Big Data with Deep Learning for Benchmarking Profitability Performance in Project Tendering Abstract   A reliable benchmarking system is crucial for the contractors to evaluate the profitability performance of project tenders. Existing benchmarks are ineffective in the tender evaluation task for three reasons. Firstly, these benchmarks are mostly based on the profit margins as the only key performance indicator (KPI) while there are other KPIs fit to drive the evaluation process. Secondly, these benchmarks donxe2x80x99t take project context into account, thereby restricts their predictive accuracy. And finally, these benchmarks are obtained from small subsets of data, making it hard to generalise. As a result, estimators cannot probe into tenders to judge the strengths and weaknesses of their bids. This advancement is critical for not only choosing more lucrative opportunities but also driving negotiations during the tendering process.  This study aims to develop a benchmarking system for tender evaluation using Big Data of 1.2 terabytes, comprising 5.7 million cells. A holistic list of seventeen (17) KPIs is identified from the email data using Text Mining approaches. Besides, eight (8) key project attributes are chosen for ensuring contextaware benchmarking using Focused Group Interviews (FGIs). At the crux of this work lies the proposition of a deep ensemble learner based on the decompositionintegration methodology. In the decomposition stage, the model predicts several attributespecific benchmarks for each KPI using our proposed contextaware algorithm. In the integration stage, deep neural networkbased learners are trained to generate final projectsensitive KPI benchmark. The learner is deployed in the  Spring  tool to support the tender evaluation of power infrastructure projects. A tender of 60km underground cabling project is evaluated using the proposed learner. The system spontaneously identified KPIs in the tender that require further attention to achieve greater profitability performance.

Predicting Profit Performance of International Construction Projects This study developed a practical casebased reasoning (CBR) model to predict the profit performance of international construction project (ICP) due to the nature of the prediction as a projectbased, unstructured, and small sample issue. The proposed model helps contractors to make reasonable bid decision or risk treatment in the early stage of ICPs by retrieving similar cases. Grey relational analysis is applied to respectively measure the weight and variable similarity so that case similarity can be scored. Finally, the proposed model is tested on the basis of 30 actual overseas rail projects. The results indicate that the proposed CBR model performs at a higher level of accuracy than other classical CBR models. A structured process to choose appropriate methods for CBR model development by considering the prediction accuracy performance of methods available is also included.