Search results

The lack of knowledge has hindered the successful implementation of lean in the construction industry. This has alarmed the need for lean learning practices. Out of numerous models, the learning-to-learn sand cone model received a wider acknowledgment for learning practices. Thus, this study aims to propose a learning-to-learn sand cone model integrated lean learning framework for the construction industry.

The research adopted an interpretivism stance. A qualitative research approach was adopted for the study. Consequently, fifteen (15) semi-structured interviews and document reviews were carried out to collect data in three (3) cases selected through purposive sampling. Code-based content analysis was used to analyse the data.

Fifty-two (52) sub-activities pertaining to nine lean learners at each stage of the lean learning procedure were identified. The most significant practices in the lean learning procedure to continuously improve lean learning in the organisation were maintaining records, providing a performance update to senior management and preparing and distributing several hierarchical manuals for all levels of staff to aid in the implementation of lean approaches.

The findings of the research can be aided to successfully implement lean by following the identified sub-activities via various parties within the organisation. The proposed lean learning framework opens several research areas on lean learning in the construction industry. This is the first research to uncover a lean learning framework in the construction industry rather than at the educational institute level.

Cost overrun in infrastructure projects is a constant concern, with a need for a proper solution. The current estimation practice needs improvement to reduce cost overruns. This study aimed to find possible statistical modelling techniques that could be used to develop cost models to produce more reliable cost estimates.

A bibliographic literature review was conducted using a two-stage selection method to compile the relevant publications from Scopus. Then, Visualisation of Similarities (VOS)-Viewer was used to develop the visualisation maps for co-occurrence keyword analysis and yearly trends in research topics.

The study found seven primary techniques used as cost models in construction projects: regression analysis (RA), artificial neural network (ANN), case-based reasoning (CBR), fuzzy logic, Monte-Carlo simulation (MCS), support vector machine (SVM) and reference class forecasting (RCF). RA, ANN and CBR were the most researched techniques. Furthermore, it was observed that the model's performance could be improved by combining two or more techniques into one model.

The research was limited to the findings from the bibliometric literature review.

The findings provided an assessment of statistical techniques that the industry can adopt to improve the traditional estimation practice of infrastructure projects.

This study mapped the research carried out on cost-modelling techniques and analysed the trends. It also reviewed the performance of the models developed for infrastructure projects. The findings could be used to further research to develop more reliable cost models using statistical modelling techniques with better performance.