Biochar also known as ‘biocarbon’ or ‘biocoal’ is a material that has a charcoal similar property. It is obtained from thermolysis (pyrolysis) of biomass feedstocks and plant matters. It can help the process of eliminating carbon dioxide from the atmosphere. The biochar was considered as waste by industrial plants and considered no additional cost except for the transportation. Biochar was tested for its chemical properties in Department of Science and Technology as a parameter for Simapro. Environmental and health impact were analyzed in this study for concrete with biochar as partial replacement for cement. Different mixtures with zero percent to twenty percent biochar replacement was simulated using life cycle assessment with the help of Simapro. Different sources in Luzon island, Philippines were gathered and found out that sources in southern part of Luzon is the best sources for biochar because of its near location that decreases the effect of transportation. Also, concrete with biochar replacement with or without considereing the effect of transportation yields greater health and environmental impact compared to mixture without biochar replacement.

Philippine is one of the countries near in the Pacific Ring of Fire. In recent years, several moderate to high seismic activities happened that leads to casualties, deaths and damages in different structures. Manila is the capital of the Philippines with a population of almost 1.8 million. Many structures have been considered as old and unsafe and with an impending earthquake, it is essential to rehabilitate these structures. The imminent danger of the West Valley Fault when it moves is known throughout the metro manila and other neighbouring regions. The damage and casualties that will sustain from the possible 7.2 magnitude earthquake is fatal. Conducting mitigation programs is critical for it will greatly benefit the government and the people. Rapid Visual Screening (RVS) is an effective and efficient way of assessing the building’s structural integrity. This methodology is used to assess the structure’s seismic risk by visual observation of the exterior and interior of the buildings and a data collection form. RVS was applied in 26 hospital building’s located in Manila and the outcome of the assessment has shown that only 6 hospital buildings proved to be seismically adequate when using the level 1 data collection form. RVS is an effective tool in providing initial insight in the building’s vulnerability to seismic event.

The application of natural fibers in reinforced composites to create sustainable materials is gaining more attention due to their appealing attributes, such as low cost, low density, and good mechanical properties. The development of Self-Compacting Concrete (SCC) in the construction industry continuously improved over the last decade due to its advantage of having self-consolidation capability. However, a lack of studies on how SCC behaves when introducing natural fiber still arises. Hence, the present work investigates the effect of Coir fiber (CF) as reinforcement to SCC. In addition, this study provides information on the extraction process, surface characterization using Scanning Electron Microscopy (SEM), and tensile strength of the Coir fiber. Finally, the coir fiber was applied to SCC by varying the dosage ranging from 0%, 1%, 1.5%, and 2% by weight of cement to determine the fresh properties and compressive strength. The fresh properties were assessed using slump flow, T500, L-box, and GTM Screen stability tests. Furthermore, the compressive strength of the new composite was determined after 28 days. The results reveal that adding coir fiber significantly affects the fresh properties of SCC because of its hydrophilic nature. Despite the reduction in the flowability, passing ability, and segregation resistance as the fiber increases, the SCC with CF is within the acceptable ranges specified on the EFNARC standard except for the mixture with 2% coir fiber. Additionally, incorporating 1% coir fiber achieves 12.84% higher compressive strength with less cracking pattern and failure mode. Thus, it emphasizes that it can be utilized fully in the construction industry to reinforce SCC.

Fiber-reinforced polymer (FRP) is an innovative material in the construction industry. It is beneficial because of its toughness, and unlike steel, it is not prone to corrosion. Some research studies focus its behavior as a reinforcement in concrete while deriving several equations pertaining to its shear strength capacity. This study used the artificial neural network modeling technique to derive a more accurate solution to predict concrete shear capacity with FRP as reinforcement. Experimental data from previous studies were collected and used to train the model. The parameters considered were compressive strength of concrete, FRP ratio, beam dimensions, and modulus of elasticity. As a result, the model consistently provides a better prediction of the shear capacity of concrete against existing models like ACI 440.1R-03, ACI 440.1R-06, and El-Sayed. Furthermore, the ANN model showed no sign of disarray in predicting every parameter compared to other existing models. According to ACI 440.1R-06, FRP bars largely affect the total shear capacity of concrete. In the model provided by ACI, FRP reinforcement’s axial stiffness accounts linearly to the shear strength capacity of concrete. Since then, the predicted capacity in accordance with the ACI was excessively conservative. With respect to the derived model, axial stiffness offered a variation in the shear capacity. The proposed ANN model can be utilized for the design since the minimum ratio between the actual test result yields to 0.77 which is greater than the strength reduction factor of 0.75. Parametric studies were also conducted to show the effect of the modulus of elasticity of FRP, FRP ratio, and beam dimensions on the shear capacity.

To help the future of the industry, the researchers developed a mix design of concrete with varying water-cement ratios from 0.50%, 0.55%, 0.60%, and 0.65% to be used as recycled concrete aggregates. These varying mix designs will determine the effects of the porosity of the RCA on a new concrete mix. Initially the compressive strength test results of the RCA display that as the Water-cement ratio increases the Compressive Strength decreases which can clearly see that Samples with 0.65% W/C ratio display an average compressive strength of 27.29 MPa which does not achieve the C30 standards. Using the Rapid Chloride Penetration Test the amount of electrical charge flows to the sample was computed, based on the results obtained from the rapid chloride penetration test, it can be concluded that the porosity of the recycled concrete aggregates has a significant impact on the new porosity of concrete when a mixture of both recycled aggregates and natural aggregates are used. The researchers found out that from a controlled sample which is a normal concrete, the value of its ion penetration gives an average of 3431.33 Coulombs. On the other hand, RCA 1 achieved an average of 5466 Coulombs, RCA 2 passed an average charge of 5681.33 Coulombs, and RCA 3 obtained an average charge of 6528.67 Coulombs, these indicate that the higher the water-cement ratio on the recycled aggregates used will contribute to the percentage of the void of the concrete that will give the ion a passageway to penetrate the concrete and obtain a higher value of charge.

Corrosion is the leading reason for reinforced concrete structures reduced service life. Structures such as ports and harbors and bridges and other offshore and near shore are prone to chloride-induced corrosion. This research evaluates the use of self-compacting concrete (SCC) as an alternative concrete for such structures. In theory, SCC reduced water content, and high cement and powder content will help protect the reinforcement from chloride intrusion because of its lower porosity and the alkalinity that the cement provided. Sixteen different mixtures of SCCs were mixed and tested for rheology, compressive strength, rapid chloride ion penetration test (RCPT), and impressed current (IC). Water content is the significant factor that affects both RCPT and IC. The segregation of SCC when too much water-cement ratio is combined with a high amount of superplasticizer resulted in a high level of corrosion in the reinforcement. The formation of cracks accelerates the corrosion due to the increased flow of current in the IC set-up. The impressed current technique is the suggested method for determining the corrosion resistance of concrete since it simulates the similar effect of corrosion to concrete which is cracking. It also stimulates the effect of rust on the flow of current. A rapid chloride penetration test is a good indicator of the durability of concrete but may be insignificant for predicting the corrosion level of reinforcement for SCC. Segregation negatively affects the total charge passed in the impressed current and the corrosion level of the rebar.

Corrosion is one of the biggest problems of reinforced concrete structures prone to high chloride environments such as ports and harbors. Due to a lack of studies that can support the use of steel fiber reinforced self-compacting concrete, researchers are still in dispute regarding the effect of using steel fibers in chloride-rich environments. This paper explores the use of neural network modeling to precisely predict and further analyze this problem. Twenty-six different mixtures of steel fiber reinforced self-compacting concrete with varying amounts of cement, water-cement ratio, superplasticizer, and steel fiber were used to derive the feed forward back propagation neural network and compared to a derived non-linear model. The derived neural network model with fourteen hidden nodes and tansig as transfer function has an R-squared of 0.949 for the training. The comparison shows that ANN has superior predicting capability compared to non-linear modeling even with a limited number of data. Parametric analysis was performed and found that steel fiber shows improvement in the corrosion resistance of concrete for mixtures with low to moderate water-cement ratio and an opposite behavior for high water-cement ratio. This is due to the presence of voids formed around the surface of the steel fiber due to capillary action. These voids serve as highways for chloride ions.

In this study, there were two (2) derived models which are the compressive strength and slump workability of concrete with waste ceramic tile without adding any additives using an Artificial Neural Network (ANN) model based on five (5) different input parameters which are the Amount of Fine Aggregate (FA), Amount of Coarse Aggregate (CA), Cement Dosage (C), Water-cement ratio (W/C) and the Amount of Waste Ceramic (CW) respectively while concrete slump and compressive strength test result as an output on the model. The two (2) derived models have satisfactory accuracy where the regression values are 0.98007 and 0.99643 and the mean square error of 10.218 and 1.4927, respectively. All models show excellent accuracy has a maximum error of 19.07% and average error of 2.2%. for slump workability, maximum error of 9.26% and average error of 1.81% for compressive strength model. Parametric study was used to describe the behavior of the derived models, the addition of ceramic waste improves the mechanical properties of the concrete, specifically its compressive strength, while the value of slump workability decreases. The study also performs the relative importance calculation, and based on the results, water to cement ratio (w/c) is the main contributing factor for the slump workability and compressive strength model among other parameters, having the most contributing relative importance value of 28.35% on slump model and 27.47% on compressive strength model.

The accumulation of waste tires leads to environmental degradation caused by uncontrolled dumping in landfills, which are prone to fire and emit harmful gases like carcinogens. Reusing this as reinforcement to self-compacting concrete (SCC) is an alternative way to address the issue. For over a decade, SCC emerged in the construction industry due to its enhanced mechanical properties and capacity to self-consolidate on its own. However, there is still limited literature describing the behavior of SCC with tire waste steel fiber (TWSF). This study provides an overview of the extraction, quantification, geometric characterization, surface characterization, and application of TWSF to self-compacting concrete to determine workability and the compressive strength of SCC with TWSF. A total of five mixes were prepared, including the control noted as SCC without fiber and SCC with TWSF, with fiber content ranging from 0.7 %, 1 %, 2 %, and 3 %. The fresh properties were evaluated using the European Federation for Specialist Construction Chemicals and Concrete (EFNARC) standards such as slump flow test, T500, L-Box, and wet sieving or GTM Screen Stability Test. In addition, the compressive strength was determined after 28 days. The investigation reveals that these fibers can be retrieved in three ways: manually cutting the tire's edge, using a specialized machine to pluck the fibers, or incinerating them. It was projected that 4.85 - 7.16 x 105 t of TWSF might be generated annually. The result of the inclusion of TWSF in SCC does not significantly affect the workability. However, there is a reduction in the passing ability of about 11.713 % and 186.75 % for GTM screen stability, but all mixes are still within the acceptable ranges specified on the EFNARC standard. In contrast, the results reveal that adding 3 % TWSF to SCC enhances compressive by 31 %, which might be due to the fiber's uneven surface, increasing the bond between the fiber and concrete. As a result, the TWSF can be utilized to strengthen the SCC and fully applied in the construction industry. Additionally, it is advantageous to combine TWSF with SCC to extend its life resulting in lower carbon emissions produced during the production processes.

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.