Modelling the Effectiveness of PPP Road Infrastructure Projects by Applying Random Forests

Rudzianskaite-Kvaraciejiene, Ruta; Apanavičienė, Rasa; Gelžinis, Adas

doi:10.3846/13923730.2014.971129

Cited by 19 publications

(10 citation statements)

References 28 publications

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“…Technical, financial, private partner selection and all three sustainability aspects were incorporated in the Model for effectiveness evaluation of public-private partnership (PPP) automobile road infrastructure construction projects developed by Rudžianskaitė-Kvaraciejienė et al [58]. This study revealed that the effectiveness of PPP infrastructure projects from the private partner perspective depends on technical, financial and private partner's competence factors, and project sustainability metrics as well.…”

Section: Methodology For Real Estate Investment Evaluation Based On Smentioning

confidence: 99%

Sustainability Aspects of Real Estate Development: Lithuanian Case Study of Sports and Entertainment Arenas

et al. 2015

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Abstract:The principles of sustainability have become particularly important in the real estate and construction sector over the past decade. Despite extended efforts to evaluate the sustainability aspects of the real estate development, very few attempts have been exposed in the scientific literature pursuing integrating the investors and real estate developers' responsibility for the local community, regional economy and environmental problems within their financial expectations. The paper presents methodology enabling evaluating the balance of real estate development sustainability and financial viability issues during the feasibility study process. The Model for Real Estate Investment Evaluation Based on Sustainable Development Aspects proposed by the authors was applied to the case study of Lithuanian sports and entertainment arenas. By using multi-criteria decision analysis sustainability assessment of multi-functional arenas was carried out which disclosed that positive sustainability effects lays down on the owner's and operator's monetary liabilities.

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Section: Methodology For Real Estate Investment Evaluation Based On Smentioning

confidence: 99%

Sustainability Aspects of Real Estate Development: Lithuanian Case Study of Sports and Entertainment Arenas

et al. 2015

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“…The study revealed that the ensemble models provided better accuracy that the individual models. Rudžianskaitė-Kvaraciejienė et al (2015) Integrated a Random Forest algorithm with the modeling of public-private partnerships in infrastructure projects [26]. The method performed with good accuracy in the prediction process for public and private projects.…”

Section: Research Backgroundmentioning

confidence: 99%

“…The algorithm gained significant important because it is invariant under scaling and it is robust to the inclusion of irrelevant features [38]. Several studies examined the application of Random Forest in engineering applications and demonstrated its feasibility in prediction processes [26,34,35]. Under the bootstrapping method, the data during the training phase are selected randomly and independently to develop an RF model, and the data that are not involved in the selection process are named "out-of-bag" [39].…”

Section: Random Forest Modelmentioning

confidence: 99%

Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Ali

Salih

et al. 2020

Sustainability

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Project delays are the major problems tackled by the construction sector owing to the associated complexity and uncertainty in the construction activities. Artificial Intelligence (AI) models have evidenced their capacity to solve dynamic, uncertain and complex tasks. The aim of this current study is to develop a hybrid artificial intelligence model called integrative Random Forest classifier with Genetic Algorithm optimization (RF-GA) for delay problem prediction. At first, related sources and factors of delay problems are identified. A questionnaire is adopted to quantify the impact of delay sources on project performance. The developed hybrid model is trained using the collected data of the previous construction projects. The proposed RF-GA is validated against the classical version of an RF model using statistical performance measure indices. The achieved results of the developed hybrid RF-GA model revealed a good resultant performance in terms of accuracy, kappa and classification error. Based on the measured accuracy, kappa and classification error, RF-GA attained 91.67%, 87% and 8.33%, respectively. Overall, the proposed methodology indicated a robust and reliable technique for project delay prediction that is contributing to the construction project management monitoring and sustainability.Sustainability 2020, 12, 1514 2 of 14 in terms of litigation, dispute and arbitration [5]. Delays are caused by many sources and factors such as the owner [6,7], designer [3,8], contractor [4,7], materials [4,7], project [7,8], labor [9] and external factors [3,10]. Literature ReviewThe prediction of project delay based on internal and external sources can help project managers to provide an accurate forecast of the project schedule, and this can assist a proactive management approach in the construction project [11]. Construction projects are dynamic and complex, included a huge number of project stockholders, feedback processes and non-linear relationships [12]. The existence of a delay problem is related to interdependent factors that affect the construction project and the complexity and uncertainty of construction activities. Thus, providing of an efficient tool for analyzing delay factors is key for estimating an accurate duration in construction projects [11].By recalling previous studies, Chan (2001) used regression analysis to identify time-cost relationships for building projects in Malaysia [2]. This approach was developed for managers and owners to estimate the average time that is required for project delivery. Chan and Chan (2004) performed multiple regression exercises to analyze data related to the time performance of construction projects [13]. The results indicated that multiple regression was used as a useful method to predict time performance in construction projects. Rezaie et al. (2007) used Monte Carlo analysis to investigate the effects of uncertainties on the schedule performance [14]. The results revealed that this method is a good tool to simulate the relationship of uncertainties of construct...

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“…A number of studies explored RF's application in engineering applications and demonstrated its viability in prediction processes (Rudžianskaitė-Kvaraciejienė et al 2015;Yaseen et al 2019aYaseen et al , 2019bShirzad and Safari 2019). Under the bootstrapping method, data is selected randomly and independently during the training phase to develop the RF model, and data not involved in the selection process is referred to as "out-of-bag" (Catani et al 2013).…”

Section: Study Area and Modeling Datamentioning

confidence: 99%

Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction

Meshram

Safari

Khosravi

et al. 2020

Environ Sci Pollut Res

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Suspended sediment load is a substantial portion of the total sediment load in rivers and plays a vital role in determination of the service life of the downstream dam. To this end, estimation models are needed to compute suspended sediment load in rivers. The application of artificial intelligence (AI) techniques has become popular in water resources engineering for solving complex problems such as sediment transport modeling. In this study, novel integrative intelligence models coupled with iterative classifier optimizer (ICO) are proposed to compute suspended sediment load in Simga station in Seonath river basin, Chhattisgarh State, India. The proposed models are hybridization of the random forest (RF) and pace regression (PR) models with the iterative classifier optimizer (ICO) algorithm to develop ICO-RF and ICO-PR hybrid models. The recommended models are established using the discharge and sediment daily data spanning a 35-year period . The accuracy of the developed models is examined in terms of error; by root mean square error (RMSE) and mean absolute error (MAE); and based on a correlation index of determination coefficient (R 2 ). The proposed novel hybrid models of ICO-RF and ICO-PR have been found to be more precise than their stand-alone counterparts of RF and PR. Overall, ICO-RF models delivered better accuracy than their alternatives. The results of this analysis tend to claim the appropriateness of the implemented methodology for precise modeling of the suspended sediment load in rivers.

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Modelling the Effectiveness of PPP Road Infrastructure Projects by Applying Random Forests

Cited by 19 publications

References 28 publications

Sustainability Aspects of Real Estate Development: Lithuanian Case Study of Sports and Entertainment Arenas

Sustainability Aspects of Real Estate Development: Lithuanian Case Study of Sports and Entertainment Arenas

Prediction of Risk Delay in Construction Projects Using a Hybrid Artificial Intelligence Model

Iterative classifier optimizer-based pace regression and random forest hybrid models for suspended sediment load prediction

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