A statistical methodology for reliable evaluation of calibrated dynamic modulus regression models

Santos, Isadora Guimarães dos; Espíndola, Rogério Pinto; Aragão, Francisco Thiago Sacramento; Nascimento, Luis Alberto Herrmann

doi:10.1590/1517-7076-rmat-2023-0188

Cited by 1 publication

(1 citation statement)

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“…This statistically significant data provides valuable insights into BOT project construction in India. The study also indicates the obstacles the government must overcome in order for the BOT network to operate properly [25]. This research investigates a novel approach to promoter selection for India's BOT projects within Public-Private Partnerships (PPP), prioritizing public sector value.…”

Section: Methodsmentioning

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: Methodsmentioning

confidence: 99%