Florante D. Poso, Jr.
23 Publications
Scopus ID: 85159468687
2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2022), pp. 1-6
Conference Paper | Published: January 1, 2022
Abstract
Earthquakes or seismic events cause several disturbances in the ground which can harm or damage the structural stability and could turn fatal. Liquefaction causes a sudden movement shift that is out of sync with the rest of the structure. This might cause several structural damages to the property leading to casualties. In the Philippine geographical context, the West Valley Fault which traverses Metro Manila is a seismic threat capable of producing a maximum magnitude of 7.2. This study aims to assess the probability of liquefaction in Farnecio St., Quaipo, Manila as well as the determination whether the structures present will suffer from critical failure or not. Probabilistic Seismic Hazard Analysis and Monte Carlo Simulation were used to determine the seismic hazard. Recorded earthquake history from the Philippine Institute of Volcanology and Seismology was used as part of the seismic analysis. The seismic hazard analysis shows that a magnitude 5.3 earthquake has a 44.44 % probability of occurrence within the 10-to-20-kilometer distance from the seismic source. A peak ground acceleration of 0.458g and 0.548g was also determined for return periods of 500 and 2500 years for which a Uniform Hazard Response Spectrum was generated. The probability of damage for 2% in 50 years and 10% in 50 years is 39.63% and 30.41%, respectively.
Scopus ID: 85159496393
2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2022), pp. 1-5
Kimberly Ann V. Yano, Mike Aldrin D. Cabaluna, ... John Manuel B. Vergel
Conference Paper | Published: January 1, 2022
Abstract
Flooding is a perennial problem in the Philippines, particularly in its capital city of Manila. Sampaloc is one of the barangays in Manila vulnerable to flooding according to the Flood Risk Map of Metro Manila. The researchers considered España Boulevard as the area of study since it is one of the most flood prone roads in Manila, according to Metro Manila Development Authority (MMDA). The study is focused on the analysis of Rainwater Gardens as an additional flood mitigation in España Boulevard using Storm Water Management Model (SWMM) simulation. Studies have proven that rainwater garden is considered as one of the most effective, simplest, and low-cost methods to address abrupt flooding. Moreover, it is easy to install, maintain and has a lot of advantages such as removing nutrient-based pollutants such as nitrogen and phosphorus, improving air quality, money saving and water conservation and improving environmental aesthetics. The data was collected through online surveys. The gathered data was calibrated and simulated using SWMM. As per the results, the rainwater garden is effective as an additional flood mitigation system since it can reduce the flood depth up to 19.42% and 14.78% for 25-year return period and 50-year return period storm, respectively. The delay of abrupt flooding is beneficial to the residents of flood prone areas. In real life scenario, the 0.15 m difference in flood depth for a 25-year return period storm and 0.17 m difference in flood depth for a 50-year return period storm will serve as longer time for evacuation of the residents when excessive flooding occurs. Moreover, rescuers will have more time to respond to affected areas and save more people.
Scopus ID: 85159450150
2022 IEEE 14th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2022), pp. 1-5
Conference Paper | Published: January 1, 2022
Abstract
Slope stability of homogenous earth dams is a very important design analysis to consider since earth dams are prone to failure on slopes due some factors such as material properties and the design slope of the downstream side. SLOPE/W simulation using the method of slices was utilized to determine the effect of the input parameters in a Mohr-Coulomb analysis including the unit weight, cohesion, and angle of internal friction (Ø) of the dam material to be used. Hydraulic structure designers encounter difficulties in analyzing the best design which include the proper slope due to the complexity of the required input parameters and other factors affecting the stability of the dam. It is the main objective of this paper to provide a prediction model of the slope stability (SS) of homogenous earth dams using neural network (NN) particle swarm optimization (PSO) model. The model output will be of great contribution to the designers in investigating the proper earth material and slope design to consider both the structural integrity and economic aspect of the structure. There were seven (7) slopes and 14 earth materials analyzed in the study. The particle swarm optimization technique was implemented and utilized to train and optimize the initial neural network model. The material parameters such as unit weight of the soil (kN/m3), cohesion (kPa), angle of internal friction Ø (degrees), and slope were utilized as the input variables in the study while the factor of safety (FOS) values was used as the output variable. The developed NN-PSO model has an R (all) value of 0.99752, while the MSE of the developed NN-PSO model is 0.0021844. The values of R and MSE are close to the ideal values of 1.0 and 0 respectively, showing further that the model is satisfactory. Using the Garson’s algorithm, it was observed that the angle of friction is the most significant parameter to the FOS.
Scopus ID: 85127578446
2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2021), pp. 1-5
Conference Paper | Published: January 1, 2021
Abstract
The study observes the Pandemic Crisis (Covid 19) that resulted in impacts on the Transportation category in the area National Capital Region. Public transportation is an important aspect of human’s ability to travel to different places whether its personal or business purpose, it’s a part of life that people take for granted and can’t be taken away easily. But due to the pandemic era, people have been careful in their choices, which resulted in the change standard when it comes to public transportation choices. With that said, to understand and observe these impacts, a scenario must be made such as before and after the pandemic designed as an environment for the study to take root. The study has used machine learning called Random Forest Algorithm with the used several parameters to create a prediction model. As for the method in gathering data, a survey of Google Form is utilized to gather 200 participants of the National Capital Region with varying parameters for their choice of public transportation. The machine algorithm has shown satisfactory accuracy of 89.88% and 88.88%. As an important note, it is observed that travel expense has more impact on public transportation choices than other parameters. The Random Forest Algorithm has been utilized in creating classification types of models and can help future researchers improve the machine learning approach.
Scopus ID: 85127574002
2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2021), pp. 1-6
Jeric Bustarde, Juan Miguel Cruz, ... Mark Ondac
Conference Paper | Published: January 1, 2021
Abstract
Since the start of the 2019 pandemic, medical staff and non-medical staff are fighting on the front line in all hospitals worldwide. However, the possibility of healthcare workers’ scarcity due to the increasing medical infection rate is ignored in many recent studies. To prevent such things to happen, the installation of a negative air pressure isolation room is proposed to Norzagaray Municipal Hospital (NMH). Primary parameters such as filtration, pressure management, and dilution ventilation were investigated in SOLIDWORKS simulation software by removing one parameter per simulation. Two existing schemes were simulated, and the primary parameters present were evaluated. Three ventilation design set-ups were designed and the effects of the varying placements of the primary parameters to the airflow pattern in a negative air pressure isolation room were determined. Cost-benefit analysis (CBA) was conducted to determine if the cost of installing the negative air pressure room outweighs its benefit. The set-up where the High efficiency particulate air (HEPA) machine is inside the room is proposed to NMH as this abides by the Department of Health (DOH) memorandum and standards on Airborne Infection Isolation Rooms (AIIRs) and is the most effective of the three set-ups. Results show that filtration filters the infectious particles, pressure management manages the proper airflow direction, and dilution ventilation makes sure there are enough air changes per hour to filter a percentage of infectious particles. In the existing schemes, all the primary parameters were used to contain the infectious particles in the room, however, the effectivity of the filtration also depends on the location of the patient, supply, and exhaust. The most significant effect of the varying placements of the primary parameters can be seen in filtration as only the set-up where the HEPA machine is inside the room was able to filter 100% of the infectious particles. It is also the most profitabl...
Scopus ID: 85127571860
2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2021), pp. 1-6
Conference Paper | Published: January 1, 2021
Abstract
Air pollution is the atmospheric condition in which substances are present in the air in such concentrations and duration that are detrimental to human health and the environment. The effects of air pollution on public health are being felt worldwide. These are the common air pollutants, including lead, nitrogen oxide, Sulphur dioxide, carbon monoxide, and Total Suspended Particulates (TSP), the latter being the most widespread and the most serious for human health. This study presents a GIS-based mapping as a means for generating high-resolution maps over large geographic areas. A wide range of data collected from different air monitoring stations in the Metro Manila, Philippines, can be managed in the frame of spatial models developed in GIS. The approach of this study is demonstrated by modeling concentrations of Total Suspended Particles for Metro Manila. Mapping of the air pollution using the GIS for seven different stations during the dry and wet seasons from 2016 up to 2020 was developed. The concentration of TSP for the dry and wet seasons were visualized in planar view. The visualized result generated by the GIS has the potential to offer valuable information in demonstrating the air quality index of Metro Manila over the span of 5 years. The results showed that during the wet seasons, the air quality became good. On the other hand, the dry seasons showed the air quality being consistently moderate and, in some parts, changing from being good to moderate. Generally, we can conclude that the public can still enjoy and experience usual activities outdoors, although the results may seem to be at no risk, it is best to be mindful of the current conditions, especially in the present-day, climate change is getting worse.
Scopus ID: 85127601486
2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2021), pp. 1-6
Conference Paper | Published: January 1, 2021
Abstract
The construction industry is heavily reliant on the production of laborers, and construction labor costs share a big part in the total cost of the projects. Due to the emergence of the COVID-19, the construction industry has been experiencing restrictions and limitations in their project sites, affecting labor productivity. This paper aims to determine which factors are critical in influencing Construction Labor Productivity in the National Capital Region (NCR) in the Philippines during the COVID-19 pandemic by obtaining each of the critical factors’ respective relative importance index. In order to achieve the study’s objective, the researchers asked experts under the category AAAA companies who have a site and managerial experience during the pandemic to participate in an online questionnaire survey. 34 factors were considered for this study and categorized into four groups: (1) Human/labor; (2) Management; (3) Technological; and (4) COVID-19. The findings of the study were able to identify the 10 significant factors affecting labor productivity during the pandemic: (1) Laborer’s Experience and Skill; (2) Availability of Materials; (3) Clarity of Instructions and Daily Task Assignments; (4) Coordination among Level Design Disciplines; (5) Shortage of Laborers, Construction Method; (6) Prolonged Delivery Period, Limited Number of Work Personnel Per Zone, Leadership of Construction Management; (7) Clarity of the Drawings and Specifications; (8) Communication among Laborers, Rework; (9) Social Distancing, Laborer’s Absenteeism; and (10) Availability of Personal Protective Equipment. The study can guide construction firms for efficient management of laborers during a pandemic to improve construction labor productivity and accomplish a cost-effective project.
Scopus ID: 85127588959
2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2021), pp. 1-6
John Mark Cagurungan, Royvin Factuar, ... Jon Arnel S. Telan
Conference Paper | Published: January 1, 2021
Abstract
Solid waste generation is one of the world’s most prevalent challenges, especially in places with crowded populations and inadequate solid waste disposal strategies. There are several extant influencing variables on solid waste creation. In this regard, the researchers focus on five (5) elements or categories that contributed the most to solid trash generation. The researchers sought to determine which one has the greatest influence on solid waste generation in Barangay Sagrada Familia among these five categories. This will contribute to their future solid waste management plan through minimizing, segregating, and recycling the solid waste, which is one of the causes of their flooding problem. ANN (Artificial Neural Network) is a simplified computational brain model that is one of the most often utilized artificial intelligence in solid waste management. To get the desired outcomes, Matrix Laboratory (MATLAB) testing is essential. The researchers gathered information from studies, theories, and literature in the field. The researchers then performed a survey to gather data and existing data in the barangay and used Excel and Matrix Laboratory (MATLAB) to construct the model for a Neural Network analysis. Finally, the authors analyzed the Neural Network, with the goal value varying according to Pearson’s Correlation Coefficient (R).
Scopus ID: 85127585497
2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2021), pp. 1-6
Sophia Chloe Caress, Angela Abigail Belen, ... Melvin B. Solomon
Conference Paper | Published: January 1, 2021
Abstract
Agriculture relies heavily on weather forecasts, and a reliable weather forecasting system can help mitigate the calamities which can affect this industry. Rainfall and meteorological drought duration forecasting are some of the most important yet challenging tasks. This paper presents the creation of feedforward backpropagation artificial neural networks for daily rainfall forecasting and monthly meteorological drought forecasting. Artificial Neural Networks can capture the variability of these phenomena. Rainfall data from nine stations all over Albay, the Philippines, spanning from 1967 to 2000, were used to create the models. The input parameters used for developing the models for daily rainfall forecasting were 14-day antecedent rainfall, current-day rainfall, relative humidity, mean temperature, and sunshine duration. The monthly meteorological drought forecasting parameters were 1-month SPI, current-month rainfall, relative humidity, mean temperature, and sunshine duration. Having the results presented in this paper, the performance of the ANN Models of the stations were compared based on R and RMSE. The rainfall forecasting models and meteorological drought forecasting models have provided satisfactory performance. A satisfactory performance for forecasting has an R-value ranging from 0.2 to 0.5. Sensitivity analysis indicated that the most significant parameter for rainfall forecast is the relative humidity and mean temperature for drought forecast.
Scopus ID: 85127580954
2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM), (2021), pp. 1-6
Richard D. Andress, Jason James V. Robin, ... Bon Ryan P. Aniban
Conference Paper | Published: January 1, 2021
Abstract
Solid waste management has been a major issue in developing countries such as the Philippines, as the nation improves living, economic growth, industrialization, and increase in population. Another major issue is the lack of electricity in the Philippines, particularly in the province. The Philippines’ plan for full electrification of all households becomes difficult due to its topography, and geography. This study aims to provide an alternative solution for both issues by innovating through Waste-to-Energy. Waste-to-Energy is one of the alternative solutions in response to the worsening municipal solid waste in the world and source of electrical energy. A small-scaled incinerator was built for an alternative solid waste management machine and micro-electricity supply for rural communities. Instead of burning the waste or dumping it in the landfills, it will be processed in the prototype to lessen its environmental impact. This is possible using air filter bags and crushed mussels, which lessen the air pollution produced during the incineration process of the waste as it builds up heat. The thermoelectric converter would absorb the build-up heat, convert heat to electricity, and store in a battery. The findings demonstrated that it is possible to generate up to 0.317 kW of electricity from 8 kilograms of waste using thermoelectric converters. This quantity can produce electricity for several households in municipal rural areas.