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PurposeThis study seeks to understand the connection of methodology by finding relevant papers and their full review using the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” (PRISMA).Design/methodology/approachConcrete-filled steel tubular (CFST) columns have gained popularity in construction in recent decades as they offer the benefit of constituent materials and cost-effectiveness. Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Gene Expression Programming (GEP) and Decision Trees (DTs) are some of the approaches that have been widely used in recent decades in structural engineering to construct predictive models, resulting in effective and accurate decision making. Despite the fact that there are numerous research studies on the various parameters that influence the axial compression capacity (ACC) of CFST columns, there is no systematic review of these Machine Learning methods.FindingsThe implications of a variety of structural characteristics on machine learning performance parameters are addressed and reviewed. The comparison analysis of current design codes and machine learning tools to predict the performance of CFST columns is summarized. The discussion results indicate that machine learning tools better understand complex datasets and intricate testing designs.Originality/valueThis study examines machine learning techniques for forecasting the axial bearing capacity of concrete-filled steel tubular (CFST) columns. This paper also highlights the drawbacks of utilizing existing techniques to build CFST columns, and the benefits of Machine Learning approaches over them. This article attempts to introduce beginners and experienced professionals to various research trajectories.

In today's age of globalization and internalization, precise and meaningful data plays a fundamental role in making crucial decisions across every strata of business, whether it is associated with human resource or sales or production or marketing or for any other field. For example, Apple, Facebook, Twitters and others know the next emerging trend in the market and transmute its strategies to adapt to the recent trends. All these multi-billionaire giants have the power of analysing the myriad of data flowing in from sundry of sources like social media, market trends, past annals and lot more. They are all accoutred with the most-indispensable tool in current globalized economy, "The BIG DATA." It refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyse. With the advent of Big Data, the business houses now can capture various trends in the market with minimal cost incurred. It had helped individuals as well as the organization to make proper decisions and increase the base of their operations by targeting the apt customers for the business. Big Data is playing a pivotal role in transfiguring the business decisions. On the other side, there are numerous challenges presented by the 'Big Data," like security and privacy issues, infrastructure failure, and others. Thus, this paper presents the concept of Big Data, opportunities derived by companies using cases, and challenges related to Big Data. Since, Big Data is in its embryological phase; this paper will provide a snapshot to the scholar for future research in this field.

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