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Like several of its neighbouring countries, the Kingdom of Saudi Arabia (KSA) has seen a move away from an oil-based economy towards a knowledge-based economy in recent decades. Research productivity in the Kingdom is increasing quickly along with international collaborations. Nonetheless, postgraduate research at universities in KSA is a relatively young domain, which certainly is a core factor in the evolving research management structures in the country.

Going forward, increased openness in research, along with continuing significant investment into higher education and research both by the government and the strong private sector points to a need for refined research governance and policy frameworks with designated expert research management staff developing and facilitating the underlying processes to enable Saudi institutions to engage at the forefront of academic research.

Utility strikes have spawned companies specializing in providing a priori analyses of the underground. Geophysical techniques such as Ground Penetrating Radar (GPR) are harnessed for this purpose. However, analyzing GPR data is labour-intensive and repetitive. It may therefore be worthwhile to amplify this process by means of Machine Learning (ML). In this work, harnessing the ADR design science methodology, an Intelligence Amplification (IA) system is designed that uses ML for decision-making with respect to utility material type. It is driven by three novel classes of Convolutional Neural Networks (CNNs) trained for this purpose, which yield accuracies of 81.5% with outliers of 86%. The tool is grounded in the available literature on IA, ML and GPR and is embedded into a generic analysis process. Early validation activities confirm its business value.