article · Results in Engineering
Automated load analysis technologies offer a pathway to improved eco-efficiency across mining and construction activities. Combining onboard weighing systems, the Internet of Things, and machine learning enables precise load management alongside traditional operational workflows. Detailed case studies demonstrate that integrating these tools yields measurable operational improvements, including reduced fuel consumption, optimised resource allocation, and enhanced on-site safety. These interventions concurrently reduce the overall environmental footprint of industrial processes. Beyond technical performance, adopting automated systems entails broader operational adjustments, notably workforce transformation and structured stakeholder engagement. Establishing coordinated policy frameworks and adoption strategies supports the wider implementation of automated load management, helping heavy industrial sectors balance economic performance with sustainable development objectives.
Heavy industries such as mining and construction face mounting pressure to balance economic productivity with environmental responsibility. Using automated data collection and machine learning to track vehicle loads enables operations to lower fuel consumption and emissions while improving workplace safety. This demonstrates how modern industrial technologies can help resource-intensive sectors advance towards sustainable development goals without sacrificing operational performance.
The findings apply directly to mining and construction operators, particularly fleet managers and site engineers seeking to cut fuel overheads and improve resource allocation. Evaluated through detailed case studies using existing tools such as onboard weighing, the Internet of Things, and machine learning, the technology represents an applied and tested intervention. Practical deployment relies on workforce upskilling, operational restructuring, and supportive policies to drive adoption across diverse worksites.
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In the face of escalating global demands for sustainable practices within the construction and mining sectors, this paper investigates the transformative impact of automated load analysis technologies. Focused on bridging the gap between traditional operational methodologies and the forefront of automation technology, the study provides an in-depth examination of the integration of onboard weighing systems, the Internet of Things (IoT), and machine learning into mining operations. Through a series of detailed case studies, the research showcases how these technological innovations contribute to substantial improvements in operational efficiency, notably through enhanced load management, reduced fuel consumption, and optimized resource allocation, thereby fostering a decrease in the environmental footprint of mining activities. Furthermore, the paper addresses critical sustainability issues, including workforce transformation, stakeholder engagement, and the broader environmental implications of adopting automated technologies in mining processes. Concluding with strategic policy recommendations, the study advocates for widespread adoption of automated systems within the construction sector to achieve improved environmental and economic outcomes. By emphasizing a multidisciplinary approach, this research highlights the essential role of technological innovation in aligning mining operations with sustainable development goals, positioning automated load analysis as a pivotal strategy for advancing eco-efficiency in the construction and mining industries. • Innovates mining operations with IoT and machine learning for eco-efficiency. • Demonstrates fuel savings and operational efficiency via automated analysis. • Enhances mining safety and reduces environmental impact through technology. • Advocates for policy changes supporting sustainable mining practices. • Provides a framework for industry-wide adoption of automated load analysis.
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DOI: 10.1016/j.rineng.2024.102890
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