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Current solutions for monitoring the progress of pavement construction (such as collecting, processing and analysing data) are inefficient, labour-intensive, time-consuming, tedious and error-prone. In this study, an automated solution proposes sensors prototype mounted unmanned ground vehicle (UGV) for data collection, an LSTM classifier for road layer detection, the integrated algorithm for as-built progress calculation and web-based as-built reporting.

The crux of the proposed solution, the road layer detection model, is proposed to develop from the layer change detection model and rule-based reasoning. In the beginning, data were gathered using a UGV with a laser ToF (time-of-flight) distance sensor, accelerometer, gyroscope and GPS sensor in a controlled environment. The long short-term memory (LSTM) algorithm was utilised on acquired data to develop a classifier model for layer change detection, such as layer not changed, layer up and layer down.

In controlled environment experiments, the classification of road layer changes achieved 94.35% test accuracy with 14.05% loss. Subsequently, the proposed approach, including the layer detection model, as-built measurement algorithm and reporting, was successfully implemented with a real case study to test the robustness of the model and measure the as-built progress.

The implementation of the proposed framework can allow continuous, real-time monitoring of road construction projects, eliminating the need for manual, time-consuming methods. This study will potentially help the construction industry in the real time decision-making process of construction progress monitoring and controlling action.

This first novel approach marks the first utilization of sensors mounted UGV for monitoring road construction progress, filling a crucial research gap in incremental and segment-wise construction monitoring and offering a solution that addresses challenges faced by Unmanned Aerial Vehicles (UAVs) and 3D reconstruction. Utilizing UGVs offers advantages like cost-effectiveness, safety and operational flexibility in no-fly zones.

The aim of this study is to broaden the understanding of the set of knowledge, skills, attributes and experience (KSAE) that teams should demonstrate and the necessary roles they need to play within the team in Relational Contracting (RC). This research seeks to answer three questions: first, what KSAEs are required in a team operating under RC, second, which of the identified KSAEs are more important to enable an integrated team to perform effectively and third, how do these required KSAEs correspond to the major role clusters identified in Belbin’s team role model?

In-depth interviews with 25 experts and key management representatives in infrastructure construction in Australia were conducted to enable detailed discussion of the research questions.

Sixteen behavioural traits and four knowledge and experience areas were identified. The findings highlight that in RC, team members and especially leaders and managers need to be competent in people-oriented roles, above all others.

This research mainly captured the perspectives of personnel working in state government infrastructure departments. Further research is recommended to explore the perceptions of employees in private construction companies.

By aligning the roles required for RC with the team role clusters of the Belbin’s team roles assessment tool, this study will be useful for identifying suitable members to form high-performance project teams.

The findings of this paper can inform government infrastructure organisations and construction companies as to which roles are more critical when selecting fit-for-purpose teams to successfully deliver large infrastructure projects procured under the RC method.

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