ANN- PSO modelling for predicting buckling of self-compacting concrete column containing RHA properties

Jabar, Abdul Bari; Pradeep, Tejus

doi:10.1590/1517-7076-rmat-2023-0102

Cited by 3 publications

(2 citation statements)

References 35 publications

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“…The control variables are organized into five categories, as indicated in Table 4, and the dependent variables are cost, quality, and time, all of which are related to project completion. To assess the quantifiable impacts of the components, regression analysis models are created, and the performance is tested in equation 1 [27].…”

Section: Regression Analysismentioning

confidence: 99%

Analyzing key success factors in public-private partnership BOT projects: an empirical study on financing influences, insights, and analysis in construction management

Krishnaraj,

Subbaiyan,

Viswanathan

et al. 2024

Matéria (Rio J.)

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The study encompasses various infrastructural amenities such as railways, roads, ports, bridges, tunnels, power plants, hospitals, municipal buildings, and other public-use facilities, which are essential for every country's economic growth. Recognizing the challenges faced by governments in independently providing all infrastructure components, Public Private Partnerships [PPPs] have emerged as a cooperative approach between the public and private sectors. This paper explores the risks, delays, key success factors, contractor selection, and best value selection models in construction project management. In addition, the paper emphasizes the challenges that governments need to address in order to ensure the efficient functioning of the BOT (Built Operate Transfer) network. The challenges in establishing an efficient BOT network are highlighted, and a decision-support system using the .Net framework is developed to aid in the prequalification and rating of BOT marketers. The purpose of this system is to provide the best value to the public procurer by employing regression analysis to rate bidders/applicants and utilizing a composite programming method based on relative weighting.

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Section: Regression Analysismentioning

confidence: 99%

Analyzing key success factors in public-private partnership BOT projects: an empirical study on financing influences, insights, and analysis in construction management

Krishnaraj,

Subbaiyan,

Viswanathan

et al. 2024

Matéria (Rio J.)

View full text Add to dashboard Cite

show abstract

“…In summary, the suggested ANN and ANFIS models estimate the compressive strength of AAC block masonry prisms with good application and dependability. Furthermore, the strength under compression may be determined quickly and with minimal error rates [49].…”

Section: Final Conclusionmentioning

confidence: 99%

Prediction of autoclaved aerated cement block masonry prism strength under compression using machine learning tools

Muthukumar,

Jegatheeswaran

2024

Matéria (Rio J.)

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When the only information available is the issue parameters, and the intended outwards, machine learning techniques like ANN (Artificial Neural Networks) and ANFIS (Adaptive neuro-fuzzy inference system) been proven to address the complex problems without duplicating the phenomena under investigation. The main prompting characteristics are the height-to-the-thicknesses ratio of prisms and the strength under compression of prisms and mortar were analyzed. As inputs, the prototypes are used as blocks and mortars. Both prototypes were accomplished and evaluated. Thirty-six data sets were gathered for testing in addition to verified technical and subsequently comparison with other empirical computation methods served to validate. The outwards show that the suggested prototypes have good forecast capabilities with negligible error rates. To assess and compare the structural behavior of structural completion of AAC block with the other types. At last, both the machine learning tools are good application and dependability.

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Enhancing concrete performance through iron slag substitution: analysis of mechanical, durability and microstructural studies

Radhakrishnan,

Palaniraj,

Nirmalraj

et al. 2024

Matéria (Rio J.)

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ANN- PSO modelling for predicting buckling of self-compacting concrete column containing RHA properties

Cited by 3 publications

References 35 publications

Analyzing key success factors in public-private partnership BOT projects: an empirical study on financing influences, insights, and analysis in construction management

Analyzing key success factors in public-private partnership BOT projects: an empirical study on financing influences, insights, and analysis in construction management

Prediction of autoclaved aerated cement block masonry prism strength under compression using machine learning tools

Enhancing concrete performance through iron slag substitution: analysis of mechanical, durability and microstructural studies

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