Peter Simon was recently featured in the Fall 2019 AGC Cornerstone – a quarterly publication released by the Associated General Contractors of New York State. The article transcribed below:
Technological & Construction & Safety
The construction industry has had a reputation for being resistant to change and slow to integrate new technologies that are often more quickly adopted by other industries, but that is rapidly changing. In our daily lives people are increasingly using artificial intelligence, collision technology in automobiles, ridesharing apps, fitness & biometric monitoring devices (such as fit bit & smart watches), new cell phone applications and smart home devices. Similarly, the construction industry is gradually using tools such as BIM (Building Information Modeling), drones, equipment sensors, smart phone applications and 3D printing to more efficiently and safely perform construction work.
One of the strongest drivers behind the use of new and advanced technology is improving worker safety and health. Billions of dollars in costs are incurred annually as a result of accidents and reducing these injuries or deaths is not only financially responsible, but also involves a moral imperative to reduce avoidable injuries, disabilities and fatalities. Many of these technological advancements are so commonplace today it can be hard to imagine or remember the workplace without them (hard hats, equipment back-up alarms, fall protection harnesses, safety shoes etc.). Even with the numerous advancements in workplace safety there are still (2017) more than 14 workplace deaths everyday according to OSHA; down from the 1970 average of 38 worker deaths per day.
The area that might hold the greatest potential to improve workers’ safety & health is data collection and analysis. Some industry experts have predicted that more data will be collected on construction sites in the next 10 years than has been collected over the past 100 years. Combining data collection with an increase in connectivity and the internet of things (IOT) brings new opportunities to analyze safety risks and take new measures to eliminate hazards (some of which might have been previously unknown). As technology hardware & implementation costs continue to decrease, the construction industry will abandon its reliance on lagging post-incident indicators and increase the use of predictive analytics and leading indicators to prevent incidents.
Falls, Ladders & Smart Technology
OSHA statistics indicate that almost 40% of construction fatalities are a result of falls. The Center for Disease Control estimates that 81% of construction worker fall injuries treated in U.S. emergency departments involve a ladder and a significant proportion of ladder injuries occur at a height of 6’ or less.
The estimated annual cost of ladder injuries in the U.S. is approximately $24 billion, including work loss, medical, legal, liability, and pain and suffering expenses. Despite the large amount of resources and effort directed at preventing falls, falls and ladder falls still plague the construction industry.
A breakthrough product that could significantly reduce the number of falls and specifically falls from ladders is the connected “smart” ladder. A connected “smart” ladder places wireless sensor on ladders to detect and issue instant alerts of unsafe ladder use. Currently a “smart” ladder is under development through a combined effort of experts in product development, data analytics and construction safety. Since commencing the effort to create a “smart” ladder, the technology has evolved to the point where the ladder can detect nearly 100 separate unsafe scenarios, issue alerts and capture data to enable the study of accidents, near misses and trends.
Fall Reduction Through User Notifications
One of the most obvious ways a “smart” ladder could reduce accidents is through notification to the user that the current ladder use is approaching an event. A common example people experience every day is driving an automobile with active blind spot monitoring. Blind spot monitoring systems notify the driver when another vehicle comes too close to the driver’s vehicle. An audible noise or flashing light in the driver’s peripheral vision notify the driver of the impending collision and allow the driver to return to the previous lane instead of continuing toward a collision.
Similarly, ladder users are often not even aware of the misuse and impending accident. Not unlike blind spot notification, a “smart” ladder user that is notified that the activity is exceeding the safe tolerances is able to use this information, return to safe use and desist from use that might result in an accident. Common examples include exceeding the ladder’s weight limit, leaning too far to one side on the ladder, standing on the top steps of the ladder, climbing on the wrong side of the ladder, etc. The notifications give the user direct feedback and information helpful to avoid preventable accidents.
Predictive Analytics Instead of Lagging Indicators
For most, predictive analytics are preferred for planning and decision making, relative to lagging indicators. Unfortunately, much of the current safety methodology revolves around lagging indicators such as accident analysis, insurance accident statistics, OSHA recordables, employee lost days etc. These lagging statistics are important but involve analyzing traumatic events that safety practice seeks to prevent.
A connected “smart” ladder that is actively collecting usage data and analyzing trends can proactively identify activities that have an increased probability of resulting in an accident. An example is a user repeatedly leaning the ladder to the right during a workday or a work week. Even though the ladder has not yet tipped over causing the ladder and worker to fall, the data showing a trend of over leaning can be utilized by the worker and employer prior to an accident. Data showing repetitive over leaning can result in a re-evaluation of the task so over leaning on the ladder is eliminated through task or equipment modification. This predictive scenario is preferable to investigation of the circumstances after the accident.
Identification of Potentially Unidentified Hazards
It is not uncommon for an equipment hazard to go unrecognized because the user is unaware the equipment is being used incorrectly. This creates a potentially unfortunate situation whereby a user is injured for simply not knowing that there is an issue with their use of equipment. Additionally, safety professionals and supervisors cannot be everywhere all the time and see everything. An automated system that collects data, identifies potentially high-risk use, notifies workers and responsible supervisors, reduces the probability of having an accident due to unidentified hazards. Awareness is a tremendous advantage as it gives the user and others an ability to asses if there is a problem and how any issues might be addressed in advance of negative consequences such as an incident or accident.
Targeted Training for Adult Occupational Learners
Ladder use data will also provide a powerful new training tool that could assist workers by targeting specific areas of need based upon real work data. Instead of one-size-fits-all-training that may or may not address safety issues arising from actual daily activities, data-based training can target actual trends in the field. If a group or user is consistently overloading or leaning ladders this could be a main training topic with the group or individual.
The customized safety training tool is even more powerful given that occupational training targets adult learners who tend to have better retention if the training is hands-on or applicable to real world work situations. This will undoubtedly make the training more easily digestible as the training will target actual activities being performed by a trainee.
Change Human Behavior & Environmental Factors
Significant attention has been paid to the involvement of human behavior and human error as it relates to accident contribution (some studies have put human error as a contributing factor in more than 80% of accidents). Other accident prevention theories place more emphasis on the workplace environment and advocate that workplace injuries are a result of environmental factors. In either case, a necessary component for change in human behavior and/or environmental factors is access to data in order to assess what is happening and what the potential safety issues might be. Because humans tend to be resistant to change, data is often necessary for behavior modification. Without leading indicator data, of a ladder for example, people are less likely to change environmental circumstances (equipment) or workplace behaviors before an incident occurs.
Our society and construction industry are changing in the current age of technological innovation and data. There are always concerns about data collection, but on balance, safety-oriented data can also create a positive impact. Connected “smart” ladders are an example of technology that is within our reach and has the potential to reduce accidents and fatalities, and generally improve the wellbeing of workers and their families.