Kehinde Elelu: Harnessing Technology to Build a Safer Engineering Future 

On construction sites across the world, danger often arrives without warning. The roar of excavators, reversing trucks and heavy cranes blends into a constant mechanical hum, making it difficult for workers to distinguish between routine activity and imminent danger. In many cases, accidents happen not because workers are careless, but because they simply cannot see or hear what is coming.

It is within this complex and hazardous environment that Nigerian civil engineering researcher, Kehinde Abdulsalam Elelu, is gaining recognition for his research on artificial intelligence-driven safety systems designed to prevent accidents on construction sites.

Now a PhD candidate in the department of civil engineering at Clemson University in the United States, Elelu is part of a new generation of researchers applying artificial intelligence and advanced sensing technologies to address one of the most persistent challenges in infrastructure development, preventing workplace accidents before they happen.

His work explores how machines can learn to interpret sound, warn workers of danger offering a proactive safety measure towards against risk factors on construction sites. 

From Ilorin to the frontiers of engineering research

Elelu’s path into engineering began in Nigeria, where he studied at the University of Ilorin, graduating with a Bachelor of Science degree in Civil Engineering in 2018.

Like many young engineers trained in developing countries, he was acutely aware of the safety challenges that confront workers in sectors such as construction, where rapid urban expansion often outpaces the enforcement of safety regulations. This awareness would later shape the direction of his academic work.

Seeking to deepen his expertise, Elelu proceeded to the United States for postgraduate studies at Clemson University, where he his currently doing his PhD in Civil Engineering. His ongoing doctoral research is helping to lay the foundation for a new approach to construction safety that blends engineering with artificial intelligence.

As a Ph.D. candidate, his doctoral research, titled “Collision Hazard Prevention and Notification for Construction Worker Safety Using Audio Surveillance,” investigates how sound-based sensing systems can detect heavy machinery movement and provide real-time safety alerts to construction workers.

The motivation behind the research is clear—across the global construction industry, a significant proportion of fatalities with accidents often happen because the approaching machinery was not noticed on time.

His research proposes a novel audio-based framework capable of detecting the movement of heavy construction equipment and alerting workers to potential collision risks. Using machine learning algorithms and multi-channel audio sensing, the system identifies distinctive sounds produced by machinery and determines their location in real time.

The objective of his doctoral research is to reduce one of the most common causes of accidents in construction involving heavy equipment.

Teaching machines to recognise danger through sound

Construction sites are notoriously difficult environments for traditional monitoring systems. Dust, changing weather conditions, limited visibility and constant movement of heavy duty machines often reduce the effectiveness of camera-based safety technologies.

Elelu’s research uses machine learning models trained to detect construction equipment through sound alone. These systems can then trigger alerts through wearable devices worn by workers on site.

According to him, these “Low-cost wearable technologies powered by audio-based machine learning systems can detect the presence and motion of heavy equipment in noisy construction environments. By delivering real-time alerts, the system improves situational awareness and helps reduce human-equipment collisions.”

The innovation lies not only in the technology itself but also in its practicality. Unlike many existing safety systems that require expensive sensors to be mounted on machinery, Elelu’s approach relies on relatively inexpensive audio sensors and edge computing units making it easier and cheaper to deploy in developing economies.

Research grounded in real-world challenges

Elelu’s academic work sits at the intersection of civil engineering, computer science and data science. His research interests span sound processing, machine learning, audio surveillance and construction safety technologies.

Among his notable publications is a 2024 study in the journal Buildings which proposed a neural-network framework capable of detecting and localising equipment sounds using multi-channel audio signals. The research demonstrated how deep learning techniques could be applied to prevent hazards on construction sites.

Another widely cited work published in the journal Sensors explores technologies that enhance workers’ ability to detect critical safety cues in noisy construction environments. The paper provided a comprehensive review of systems designed to augment human hearing in industrial settings.

Although much of his research centres on construction engineering, Elelu has also contributed to broader work in artificial intelligence and audio analysis, including studies on machine learning.

Together, these studies highlight his broader interest in how machines interpret complex sound patterns to enhance safety in construction engineering and ultimately save lives

Closing the safety gap in developing economies

While the solutions Elelu is developing have global relevance, their implications deeply resonates with construction challenges in developing countries.

In many African nations, including Nigeria, construction activity has expanded rapidly in recent decades. Yet safety oversight often struggles to keep pace with the scale of development.

Limited regulatory enforcement, resource constraints and the prevalence of informal labour structures mean that workers frequently operate in high-risk environments.

For Elelu, this reality underscores the importance of designing safety technologies that are both affordable and adaptable. Wearable audio-based systems, he argues, offer a practical solution because they do not require expensive infrastructure upgrades or complex installations.

They can be deployed quickly and scaled across construction sites where smaller contractors dominate the industry.

“By embedding intelligent safety systems into everyday equipment, we can make accident prevention proactive rather than reactive,” he says.

Technology for improved accountability in construction engineering 

Elelu believes emerging technologies can also help strengthen regulatory compliance. Smart safety systems can record near-miss incidents, equipment activity and worker responses to hazards. Such data can provide valuable insights into risk patterns on construction sites and create verifiable records that regulators or project managers can use to assess safety performance.
In environments where manual oversight may be inconsistent, digital audit trails could help introduce greater transparency and traceability into safety management thus strengthening compliance.

The long-term impact, Elelu suggests, could be a shift toward data-driven safety governance in infrastructure development.

Bridging research with real-world impact

A recurring theme in Elelu’s work is the need to close the gap between academic research and real-world application. For him, learning must transcend theory to provide practical solutions to real world problems particularly in developing countries.

Higher institutions play a key role in translating engineering research into deployable technologies by moving beyond laboratory experiments toward field testing, industry collaboration and the development of practical tools that can be used by engineers and contractors.

Engineering education, he argues, must also evolve to incorporate emerging technologies such as machine learning, robotics and the Internet of Things.

Such reforms would help prepare a new generation of engineers capable of developing locally and indeed globally relevant technological solutions.

Sound: the future of safety

As cities expand and infrastructure projects multiply across the world, the construction industry will remain a cornerstone of economic development if the pace of development matches innovations that protect workers building the future.

Through his research on audio-based sensing and machine learning, Elelu is contributing to a growing movement to make construction sites smarter, safer and more responsive to risk.

As infrastructure projects expand across Africa and other emerging regions, the demand for scalable, affordable safety solutions will continue to grow. In that context, Elelu’s work which blends machine intelligence with engineering design positions him as one of the promising researchers helping to define the future of construction safety where the quiet science of sound helps ensure that the people building our cities return home safely at the end of each working day.