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

Dr. Wai Chong
Arizona State University, United States

Basic Info

Basic Info is private.

Research Keywords & Expertise

0 Information Technology
0 Modeling and Simulation
0 Predictive Analytics
0 Engineering knowledge
0 Life-cycle analysis

Fingerprints

Life-cycle analysis

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

Original article
Published: 02 March 2021 in Engineering with Computers
Reads 0
Downloads 0

The range of applications of artificial intelligence (AI) that is based on nature-inspired metaheuristics is rapidly increasing across various scientific fields as it is used to solve complex engineering problems. This work develops a cloud evolutionary machine learning system, called the nature-inspired metaheuristic optimization and prediction system (NiMOPS) that is composed of metaheuristic AI and web modules. The objective of the proposed system is to provide a user-friendly web analytics for making efficient, effective, and accurate predictions as solutions to engineering problems. For the purposes of web development, this work connects two programming languages, which are MATLAB and Java. A MATLAB Compiler is used to package the system into Java Archive (JAR) files, which provide the core modules for the development of the NiMOPS website using an integrated development environment (IDE). IDE compiles the JAR files, and web utilities (JavaScript, CSS, Servlet, and other utility files) to form the response-request connection between the user and the server. Therefore, the web-based system does not require the installation of an application by the users because they can access the cloud computing system ubiquitously with a browser or mobile device. Furthermore, it has many functions, including export—import file, train model, optimize prediction, save model and visualize results. Several case studies of this system, involving classification and regression problems, were examined. The analytic results of using the system to solve classification problems revealed that the system had a fault diagnosis accuracy of 96.5% and an accidental small fire accuracy of 52.4%. In solving regression problems, the root mean square errors were 28.58–68.82% better than those of previous methods. In particular, the proposed system performed multiple performance measures that were utilized in a regression analysis and were found to be more reliable evaluation metrics than used in elsewhere. The numerical experiments verified that cloud computing provides an innovative way to enable decision-makers to solve engineering problems.

ACS Style

Jui-Sheng Chou; Jeffisa Delaosia Kosasih; Wai K. Chong. Cloud evolutionary computation system for advanced engineering analytics. Engineering with Computers 2021, 1 -25.

AMA Style

Jui-Sheng Chou, Jeffisa Delaosia Kosasih, Wai K. Chong. Cloud evolutionary computation system for advanced engineering analytics. Engineering with Computers. 2021; ():1-25.

Chicago/Turabian Style

Jui-Sheng Chou; Jeffisa Delaosia Kosasih; Wai K. Chong. 2021. "Cloud evolutionary computation system for advanced engineering analytics." Engineering with Computers , no. : 1-25.

Journal article
Published: 24 January 2020 in Sustainability
Reads 0
Downloads 0

Building energy systems are designed to handle both permanent and temporary occupants. Permanent occupants are considered the base energy load while temporary occupants are considered a temporary or additional load. Temporary occupancy is potentially the most difficult to design as the number of temporary occupants varies more significantly than permanent occupants. This case study was designed to investigate the effect of occupancy on energy loads, i.e. the relationship between occupancy and building energy loads. This study estimated the building occupancy by using existing network infrastructure, such as Wi-Fi and wired Ethernet based on the assumption that the number of Wi-Fi connections and the wired Ethernet traffic were used as a proxy for total and stationary occupancy. The relationships were then examined using correlations and regression analyses. The results showed the following: 1. Stationary occupancy was successfully estimated using the network infrastructure; 2. There was a linear relationship between electricity use and total occupancy (and, thus, the use of network infrastructure); 3. Permanent occupants generated a higher impact on the electricity load than the temporary occupants; 4. There was a logarithmic relationship between electricity use and the Ethernet data traffic (a proxy of permanent occupants); and 5. The statistical and qualitative analyses indicated that there was no significant relationship between occupancy and thermal loads, such as cooling and heating loads.

ACS Style

Seungtaek Lee; Wai Oswald Chong; Jui-Sheng Chou. Examining the Relationships between Stationary Occupancy and Building Energy Loads in US Educational Buildings–Case Study. Sustainability 2020, 12, 893 .

AMA Style

Seungtaek Lee, Wai Oswald Chong, Jui-Sheng Chou. Examining the Relationships between Stationary Occupancy and Building Energy Loads in US Educational Buildings–Case Study. Sustainability. 2020; 12 (3):893.

Chicago/Turabian Style

Seungtaek Lee; Wai Oswald Chong; Jui-Sheng Chou. 2020. "Examining the Relationships between Stationary Occupancy and Building Energy Loads in US Educational Buildings–Case Study." Sustainability 12, no. 3: 893.

Journal article
Published: 31 May 2019 in Waste Management
Reads 0
Downloads 0

The construction industry consumes 40% of the global materials and produces one of the largest waste streams in the planet. In a circular economy, the reuse of building components in multiple life cycles aims at increasing resource efficiency and eliminating waste. But can reuse offset the environmental impacts of materials with high embodied energy (e.g. steel)? If so, in what conditions? In the study presented in this paper, the authors used two different life cycle assessment (LCA) methods to compare a single-use wood-framed wall against a reusable steel-framed wall in a tiny house in the U.S. The analyzed impact categories were global warming potential, embodied energy, and water use. One of the main goals of this study was to understand the benefits of reusing a material with high embodied energy when compared to a single-use alternative. Another equally important objective was to understand how different LCA methods can influence the results in a cradle-to-cradle (C2C) LCA. As results, reuse benefits depended on aggressive reuse rates (>70%) and multiple reuses of steel were needed to offset the embodied environmental impacts during steel production. Also, the analyses showed that process-based LCA and hybrid LCA can generate conflicting results in a C2C LCA.

ACS Style

Fernanda Cruz Rios; David Grau; Wai K. Chong. Reusing exterior wall framing systems: A cradle-to-cradle comparative life cycle assessment. Waste Management 2019, 94, 120 -135.

AMA Style

Fernanda Cruz Rios, David Grau, Wai K. Chong. Reusing exterior wall framing systems: A cradle-to-cradle comparative life cycle assessment. Waste Management. 2019; 94 ():120-135.

Chicago/Turabian Style

Fernanda Cruz Rios; David Grau; Wai K. Chong. 2019. "Reusing exterior wall framing systems: A cradle-to-cradle comparative life cycle assessment." Waste Management 94, no. : 120-135.

Journal article
Published: 01 June 2018 in Journal of Urban Planning and Development
Reads 0
Downloads 0

This research examined how various green space layouts in an urban area affected the surrounding atmospheric air temperature that contributes to a heat island effect. The aim was to develop an understanding of whether this greenery arrangement had an influence on the urban heat island. Field measurements were taken for three different layouts: greenery surrounding a complex/building, greenery in the center of a complex/building, and greenery distributed over a complex/building. The study showed that in the presence of solar heat, there were significant temperature differences among the three layouts. The study showed that the sites with greenery in the middle of a complex generated the lowest urban heat island effect of the three layouts.

ACS Style

Soo-Young Moon; Jonghoon Kim; Wai K. O. Chong; Samuel T. Ariaratnam. Urban Green Space Layouts and Urban Heat Island: Case Study on Apartment Complexes in South Korea. Journal of Urban Planning and Development 2018, 144, 04018004 .

AMA Style

Soo-Young Moon, Jonghoon Kim, Wai K. O. Chong, Samuel T. Ariaratnam. Urban Green Space Layouts and Urban Heat Island: Case Study on Apartment Complexes in South Korea. Journal of Urban Planning and Development. 2018; 144 (2):04018004.

Chicago/Turabian Style

Soo-Young Moon; Jonghoon Kim; Wai K. O. Chong; Samuel T. Ariaratnam. 2018. "Urban Green Space Layouts and Urban Heat Island: Case Study on Apartment Complexes in South Korea." Journal of Urban Planning and Development 144, no. 2: 04018004.

Conference paper
Published: 29 March 2018 in Construction Research Congress 2018
Reads 0
Downloads 0

The Construction sector uses 40% of the earth’s resources, much of which ends up as “wastes” from our civilization. We can reduce resource use and eliminate demolition waste by simply reusing building materials. Some building components are easy to take apart and reuse while others require additional costs and effort. Some generate more environmental impacts during their recycling. The paper presents a study on understanding the lifecycle impact of recycling different building components and materials, thus allowing the industry to better understand the true lifecycle environmental impacts of reuse and recycling. The study compares the embodied energy, global warming potential, and water use of a wood frame and a steel frame for a manufactured home in the United States. The analysis assumes the wood frame would be demolished and rebuilt for three life cycles, while the steel frame was assumed to be continuously reused. The analysis is based on process-based life cycle analysis (LCA) and hybrid-LCA. Considerations on transportation distances and reuse rates were made. The analyses showed that, by using a cradle-to-cradle (C2C) framework, both methods generate conflicting results. The impact of the results to manufacturers, designers, policy-makers, building owners, and researchers are discussed.

ACS Style

Fernanda Cruz Rios; David Grau; Wai K. Chong. Steel or Wood Frame? A Life Cycle Comparison of External Wall Systems through Deconstruction and Reuse. Construction Research Congress 2018 2018, 1 .

AMA Style

Fernanda Cruz Rios, David Grau, Wai K. Chong. Steel or Wood Frame? A Life Cycle Comparison of External Wall Systems through Deconstruction and Reuse. Construction Research Congress 2018. 2018; ():1.

Chicago/Turabian Style

Fernanda Cruz Rios; David Grau; Wai K. Chong. 2018. "Steel or Wood Frame? A Life Cycle Comparison of External Wall Systems through Deconstruction and Reuse." Construction Research Congress 2018 , no. : 1.

Journal article
Published: 01 October 2017 in Journal of Energy Engineering
Reads 0
Downloads 0

Buildings are the largest consumer of energy in the United States from various sectors that includes transportation, industry, commercial, and residential buildings. Leadership in Energy and Environmental Design (LEED) certification program, home energy rating system (HERS), and American Society of Heating, Refrigerating and Air-conditioning Engineers (ASHRAE) standards are developed to improve the energy efficiency of the commercial and residential buildings. However, these programs, codes, and standards are used before or during the design and construction phases. For this reason, it is challenging to track whether buildings still could be energy efficient post construction. The primary purpose of this study was to detect the anomalies from the energy consumption dataset of LEED institutional buildings. The anomalies are identified using two different data mining techniques, which are clustering, and isolation Forest (iForest). This paper demonstrates an integrated data mining approach that helps in evaluating LEED energy and atmosphere (EA) credits after construction.

ACS Style

Jonghoon Kim; Hariharan Naganathan; Soo-Young Moon; Wai K. O. Chong; Samuel T. Ariaratnam. Applications of Clustering and Isolation Forest Techniques in Real-Time Building Energy-Consumption Data: Application to LEED Certified Buildings. Journal of Energy Engineering 2017, 143, 04017052 .

AMA Style

Jonghoon Kim, Hariharan Naganathan, Soo-Young Moon, Wai K. O. Chong, Samuel T. Ariaratnam. Applications of Clustering and Isolation Forest Techniques in Real-Time Building Energy-Consumption Data: Application to LEED Certified Buildings. Journal of Energy Engineering. 2017; 143 (5):04017052.

Chicago/Turabian Style

Jonghoon Kim; Hariharan Naganathan; Soo-Young Moon; Wai K. O. Chong; Samuel T. Ariaratnam. 2017. "Applications of Clustering and Isolation Forest Techniques in Real-Time Building Energy-Consumption Data: Application to LEED Certified Buildings." Journal of Energy Engineering 143, no. 5: 04017052.

Journal article
Published: 01 October 2017 in Engineering Applications of Artificial Intelligence
Reads 0
Downloads 0
ACS Style

Jui-Sheng Chou; Ngoc-Tri Ngo; Wai K. Chong. The use of artificial intelligence combiners for modeling steel pitting risk and corrosion rate. Engineering Applications of Artificial Intelligence 2017, 65, 471 -483.

AMA Style

Jui-Sheng Chou, Ngoc-Tri Ngo, Wai K. Chong. The use of artificial intelligence combiners for modeling steel pitting risk and corrosion rate. Engineering Applications of Artificial Intelligence. 2017; 65 ():471-483.

Chicago/Turabian Style

Jui-Sheng Chou; Ngoc-Tri Ngo; Wai K. Chong. 2017. "The use of artificial intelligence combiners for modeling steel pitting risk and corrosion rate." Engineering Applications of Artificial Intelligence 65, no. : 471-483.

Journal article
Published: 01 April 2017 in Journal of Cleaner Production
Reads 0
Downloads 0
ACS Style

Jui-Sheng Chou; Abdi S. Telaga; Wai K. Chong; G. Edward Gibson. Early-warning application for real-time detection of energy consumption anomalies in buildings. Journal of Cleaner Production 2017, 149, 711 -722.

AMA Style

Jui-Sheng Chou, Abdi S. Telaga, Wai K. Chong, G. Edward Gibson. Early-warning application for real-time detection of energy consumption anomalies in buildings. Journal of Cleaner Production. 2017; 149 ():711-722.

Chicago/Turabian Style

Jui-Sheng Chou; Abdi S. Telaga; Wai K. Chong; G. Edward Gibson. 2017. "Early-warning application for real-time detection of energy consumption anomalies in buildings." Journal of Cleaner Production 149, no. : 711-722.

Journal article
Published: 01 October 2016 in Waste Management
Reads 0
Downloads 0

The United States generated approximately 730 kg of waste per capita in 2013, which is the highest amount of waste among OECD countries. The waste has adverse effects to human health and the environment. One of the most serious adverse effects is greenhouse gas emissions, especially methane (CH4), which causes global warming. However, the United States’ amount of waste generation is not decreasing, and the recycling rate is only 26%, which is lower than other OECD countries. In order to decrease waste generation and greenhouse gas emissions, identifying the causality of the waste generation and greenhouse gas emissions from waste sector should be made a priority. The research objective is to verify whether the Environmental Kuznets Curve relationship is supported for waste generation and GDP across the U.S. Moreover, it also confirmed that total waste generation and recycling of waste influences carbon dioxide emissions from the waste sector. Based on the results, critical insight and suggestions were offered to policymakers, which is the potential way to lower the solid waste and greenhouse gas emissions from the waste sector. This research used annually based U.S. data from 1990 to 2012, and these data were collected from various data sources. To verify the causal relationship, the Granger causality test was applied. The results showed that there is no causality between GDP and waste generation, but total waste and recycling generate significantly increasing and decreasing greenhouse gas emissions from the waste sector, respectively. This implies that waste generation will not decrease even if GDP increases. And, if waste generation decreases or the recycling rate increases, greenhouse gas emission will decrease. Based on these results, increasing the recycling rate is first suggested. The second suggestion is to break the causal relationship between MSW and greenhouse gas emission from the waste sector. The third is that the U.S. government should benchmark a successful case of waste management. Based on the research, it is expected that waste generation and carbon dioxide emission from the waste sector can be decreased more efficiently.

ACS Style

Seungtaek Lee; Jonghoon Kim; Wai K.O. Chong. The causes of the municipal solid waste and the greenhouse gas emissions from the waste sector in the United States. Waste Management 2016, 56, 593 -599.

AMA Style

Seungtaek Lee, Jonghoon Kim, Wai K.O. Chong. The causes of the municipal solid waste and the greenhouse gas emissions from the waste sector in the United States. Waste Management. 2016; 56 ():593-599.

Chicago/Turabian Style

Seungtaek Lee; Jonghoon Kim; Wai K.O. Chong. 2016. "The causes of the municipal solid waste and the greenhouse gas emissions from the waste sector in the United States." Waste Management 56, no. : 593-599.

Journal article
Published: 01 September 2016 in Journal of Computing in Civil Engineering
Reads 0
Downloads 0

Developing an expert system has been considered as complex and knowledge driven process. This study proposes a nature-inspired metaheuristic regression system that can find appropriate solutions. The system uses a graphical user interface but does not require a mathematical program installation. The user-friendly interface was designed in the MATLAB graphical user interface design environment (GUIDE) and was implemented by MATLAB compiler. The stand-alone system is easy to use and has many functions, including evaluation, use of an opened data file, test set selection, hold-out, cross validation, and prediction to solve many civil engineering problems with simple manipulations on the system interface. Five benchmark functions were used to evaluate the effectiveness of the optimization module. The performance of the proposed regression system was then validated by comparing its solutions obtained for civil engineering problems with those obtained by empirical methods reported previously. Five actual data sets including energy-efficient buildings, construction material strength, concrete structure shear strength, bridge scour depth, and subbase soil modulus were used as case studies. The prediction accuracy was 8.24–91.76% better than those of previously reported models. The analytical results support the feasibility of using the proposed system to solve numerous civil engineering problems. The system was also much faster at identifying the optimum parameters and solving problems. The experiments confirmed that the novel nature-inspired metaheuristic regression system proposed in this study has superior efficiency, effectiveness, and accuracy.

ACS Style

Jui-Sheng Chou; Wai K. Chong; Dac-Khuong Bui. Nature-Inspired Metaheuristic Regression System: Programming and Implementation for Civil Engineering Applications. Journal of Computing in Civil Engineering 2016, 30, 04016007 .

AMA Style

Jui-Sheng Chou, Wai K. Chong, Dac-Khuong Bui. Nature-Inspired Metaheuristic Regression System: Programming and Implementation for Civil Engineering Applications. Journal of Computing in Civil Engineering. 2016; 30 (5):04016007.

Chicago/Turabian Style

Jui-Sheng Chou; Wai K. Chong; Dac-Khuong Bui. 2016. "Nature-Inspired Metaheuristic Regression System: Programming and Implementation for Civil Engineering Applications." Journal of Computing in Civil Engineering 30, no. 5: 04016007.

Journal article
Published: 01 February 2016 in Resources, Conservation and Recycling
Reads 0
Downloads 0

The U.S. building sector was the most significant emission contributor (over 40%). This paper attempts to examine and use the causal relationships between energy resource consumption, energy prices, and carbon dioxide emissions (from 1973 to 2012) to determine the effects of energy sources and prices on carbon emissions. The relationships would potentially allow policymakers better understand the use of different energy sources, and prices to manipulate carbon emissions in the building sector. Using the Granger causality test and generalized impulse response functions, several causal relationships have been verified. The results indicate that there were long-run causalities from natural gas prices to natural gas consumption in both the residential and commercial sectors, as well as from electricity prices to electricity consumption in the commercial sector. Moreover, electricity and coal consumption were found to cause carbon dioxide emissions in the residential and commercial sectors, respectively. The short-run causality test found that natural gas consumption is the most sensitive toward changes in natural gas price in the residential sector, and electricity consumption is the most sensitive toward electricity prices in the commercial sector. This research also found that the commercial sector's energy consumption generated greater influence on carbon emissions than the residential sector.

ACS Style

Seungtaek Lee; Wai Oswald Chong. Causal relationships of energy consumption, price, and CO2 emissions in the U.S. building sector. Resources, Conservation and Recycling 2016, 107, 220 -226.

AMA Style

Seungtaek Lee, Wai Oswald Chong. Causal relationships of energy consumption, price, and CO2 emissions in the U.S. building sector. Resources, Conservation and Recycling. 2016; 107 ():220-226.

Chicago/Turabian Style

Seungtaek Lee; Wai Oswald Chong. 2016. "Causal relationships of energy consumption, price, and CO2 emissions in the U.S. building sector." Resources, Conservation and Recycling 107, no. : 220-226.

Journal article
Published: 01 January 2015 in Procedia Engineering
Reads 0
Downloads 0

The increase of greenhouse gas emissions in the atmosphere has affected the global climate. The federal, state and local governments are mostly concerned with the environment, so they have developed and/or implemented environmental policies to reduce greenhouse gas emissions in the United States. Increasing renewable energy use and improving energy efficiency are some of the strategies for reducing greenhouse gas emissions. Each state has its own set of regulations and incentive policies to address energy use and supply, and these are constantly updated to reflect changing conditions. Failed policies are often extracted, while successful policies continued (pending availability of funds). Thus, it is necessary to verify which kinds of policies are helpful to increase the usage of renewable energy and energy efficiency methods. The main objective of this research was to understand how influential different kinds of policies would be in encouraging renewable energy use and improving energy efficiency. Based on the analyses, the research team identified the key factors of a successful energy policy. This research systematically reviewed environmental policies and investigated their effectiveness. Most importantly, policymakers can utilize the factors that influence their energy policies.

ACS Style

Seungtaek Lee; Yeowon Kim; Wai K. Chong. A Statistical Analysis of Effectiveness of Energy Policy in the United States: Incentives vs. Regulations. Procedia Engineering 2015, 118, 1282 -1287.

AMA Style

Seungtaek Lee, Yeowon Kim, Wai K. Chong. A Statistical Analysis of Effectiveness of Energy Policy in the United States: Incentives vs. Regulations. Procedia Engineering. 2015; 118 ():1282-1287.

Chicago/Turabian Style

Seungtaek Lee; Yeowon Kim; Wai K. Chong. 2015. "A Statistical Analysis of Effectiveness of Energy Policy in the United States: Incentives vs. Regulations." Procedia Engineering 118, no. : 1282-1287.