Adaptation of a Cost Overrun Risk Prediction Model to the Type of Construction Facility

Construction of a financial default risk prediction model based on the LightGBM algorithm

The construction of a financial risk prediction model has become the need of the hour due to long-term and short-term violations in the financial market. To reduce the default risk of peer-to-peer (P2P) companies and promote the healthy and sustainable development of the P2P industry, this article uses a model based on the LightGBM (Light Gradient Boosting Machine) algorithm to analyze a large number of sample data from Renrendai, which is a representative platform of the P2P industry. This article explores the base LightGBM model along with the integration of linear blending to build an optimal default risk identification model. The proposed approach is applicable for a large number of multi-dimensional data samples. The results show that the prediction accuracy rate of the LightGBM algorithm model on the test set reaches 80.25%, which can accurately identify more than 80% of users, and the model has the best prediction performance in terms of different performance evaluation indicators. The integration of LightGBM and the linear blending approach yield a precision value of 91.36%, a recall of 75.90%, and an accuracy of 84.36%. The established LightGBM algorithm can efficiently identify the default of the loan business on the P2P platform compared to the traditional machine learning models, such as logistic regression and support vector machine. For a large number of multi-dimensional data samples, the LightGBM algorithm can effectively judge the default risk of users on P2P platforms.

Symmetric and Asymmetric Data in Solution Models

This Special Issue covers symmetric and asymmetric data that occur in real-life problems. We invited authors to submit their theoretical or experimental research to present engineering and economic problem solution models that deal with symmetry or asymmetry of different data types. The Special Issue gained interest in the research community and received many submissions. After rigorous scientific evaluation by editors and reviewers, seventeen papers were accepted and published. The authors proposed different solution models, mainly covering uncertain data in multi-criteria decision-making problems as complex tools to balance the symmetry between goals, risks, and constraints to cope with the complicated problems in engineering or management. Therefore, we invite researchers interested in the topics to read the papers provided in the Special Issue.

The Implementation Factors of Information and Communication Technology in the Life Cycle Costs of Buildings

Life cycle cost management is an integral part of buildings construction. The life cycle cost approach can be considered an objective approach because it considers all life cycles of buildings. Information and communication technology is one of the critical factors for the success of construction projects. Several studies point to the importance of information and communication technology use in life cycle cost management. Generally, information and communication technology can be helpful in the cost management process of buildings. However, few implementation factors of information and communication technology are used in the life cycle cost management of buildings. The research assumes that the most critical implementation factor is the investment cost for information and communication technologies used in cost management during the life cycle. The relative importance index method was used to evaluate and quantify the final rank of implementation factors. The Kruskal–Wallis test was used to confirm or reject research results that were statistically significant.

Application of Game Theory against Nature in Supporting Bid Pricing in Construction

The problem of setting prices for construction works is significant for both the investor and contractor companies. The periodically occurring instabilities in the economy require investment process participants to perform detailed market analyses and assessments, as well as to monitor price forecasts in construction. It is only after such an assessment that it is possible within the framework of the initial assumptions for cost calculation to select the basis for setting prices and their levels. The identification and analysis of the risks related to uncontrolled price increases allow contractors to secure their businesses by developing bids that ensure maximum profit. In view of the above, this study proposes an approach based on the use of game theory against nature to identify the optimal variant of a bid estimate. The study considers price forecasts for construction products, which may reduce the negative impact in case the prices increase. The obtained results confirmed the effectiveness of the used decision-making support methods, indicating the optimal strategy to reduce financial losses in times of market instability. The proposed approach also allows for a balance (symmetry) between maximum profit and probability of winning the contract.

Decision Making for Project Appraisal in Uncertain Environments: A Fuzzy-Possibilistic Approach of the Expanded NPV Method

The major drawback of the classic approaches for project appraisal is the lack of the possibility to handle change requests during the project’s life cycle. This fact incorporates the concept of uncertainty in the estimation of this investment’s worth. To resolve this issue, the authors use fuzzy numbers, possibilistic moments of fuzzy numbers and the hybrid (fuzzy statistic) fuzzy estimators’ method in order to introduce a fuzzy possibilistic version of the expanded net present value method (FPeNPV). This approach consists of two factors: the fuzzy possibilistic NPV and the fuzzy option premium. For the estimation of the fuzzy NPV, some basic assumptions are taken into consideration: (1) the opportunity cost of capital, used as the present value interest factor calculated through the weighted average cost of capital (WACC), (2) the equity cost, determined through the possibilistic set-up of the capital asset pricing model CAPM, and (3) the inflation factor, also included in the estimation of the NPV. The fuzzy estimators’ method is used for the computation of the fuzzy option premium. An algorithm of nine major steps leads to the computation of the FPeNPV. This gives the administration the opportunity to adapt to potential changes in the company’s internal and external environments. In this way, the symmetry between the planning and execution phase of a project can be reinstated. The results validate the statement that fuzzy and intelligent methods remain valuable tools to express uncertainty in various scientific areas. Finally, an illustrative example aims at a thorough comprehension of this new approach of the expanded NPV method.