Scopus ID: 85047733690
2017 IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), (2017), pp. 1-6
Nolan C. Concha
& Mary Grace V. CalilungConference Paper | Published: July 2, 2017
Abstract
One of the most common problems in the ready mix concrete production industry is to determine the most appropriate proportion of concrete components for optimum performance. However, additives particularly admixtures introduced in the production of SCC to enhance some specific properties of fresh and hardened concrete may contribute to undesirable effects on the workability performance. In this study, superplasticizers blended with Accelerating and Air entraining admixtures were used in the mix and were tested for Slump Flow, V-Funnel, L-Box, U-Box, and Screen Stability tests to determine its influence on the rheological properties of SCC. Based on the results of the study, air entrainer provided microscopic bubbles acting as ball bearings between aggregates offering desirable effect in the workability of SCC. The highest dosage of 5.0% superplasticizers provided the best results in all the tests due to the dispersibility effect in the mixture causing a reduction in flow resistance. The accelerating admixture made SSC less susceptible to segregation, while the additive produced fresh concrete with lower flowability due to the rapid hydration and early strength development in the concrete. Among the two admixtures used, the air entrainer blended with 3.7% superplasticizer exhibited the best performance in all workability tests.
Scopus ID: 85047741688
2017 IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), (2017), pp. 1-5
Kim Paolo S. Aquino, Jessica S. Caisip, ... Mary Grace V. Calilung
Conference Paper | Published: July 2, 2017
Abstract
Many construction companies and individuals (construction designers) are still using spreadsheets and laboratory tests just to obtain a certain data. In the field of technologies, advancement will contribute to the improvement of designing structures in terms of usefulness and effectiveness. By using the principle of artificial neural network, this study developed a sorptivity model which gives immediate quantities with high accuracy and precision which are needed to attain appropriate sorptivity values of concrete design mix. In this study, 40 concrete samples with varying percent replacement of copper slag to sand were tested for sorptivity by following the ASTM C1585 which is the Standard Test Method for Measurement of Rate of Absorption of Water by Hydraulic-Cement Concretes. These values in turn were used in the development of the sorptivity model using Artificial Neural Network. This study used the software called Matrix Laboratory (MATLAB) to train several neural networks. Several numbers of neurons in the hidden layer were considered because there is no actual study that suggests that a certain number of nodes in the hidden layer produce the best model. A parametric testing was conducted to determine which of the parameters considered have the greatest significance to the target output. The predicted results of the best model were compared to the experimental values of sorptivity and produced a 2.36 percentage error. The study results suggest that ANN models could be used to predict the sorptivity value of a concrete sample. The model produced a good prediction result.
Scopus ID: 85047778008
2017 IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), (2017), pp. 1-5
Conference Paper | Published: July 2, 2017
Abstract
Marikina Valley fault system movements, average fault slip rates, and recurrence intervals were carried out to predict the occurrence and characteristics of the anticipated large-magnitude earthquake. This however does not include potential liquefaction that is essential in providing profound disaster preparedness plans and contingency measures. It is therefore vital to develop a novel liquefaction map to address this concern. In this study the City of Pasig was analysed using the Soil Investigation Reports from year 2000 to 2016 of each 30 barangays. The study will benefit the City of Pasig by providing liquefaction susceptability contour maps corresponding to earthquake magnitudes 6.8, 7.2 and 7.6 using Semi-Empirical Method. Based on the analysis, 79% are highly susceptible and 21% are least susceptible to liquefaction for earthquake magnitude of 6.8. Also there are 84% that are highly susceptible and 16% are least susceptible to liquefaction for earthquake magnitude of 7.2 while 87% are highly susceptible and 13% are least susceptible to liquefaction for earthquake magnitude of 7.6. A vast majority of the soils in the City of Pasig are therefore expected to liquefy when the expected large earthquake magnitude occur.
Scopus ID: 85047721352
2017 IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), (2017), pp. 1-6
Conference Paper | Published: July 2, 2017
Abstract
The bond strength in reinforced concrete is defined as resistance to slipping of the reinforcing steel bars from the concrete. This slipping resistance is one of the most important features in the performance of the reinforced concrete structure, particularly to its failure mode and mechanisms. In this study, a hybrid model using Artificial Neural Network (ANN) and Genetic Algorithm (GA) has been developed to predict and optimize the ultimate bond strength (tu) between the reinforcing bar and the concrete based on numerous variables that influence this property. These variables include 28-day cube compressive strength f'c), concrete cover (c), the diameter of reinforcing bar (db), embedded length (Lm), rib height (hr), and rib spacing (sr). ANN was utilized into the prediction of bond property between the reinforcing bar and concrete based on the aforesaid input variables. The ultimate bond strength predicted by ANN model exhibited reasonably accurate and good agreement with the experimental values. On the other hand, GA was deployed in the search for the optimal combination of the input variables which resulted in high bond strength performance. Optimization results showed that smaller hr and sr developed high quality of the bond between the reinforcing steel bar and the concrete.
Scopus ID: 85015388334
2016 IEEE Region 10 Conference (TENCON), (2017), pp. 2268-2271
Conference Paper | Published: February 8, 2017
Abstract
The game entitled “The Planet” aims to educate young generations regarding waste segregation, tree planting, reducing the use of pollutants and other factors concerning global warming. The virtual environment immerses players, ages 10-25. The game combines strategies used in commercial gaming environments to motivate players in preserving the planet. It comprises with three different stages, The Dessert, The Water, and The Factory. Each stage, player needs to traverse the environment and solve some puzzles that aim to simulate simple solutions in fixing environmental problems.
Scopus ID: 85015356623
2016 IEEE Region 10 Conference (TENCON), (2017), pp. 3265-3269
Arlene O. Trillanes, Bernie S. Fabito, ... Maria Rosario D. Rodavia
Conference Paper | Published: February 8, 2017
Abstract
This project will enable to raise awareness regarding public health and common illness in different parts of the world. With use of Twitter stream, it enables to collect global trending issues and to visualize areas prone to illness thru Heat map. It helps to address the needs of certain area to send assistance such as vaccines, antibiotics, etc. The HealthSource Web Application also have modules is to provide useful information regarding their medicine intake to such illnesses in the easiest and most understandable way.
Scopus ID: 84992080016
2016 IEEE Region 10 Symposium (TENSYMP), (2016), pp. 95-98
Conference Paper | Published: July 22, 2016
Abstract
The research aims to develop a teacher's performance evaluation tool using opinion mining with sentiment analysis. The study may help to identify the strengths and weaknesses of the faculty members based on the positive and negative feedback of the students either in English or in Filipino language. The proposed system provides the sentiment score from the qualitative data and numerical response rating from the quantitative data of teachers evaluation. It will also graphically represent the evaluation result including the percentage of positive and negative feedback of the students. Thus, the school administrators and educators will be more aware about the sentiments and concerns of the students.
Scopus ID: 84965164135
2015 International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM), (2016), pp. 1-6
Francis F. Balahadia, Arlene O. Trillanes, ... Maria Rizza L. Armildez
Conference Paper | Published: January 28, 2016
Abstract
Fire incidents are costly occurrences that may be preventable. This study aims to analyze fire data in the City of Manila from 2011 to 2014 based on the various causes of fires. Temporal analytical techniques complemented by geo-mapping are used to determine fire patterns based on time, day, month and year. A total of 2,316 fire incidents were included in the study and fires due to faulty electrical connections occurring from 6PM to 9PM emerged as the time with the most number of fire incidents. The daily pattern does not show much variation although the monthly pattern shows that the summer months have the more number of fire occurrences with faulty electrical connections as the main cause. The yearly pattern also do not offer much variation though the same cause of fire is noted to have the highest occurrence. Patterns identified may be useful inputs in formulating proactive fire preventive measures and in allocating fire resources. Future research directions in spatial and spatiotemporal analyses have been identified.
Scopus ID: 84965100877
2015 International Conference on Humanoid, Nanotechnology, Information Technology,Communication and Control, Environment and Management (HNICEM), (2016), pp. 1-6
Nolan C. Concha
& Elmer P. DadiosConference Paper | Published: January 25, 2016
Abstract
Self compacting concrete (SSC) is one of the most useful innovations in concrete technology that has the ability to flow efficiently and maintain material homogeneity. However, additives particularly admixtures introduced in the production of SSC to enhance some specific properties of fresh and hardened concrete may contribute undesirable effects on the workability performance. In this study, superplasticizers blended with fly ash was used in the mix and were tested for Slump Flow, L-Box, and Screen Stability tests to determine its influence on the rheological properties of SCC. Several mixtures were tested in order to derive a mix proportion having the optimum rheological properties. Artificial neural network and genetic algorithm were used to determine the concrete mix proportion that will provide the best workability. Results showed that ANN was able to establish the relationship of rheology to the concrete material components and GA derived the optimum proportion for best rheological performance.