Nolan C. Concha
19 Publications
Scopus ID: 85084765618
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-4
Conference Paper | Published: November 1, 2019
Abstract
The use of reinforced concrete as a structural material originates from two different components; Reinforcing steel, known as a highly-tensile material, combined with concrete, a highly-compressive material, act together to resist different types of loadings. However, this composite action is dependent upon the transfer of loads between the steel and concrete known as the bond and is concluded to be on a form of continuous stress along the juncture of steel and concrete. This study focused on producing a bond stress model as a function of concrete compressive strength, embedment length and bar diameter, using single pullout test, with the help of multiple regression analysis by using Microsoft Excel. For the compressive strength, 40 MPa, 50MPa and 60MPa were used, with 50mm, 75mm and 100mm embedment length and bar diameters of 12mm, 16mm and 20mm, producing a total of 27 specimens. After subjecting each specimen to pull out test, data were tabulated and analyzed using Multiple Regression producing a model with 97.81% correlation coefficient. The model was then subjected to parametric testing and it was concluded that increasing the compressive strength of concrete would result to an increase in bond stress; however, increasing the embedment length and bar diameter would result to a decrease in bond stress.
Scopus ID: 85084763596
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management ( HNICEM ), (2019), pp. 1-4
Conference Paper | Published: November 1, 2019
Abstract
Cost estimation of building demolition, like any engineering project, requires ample amount of time and experience to accomplish since it involves calculations of complex relationships between its influencing factors. Since artificial neural networks (ANNs) are known to be effective in the cost-forecasting domain with complex parameters involve, the study aims to develop an ANN that can predict building demolition cost in Quezon City. One-hundred demolition projects from the Department of Building Official in Quezon City were gathered, evaluated and divided randomly into two sets: 90% for training, validation and internal testing and 10% for external application. Nine demolition cost-influencing factors were identified, namely: building condition, materials and classification, number of floors, total floor area, site accessibility, location, demolition methods used and debris removal options. The training was applied with feedforward backpropagation algorithm. The resulting architecture for the selected ANN model consists of 12 hidden nodes. The model tested and was successful in predicting demolition cost in Quezon City with an average accuracy rating of 90.21%.
Scopus ID: 85071036629
International Journal of GEOMATE, (2019), Vol. 16, No. 57
Journal Article | Published: January 1, 2019
Abstract
The production of fiber-reinforced concrete presents a complex reaction system, posing significant challenges in determining appropriate material proportions to achieve targeted mechanical properties. To address this issue, this study proposes novel Artificial Neural Network (ANN) models for predicting the compressive and flexural strengths of fiber-reinforced concrete using a backpropagation feed-forward algorithm. A wide range of concrete mix designs was prepared and tested using cylindrical samples for compressive strength and beam samples for flexural strength. Polypropylene fibers were incorporated into the mixes, and all specimens were cured for 28 days in a water-saturated lime solution. The results demonstrated that the ANN models produced strength predictions that closely aligned with experimental data, yielding high correlation values of 99.46% and 98.57% for compressive and flexural strengths, respectively. The best-fit models exhibited mean squared errors of 0.0024 (compressive) and 0.44 (flexural). Furthermore, parametric analysis indicated that the proposed models effectively captured the constitutive relationships among the concrete components and successfully represented the dominant mechanical behavior of the tested specimens.
Scopus ID: 85057242059
IOP Conference Series: Materials Science and Engineering, (2018), Vol. 431, pp. 072009
Nolan C. Concha
& Andres Winston C. OretaJournal Article | Published: November 15, 2018
Abstract
o monitor the initiation of concrete cracking beyond the service life of the structure, a novel prediction model of time to cracking of concrete cover using artificial neural network (ANN) was developed in this study. Crack mitigation prevents corrosion and crack development to occur in a more rapid phase that is an essential component in performance-based durability design of reinforced concrete structures. Data available in various literatures were used in the development of the ANN model which is a function of compressive strength, tensile strength, concrete cover, rebar diameter, and current density. The neural network model was able to provide reasonable results in time predictions of cracking of concrete protective cover due to formations of corrosion products. The performance of ANN model was also compared to various analytical and empirical models and was found to provide better prediction results. Even with limitations in the available training data, the ANN model performed well in simulating cracking of concrete due to reinforcement corrosion.
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: 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: 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: 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.
Scopus ID: 85023603612
Materials Science Forum, (2016), Vol. 866, pp. 9-13
Journal Article | Published: January 1, 2016
Abstract
One of the most useful innovations in concrete technology is Self Compacting Concrete that has the ability to flow efficiently and maintain material homogeneity. The rapid change in the behavior of concrete due to accelerating admixtures can significantly affect the workability properties of the mixture and reduce its ability to flow efficiently. To describe the influence of superplasticizers blended with accelerant on the rheological properties of SCC, several mixtures were tested for Slump Flow, L-Box, and Screen Stability tests. Artificial neural network was used to obtain a model describing the constitutive relationships between the material components and workability parameters of SCC and was optimized using Genetic Algorithm. 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. Most of the design samples of SCC with blended superplasticizer and sodium lignosulfate accelerant were not able to perform well in the flowing ability due to inefficiency of the fresh SCC to flow. The increasing dosage of accelerant however rendered strong stability between the concrete particles allowing the SCC samples to resist segregation and maintain material homogeneity.