Proactive measures of governmental debt guarantees to facilitate Public-Private Partnerships project

Governmental Debt Guarantees (GDGs) are often used to encourage involvement by promoters and financial institutions in Public-Private Partnerships (PPP) projects. However, even after demonstrating the bankability of a project and reducing debt cost, the success of the project may be prevented by the lack of long-term commitment from shareholders. Equity contributions by promoters in the project company may be recovered from earnings on short-term construction activities. Based on lesson learned from early PPP projects with GDG, the hold-up problem for government in the view of transaction cost economic (TCE) theory may worsen if the designed contractual structure does not adequately manage opportunistic behaviours from promoters. This study empirically examined the effects of a structured GDG mechanism with particular complementary measures applied in joint projects to develop the Taipei Mass Rapid Transit (MRT) stations. A GDG game model was then applied to bridge the theoretical gap based on the Taipei MRT experience. The analysis shows that requiring the promoter to provide sufficient equity and ensuring the commitment of the lender to provide the loan are the appropriate proactive measures. This study demonstrates its practical value for policy makers by combining case study, TCE and game theory in contractual issues

The SFA-LSSVM as a Decision Support System for Mitigating Liquefaction Disasters

Advanced data mining techniques are potential tools for solving civil engineering problems. This study proposes a novel classification system that integrates smart firefly algorithm (SFA) with least squares support vector machine (LSSVM). SFA is an optimization algorithm which combines firefly algorithm (FA) with smart components, namely chaotic logistic map, chaotic gauss/mouse map, adaptive inertia weight and Lévy flight to enhance optimization solutions. The least squares support vector machine (LSSVM) was adopted in this study for its superior performance of solving real-world problems. Based on the provided engineering data, the analytical results confirm that the SFA-LSSVM has 95.18% prediction accurac

Wildfire Predictions: Determining Reliable Models using Fused Dataset

Wildfires are a major environmental hazard that causes fatalities greater than structural fire and other disasters Computerized models have increased the possibilities of predictions that enhanced the firefighting capabilities in U S While predictive models are faster and accurate it is still important to identify the right model for the data type analyzed The paper aims at understanding the reliability of three predictive methods using fused dataset Performances of these methods Support Vector Machine K-Nearest Neighbors and decision tree models are evaluated using binary and multiclass classifications that predict wildfire occurrence and its severity Data extracted from meteorological database and U S fire database are utilized to understand the accuracy of these models that enhances the discussion on using right model for dataset based on their size The findings of the paper include SVM as the best optimum models for binary and multiclass classifications on the selected fused datase

Project dispute prediction by hybrid machine learning techniques

This study compares several well-known machine learning techniques for public-private partnership (PPP) project dispute problems. Single and hybrid classification techniques are applied to construct models for PPP project dispute prediction. The single classification techniques utilized are multilayer perceptron (MLP) neural networks, decision trees (DTs), support vector machines, the naïve Bayes classifier, and k-nearest neighbor. Two types of hybrid learning models are developed. One combines clustering and classification techniques and the other combines multiple classification techniques. Experimental results indicate that hybrid models outperform single models in prediction accuracy, Type I and II errors, and the receiver operating characteristic curve. Additionally, the hybrid model combining multiple classification techniques perform better than that combining clustering and classification techniques. Particularly, the MLP+MLP and DT+DT models perform best and second best, achieving prediction accuracies of 97.08% and 95.77%, respectively. This study demonstrates the efficiency and effectiveness of hybrid machine learning techniques for early prediction of dispute occurrence using conceptual project information as model input. The models provide a proactive warning and decision-support information needed to select the appropriate resolution strategy before a dispute occurs

Multi-objective symbiotic organisms optimization for making time-cost tradeoffs in repetitive project scheduling problem

Time-cost problems that arise in repetitive construction projects are commonly encountered in project scheduling. Numerous time-cost trade-off approaches, such as mathematical, metaheuristic, and evolutionary methods, have been extensively studied in the construction community. Currently, the scheduling of a repetitive project is conducted using the traditional precedence diagramming method (PDM), which has two fundamental limitations: (1) progress is assumed to be linear from start to finish; and (2) activities in the schedule are connected each other only at the end points. This paper proposes a scheduling method that allows the use of continuous precedence relationships and piece-wise linear and nonlinear activity-time-production functions that are described by the use of singularity functions. This work further develops an adaptive multiple objective symbiotic organisms search (AMOSOS) algorithm that modifies benefit factors in the basic SOS to balance exploration and exploitation processes. Two case studies of its application are analyzed to validate the scheduling method, as well as to demonstrate the capabilities of AMOSOS in generating solutions that optimally trade-off minimizing project time with minimizing the cost of non-unit repetitive projects. The results thus obtained indicate that the proposed model is feasible and effective relative to the basic SOS algorithm and other state-of-the-art algorithms

Quality management platform inspired during COVID-19 pandemic for use by subcontractors in private housing projects

Due to the COVID-19 pandemic in Taiwan, many construction sites must limit the number of people on the jobsite or conduct work independently to avoid the spread of COVID-19. The quality of construction may be in doubt with unclear job handover, especially when workers have COVID-19 infection that should be isolated immediately. On top of that, first-level subcontractor self-inspections are crucial parts of construction process management, and neglecting inspection processes can lead to construction errors and poor quality. To improve current quality inspection methods for private projects, a literature analysis was conducted to identify construction quality management issues that are faced in private housing projects. In-depth interviews with small and medium-sized subcontractors of private housing projects were performed to understand the quality management methods that they use in practice. Next, improvement measures for quality management were formulated and a simplified checklist for private project subcontractors, based on the practical feedback obtained, was created. Finally, the AppSheet platform was used to develop an inspection application for construction, and a subcontractor was invited to confirm its feasibility. The paperless design avoids redundant human contact, and the results of this study greatly facilitate construction practice, particularly during the pandemic. The main contribution of this study is its investigation of the procedures that are used by private project subcontractors to inspect their work for quality management; its results can serve as a reference for academics in evaluating construction quality management levels and improving the management of work by subcontractors to promote safety and health

Nanoscale III-V Semiconductor Photodetectors for High-Speed Optical Communications

Nanophotonics involves the study of the behavior of light on nanometer scale. Modern nanoscale semiconductor photodetectors are important building blocks for high-speed optical communications. In this chapter, we review the state-of-the-art 2.5G, 10G, and 25G avalanche photodiodes (APDs) that are available in commercial applications. We discuss the key device parameters, including avalanche breakdown voltage, dark current, temperature dependence, bandwidth, and sensitivity. We also present reliability analysis on wear-out degradation and optical/electrical overload stress. We discuss the reliability challenges of nanoscale photodetectors associated with device miniaturization for the future. The reliability aspects in terms of high electric field, Joule heating, and geometry inhomogeneity are highlighted