, annual reports of related government agencies, and opinions from technical experts in private sector. Financial analysis is used to analyze and calculate related financial values. The results reveal that the PPP adoption in this project yields different benefits and risks depending on each phase of the project. Adopting PPP can overcome key limitations and provide some real benefits that the traditional approach cannot. Simultaneously, there are risks incurred from the PPP adoption due to the complexity in PPP partnership such additional transaction costs and interrelation complexity. However, the risks can be managed by a rigorous plan and practice. Both governmental and private sectors need to collaborate to ascertain the project's success.

ACS Style

Jessada Sresakoolchai; Sakdirat Kaewunruen. Comparative studies into public private partnership and traditional investment approaches on the high-speed rail project linking 3 airports in Thailand. Transportation Research Interdisciplinary Perspectives 2020, 5, 100116 .

AMA Style

Jessada Sresakoolchai, Sakdirat Kaewunruen. Comparative studies into public private partnership and traditional investment approaches on the high-speed rail project linking 3 airports in Thailand. Transportation Research Interdisciplinary Perspectives. 2020; 5 ():100116.

Chicago/Turabian Style

Jessada Sresakoolchai; Sakdirat Kaewunruen. 2020. "Comparative studies into public private partnership and traditional investment approaches on the high-speed rail project linking 3 airports in Thailand." Transportation Research Interdisciplinary Perspectives 5, no. : 100116.

Journal article
Published: 20 March 2020 in Sustainability
Reads 0
Downloads 0

A number of bridge infrastructures are rising significantly due to economic expansion and growing numbers of railway and road infrastructures. Owing to the complexity of bridge design, traditional design methods always create tedious and time-consuming construction processes. In recent years, Building Information Modelling (BIM) has been developed rapidly to provide a faster solution to generate and process the integration of information in a shared environment. This paper aims to highlight an innovative 6D BIM approach for the lifecycle asset management of a bridge infrastructure by using Donggou Bridge as a case study. This paper adopts 6D modelling, incorporating 3D model information with time schedule, cost estimation, and carbon footprint analysis across the lifecycle of the bridge project. The results of this paper reveal that raw materials contribute the most embodied carbon emissions, and as the 6D BIM model was developed in the early stage of the lifecycle, stakeholders can collaborate within the BIM environment to enhance a more sustainable and cost-effective outcome in advance. This study also demonstrates the possibility of BIM applications to bridge infrastructure projects throughout the whole lifecycle. The 6D BIM can save time by transforming 2D information to 3D information and reducing errors during the pre-construction and construction stages through better visualisation for staff training. Moreover, 6D BIM can promote efficient asset and project management since it can be applied for various purposes simultaneously, such as sustainability, lifecycle asset management and maintenance, condition monitoring and real-time structural simulations. In addition, BIM can promote cooperation among working parties and improve visualisation of the project for various stakeholders.

ACS Style

Sakdirat Kaewunruen; Jessada Sresakoolchai; Zhihao Zhou. Sustainability-Based Lifecycle Management for Bridge Infrastructure Using 6D BIM. Sustainability 2020, 12, 2436 .

AMA Style

Sakdirat Kaewunruen, Jessada Sresakoolchai, Zhihao Zhou. Sustainability-Based Lifecycle Management for Bridge Infrastructure Using 6D BIM. Sustainability. 2020; 12 (6):2436.

Chicago/Turabian Style

Sakdirat Kaewunruen; Jessada Sresakoolchai; Zhihao Zhou. 2020. "Sustainability-Based Lifecycle Management for Bridge Infrastructure Using 6D BIM." Sustainability 12, no. 6: 2436.

Journal article
Published: 25 December 2019 in Sustainability
Reads 0
Downloads 0

The Beijing-Shanghai High-Speed Railway (HSR) is one of the most important railways in China, but it also has impacts on the economy and the environment while creating social benefits. This paper uses a life cycle assessment (LCA) method and a life cycle cost (LCC) analysis method to summarize the energy consumption, carbon emissions and costs of the Beijing-Shanghai HSR from the perspective of life cycle, and proposes some corresponding suggestions based on the results. The research objective of this paper is to analyse the carbon emissions, energy consumption, and costs of the rail system which includes the structure of the track and earthwork of the Beijing-Shanghai HSR during four stages: conception stage, construction stage, operation and maintenance stage, and disposal stage. It is concluded that the majority of the carbon emissions and energy consumption of the entire rail system are from the construction stage, accounting for 64.86% and 54.31% respectively. It is followed by the operation and maintenance stage with 31.60% and 35.32% respectively. In contrast, the amount of carbon emissions and energy consumption from the conception stage is too small to be considered. Furthermore, cement is the major contributor to the carbon emissions and energy consumption during the construction stage. As for the cost, the construction stage spends the largest amount of money (US$4614.00 million), followed by the operation and maintenance stage (US$910.61 million). Improving production technologies and choosing construction machinery are proposed to reduce the cost and protect the environment.

ACS Style

Sakdirat Kaewunruen; Jessada Sresakoolchai; Junying Peng. Life Cycle Cost, Energy and Carbon Assessments of Beijing-Shanghai High-Speed Railway. Sustainability 2019, 12, 206 .

AMA Style

Sakdirat Kaewunruen, Jessada Sresakoolchai, Junying Peng. Life Cycle Cost, Energy and Carbon Assessments of Beijing-Shanghai High-Speed Railway. Sustainability. 2019; 12 (1):206.

Chicago/Turabian Style

Sakdirat Kaewunruen; Jessada Sresakoolchai; Junying Peng. 2019. "Life Cycle Cost, Energy and Carbon Assessments of Beijing-Shanghai High-Speed Railway." Sustainability 12, no. 1: 206.

Case report
Published: 23 November 2019 in Sustainability
Reads 0
Downloads 0

The concept of the Net Zero Energy Building (NZEB) has received more interest from researchers due to global warming concerns. This paper proposes to illustrate optional solutions to allow existing buildings to achieve NZEB goals. The aim of this study is to investigate factors that can improve existing building performance to be in line with the NZEB concept and be more sustainable. An existing townhouse in Washington, DC was chosen as the research target to study how to retrofit or reconstruct the design of a building according to the NZEB concept. The methodology of this research is modeling an existing townhouse to assess the current situation and creating optional models for improving energy efficiency of the townhouse in Revit and utilising renewable energy technology for energy supply. This residential building was modeled in three versions to compare changes in energy performance including improving thermal efficiency of building envelope, increasing thickness of the wall, and installing smart windows (switchable windows). These solutions can reduce energy and cost by approximately 8.16%, 10.16%, and 14.65%, respectively, compared to the original townhouse. Two renewable energy technologies that were considered in this research were photovoltaic and wind systems. The methods can be applied to reconstruct other existing buildings in the future.

ACS Style

Sakdirat Kaewunruen; Jessada Sresakoolchai; Lalida Kerinnonta. Potential Reconstruction Design of an Existing Townhouse in Washington DC for Approaching Net Zero Energy Building Goal. Sustainability 2019, 11, 6631 .

AMA Style

Sakdirat Kaewunruen, Jessada Sresakoolchai, Lalida Kerinnonta. Potential Reconstruction Design of an Existing Townhouse in Washington DC for Approaching Net Zero Energy Building Goal. Sustainability. 2019; 11 (23):6631.

Chicago/Turabian Style

Sakdirat Kaewunruen; Jessada Sresakoolchai; Lalida Kerinnonta. 2019. "Potential Reconstruction Design of an Existing Townhouse in Washington DC for Approaching Net Zero Energy Building Goal." Sustainability 11, no. 23: 6631.

Dataset
Published: 30 August 2021
Reads 0
Downloads 0
ACS Style

Sakdirat Kaewunruen; Jessada Sresakoolchai; Yi-Hsuan Lin. Research data supporting the publication “Digital twins for managing railway maintenance and resilience”. 2021, 1 .

AMA Style

Sakdirat Kaewunruen, Jessada Sresakoolchai, Yi-Hsuan Lin. Research data supporting the publication “Digital twins for managing railway maintenance and resilience”. . 2021; ():1.

Chicago/Turabian Style

Sakdirat Kaewunruen; Jessada Sresakoolchai; Yi-Hsuan Lin. 2021. "Research data supporting the publication “Digital twins for managing railway maintenance and resilience”." , no. : 1.