A novel hybrid intelligent approach for contractor default status prediction

Default Behaviors of Contractors under Surety Bond in Construction Industry Based on Evolutionary Game Model

In construction projects, some contractors will take default actions against the contracts to obtain maximum profits and damage the owners’ benefits as a result. In the construction markets where effective supervision is not performed well, contractors have more opportunities to default. Surety bonds were designed to solve the default problems and promote the sustainable development of the construction markets. This paper was proposed to explore the interactions between owners and contractors and investigate the influence of surety bonds (high penalty and low penalty) on the default behavior of contractors based on a static and dynamic evolutionary game analysis model. The results showed that applying the surety bond strategy is effective at decreasing the probability of the contractors’ default behavior when the credit system based on a surety bond system is well developed in the construction industry and the cost of the surety bond is low enough. Therefore, government strategies such as a better development of the credit system driven by surety bonds and the subsidies on surety bonds to reduce the cost can mitigate the contractors’ default behavior and keep the sustainability of the construction markets.

A framework for big data analytics approach to failure prediction of construction firms

This study explored use of big data analytics (BDA) to analyse data of a large number of construction firms to develop a construction business failure prediction model (CB-FPM). Careful analysis of literature revealed financial ratios as the best form of variable for this problem. Because of MapReduce’s unsuitability for iteration problems involved in developing CB-FPMs, various BDA initiatives for iteration problems were identified. A BDA framework for developing CB-FPM was proposed. It was validated by using 150,000 datacells of 30,000 construction firms, artificial neural network, Amazon Elastic Compute Cloud, Apache Spark and the R software. The BDA CB-FPM was developed in eight seconds while the same process without BDA was aborted after nine hours without success. This shows the issue of not wanting to use large dataset to develop CB-FPM due to tedious duration is resolvable by applying BDA technique. The BDA CB-FPM largely outperformed an ordinary CB-FPM developed with a dataset of 200 construction firms, proving that use of larger sample size with the aid of BDA, leads to better performing CB-FPMs. The high financial and social cost associated with misclassifications (i.e. model error) thus makes adoption of BDA CB-FPMs very important for, among others, financiers, clients and policy makers.