Journal Issue: Journal of Advances in Engineering and Technology (JAET)
Volume
Volume 01
Number
Issue ii
Issue Date
2023-03
Journal Title
Journal ISSN
2950-7138
Journal Volume
Articles
Strategies Used by the Sri Lankan Construction Industry to Overcome the Challenges Posed by the Covid-19
(SLIIT, Faculty of Engineering, 2023-03-02) Fernando, P; Gunarathna, N
The global outbreak of the COVID-19 pandemic has thrown the world's political, social, economic, religious, and financial structures into disarray. COVID-19 has a broad range of effects on numerous industries, including construction. Sri Lanka as a developing country is also affected by the pandemic and many sectors of the Sri Lankan economy such as construction, education, tourism, imports and exports, agriculture, and health etc are experiencing negative consequences of the pandemic, Construction industry as one of the key sectors of the economy was also severely affected by the COVID-19 global pandemic in various ways. Construction companies and government institutes are taking action to face these challenges. This study describes how COVID-19 impacts the local construction industry throughout the construction process. Furthermore, the impact was evaluated with regard to several aspects namely, financial, human resources, supply chain and logistics, legal, and completion and handover of the project. The strategies which are used by the construction industry participants to face the challenges of the pandemic are also discussed. A mixed research approach was used in the study and questionnaires which comprised of both qualitative and quantitative questions were used as a data collection tool. This research revealed new knowledge about the construction industry in relation to the COVID-19 pandemic. The study's most important finding was that the spread of the virus influenced the entire construction process. The pandemic had a major impact on the construction stage as well as the human resources aspect of the industry.
Data Science to Determine Mechanical Properties of Low Carbon Steel During In-Process Inspections
(SLIIT, Faculty of Engineering, 2023-03-02) Alahapperuma, K. G.; Suraweera, D. D. D.; Nandhakumar, N.
Carbon steel is a widely used category of engineering metal, mainly due to its attractive mechanical and fabrication properties and low cost. The chemical composition, physical parameters, and mechanical properties of carbon steel are maintained as per the specified standards, and local steel should be complied with Sri Lankan Standard 375: 2009. Generally, the chemical composition is tested during melt stages, and mechanical properties are tested for finished products. Since it is necessary to ensure products comply with the standard, mechanical properties are tested during in-process inspections as well. When the results are not within the acceptable range, a considerable amount of production has to be rejected, causing a loss to the manufacturers. If the results of the in-process inspection are instant, it will help make suitable adjustments to process conditions and thereby prevent rejection of products, while reducing quality assurance costs, as well. Therefore, the objective of this study is to predict tensile properties with chemical composition, as input variables, to be used for in-process inspections. Forty mechanical test reports were collected from a steel manufacturing factory for 12 mm diameter, thermo-mechanically treated (TMT) steel bars. Each test report is of 15 samples from the respective batch, and consists of corresponding chemical composition and physical parameters. Multiple linear regression analysis was applied to each batch, using Statistical Package for the Social Sciences (SPSS) software, to predict yield strength (YS), ultimate tensile strength (UTS), elongation at break (EB) with carbon equivalent value (CEQ) and percentage of Sulphur as inputs. Relationships between variables were not significant, even though those relationships can be used to predict tensile properties. The predictions may not be reliable, due to the limited conditions of the study and assumptions made. It is therefore recommended to apply multivariate regression analysis or Artificial Neural Network (ANN) techniques, with other chemical elements, process temperature and water flow rate etc. also as input variables.
Experimental Identification of Alkali-Aggregate Reaction in Concrete
(SLIIT, Faculty of Engineering, 2023-03-02) Baddeliyanaralalage Don, R
Several advanced and time-consuming methodologies have been developed to detect Alkali-Silica Reaction (ASR) in suspected structures. The main objective of this research study was to identify a reliable experimental procedure for detecting ASR in existing concrete. A simple staining solution is used here to detect ASR in concrete specimens. The staining reagent employed here is Sodium Cobaltinitrite, which is used in the Los Alamos staining method to detect ASR. Sodium Cobaltinitrite can identify potassium-rich ASR gel by staining it yellow for rapid field screening purposes. Reactive and control concrete specimens were cast to get some experience with this test and to verify whether this test can be used in a suspected concrete structure. Waste white soda-lime glass aggregate was used to cast reactive concrete specimens, whereas natural coarse aggregates were used to cast non-reactive concrete specimens. Testing was carried out in two batches. Each batch consisted of six reactive and six control concrete specimens which were cured in the above-mentioned solutions. The first batch was examined after 44-days and the second batch was tested after 60-days of casting. Results of this test showed that reactive concrete specimens cast using glass displayed yellow stains as expected, demonstrating the presence of potassium-rich ASR gel on the concrete surface. Employing NaOH as a curing medium had accelerated ASR. There is a limitation in the method when utilizing KOH as a curing agent. It is concluded that Sodium Cobaltinitrite can be used as a method for rapid identification of ASR in the preliminary stages of experimental identification of the alkali-aggregate reaction in an existing concrete structure.
Efficient Ventilation Configurations for an Isolation Ward in View of Reducing the Potential Contamination of Its Occupants
(SLIIT, Faculty of Engineering, 2023-03-02) Durage, H; Attalage, R; Bandara, R.M.P.S.
The rise of respiratory infections, such as the SARS epidemic in 2003, and the H1N1 influenza epidemic in 2011, highlighted the importance of efficient ventilation in healthcare facilities. The novel SARS -Cov-2 disease has sparked many concerns over the ventilation performance of multi-bed isolation wards and their ability to suppress airborne infectious contamination. The study is primarily based on suggesting ventilation improvements for a locally acquired multi-bed intensive care isolation unit. The study via ANSYS -fluent incorporates a k-𝜀 turbulent model that is used to analyze exhaled CO2 particle tracks of 4 human models. Three ventilation strategies, namely, Displacement, Stratum, and Curtain -Air-jet are initially considered and evaluated based on two indoor air quality indices (IAQs), namely, air change efficiency and contaminant removal effectiveness. Stratum ventilation comfortably exhibits unidirectional flow characteristics with an air change efficiency of 0.946, which was obtained through ANSYS -CFX while each suggested configuration is capable of achieving a contaminant removal effectiveness value greater than 1 which depicts that the contamination source is not in a perfect mixing zone. Results provided inconclusive evidence to draw correlations between the two IAQ indices and thus it is confirmed that these indices solely depend on the type of ventilation strategy. Contaminant concentration on health care worker breathing plane and exhaled particle tracking for 4 minutes in each analyzed configuration revealed that both Stratum and Curtain air-jet models improve the escaped particle efficiency by 25% and 29% respectively compared to the base model. These models are further compared against reference values specified by guidelines to evaluate their suitability for real-world operation.
Neural Network based automated hot water mixture
(SLIIT, Faculty of Engineering, 2023-03-02) Firsan, F.N.M; Herath, G.M; Thilakanayake, T.D
In the present day and age, most residential spaces comprise a shower system and generally a conventional system of hot water showers. Throughout history, showering has developed as an essential need in a person’s life. Nevertheless, a typical hot water shower system comprises delays in hot water mixing and usually requires an average of 2 to 4 minutes to mix the cold and hot water to deliver the appropriate shower temperature. The delay in mixing provides less comfort and poor satisfaction affecting people’s lifestyles. Due to these disadvantages, a system incorporating artificial Intelligence can be utilized to enhance the performance of mixing which can offer an automated hot water mixture system with improved efficiency and effectiveness. Recently, significant research has been focused on utilizing deep learning technology due to its multiple breakthroughs in fabricating a broad range of automated novel applications since Neural Networks comprise the capacity to learn from data to offer efficient and accurate systems. In this research project, the hot water mixture is employed by an Artificial Neural Network model integrated with the combination of an embedded system of the proposed system of hot water mixture. Furthermore, the proposed system comprises temperature and flow sensors along with controllable flow valves. The tested system indicated acceptable accuracy between the actual and desired output flow rate and temperature.
Description
Editorial Committee
Prof. Upaka Rathnayake
Prof. Niranga Amarasingha
Prof. Migara Liyanage
Dr. Sujeewa Hettiwatte
Dr. Mudith Karunarathna
Ms. Nishanthi Gunarathna
Advisory Board
Prof. Dilanthi Amarathunga University of Huddersfield, UK
Prof. Janaka Ekanayake, University of Peradeniya, Sri Lanka
Prof. Kyaw Thu Kyushu University, Japan
Prof. Jagath Manatunge University of Moratuwa, Sri Lanka
Prof. George Mann Memorial University of Newfoundland, Canada
Prof. Srinath Perera, Western Sydney University, Australia
Prof. Ahmed Abu–Siada Curtin University, Australia
Prof. R. Thevamaran, University of Wisconsin, Madison, USA
Prof. S. C. Wirasinghe University of Calgary, Canada
Editorial Assistant / Secretary of the Journal
Ms. Nishanthi Gunarathna
Keywords
Speed Humps, Speed Profile, Noise Profile, Construction Industry, Covid-19 Challenges, Sub-Synchronous Resonance, Thyristor Controlled Series Compensators, Neural Network, Automated Hot Water Mixture, Alkali-Aggregate Reaction, Concrete, Data Science, Mechanical Properties, Low Carbon Steel, In-Process Inspections, Ventilation Configurations, Isolation Ward, Contamination Reduction