According to the Occupational Safety and Health Administration (OSHA), over 2 million workers work on scaffolds. However, despite regulatory practices set by OSHA that ensure hours of staff training on the use of scaffolds, there are approximately 50 deaths and 4,500 injuries every year[1].
Scaffolding accidents are mainly due to human error—about 75% of the total[2]. Because of this, even the best-trained construction supervisor can find it difficult to completely monitor their personnel. Hence, the probability of occurrence of accidents is proportional to the complexity of the structure under construction.
The other 25% of scaffolding accidents are due to external conditions such as strong wind forces, earthquake loads, and other natural disasters.
Therefore, there is a need to reduce or prevent these accidents. Various research has been done and is ongoing to fuse modern technology and automation into the supervision of scaffolds during construction works. This could be described by the term “smart scaffolding.”
The main technology of concern is force/strain sensors and their variants. Research is being carried out using these sensors together with computer software and machine learning algorithms to implement automated monitoring systems.
What Are Scaffolds?
A scaffold is defined as “a temporary structure used to support people and material during construction or maintenance of buildings and other large structures.”[2] Its main purpose is to assure that work can be done safely at heights. They are a set of secondary structures that needs to be carefully constructed and designed by scaffolding professionals. However, scaffold construction and design is mostly done by a construction company’s workers who may not have enough expertise on best practices.
A professional design should take into account the following factors
- Fabrication materials
- Designing
- Assembly of parts
These three factors affect the stability of the scaffold setup and go a long way to determine if the structure will fail or not. A situation of lack of stability simply refers to the sudden deformation of the scaffold components when the applied or existing load is slightly increased. This lack of stability, together with material failure, has been considered by researchers to be the root cause of system collapse.
Scaffolds, depending on their applications, are a modular assembly of cylindrical pipes made from materials such as timber, bamboo, steel, and aluminum. Each of these materials has its own advantages, but aluminum is preferable and is lighter than the rest. The bearing capacity of each material can be calculated using standard mathematical equations[3].
Scaffold design is based on standard values of key parameters of column spacing and the height of each story. There are also different mathematical formulas that define the relationship between these parameters and the bearing capacity of the structure.
The assembling of any scaffold should be supervised by an expert. Also, there are different standards to follow in building scaffolds systems.
Some of the major components of a scaffold that contribute to its load bearing capacities are the supports, the anchors, and guard rails[4].
Types of Scaffolds
Generally, there are three types of scaffolds:
- Suspended
- Supported type
- Special/other types[5]
Each type has sub-types are used for a wide range of applications that require working at heights above the ground. Examples of such applications are window washing of skyscrapers, chimney cleaning, high-rise building erection, ship-building, bridge construction, aircraft manufacturing, etc.
Categories of Scaffolds
According to BS EN 12811, scaffolds are classified according to their load-bearing capacity and area of applications. These classes are rated from class 1-6 with class 1 being the lowest class with support for an average load of 750N/m2 and supporting only light work and equipment. Class 6 can support distributed forces of about 6kN, allowable storage weight of 10kN, and is the highest rated class.
Another piece of research[6] classified scaffolds into access scaffolds and support scaffolds. The former classes are attached to a permanent structure for increased stability, while the latter classes have adjustable extensions with U-head screw jacks.
The Parameters of Interest
These are the output or responses of the scaffold system that need to be monitored by sensors in order to build an intelligent monitoring system. Special sensors can be integrated into the monitoring process to create a kind of feedback.
Furthermore, the parameters are selected based on their influence on the health of the scaffold structures—the degree to which a collapse of the scaffold depends on those parameters. A general set of parameters considered widely by researchers include slab settlement, overall vertical and horizontal displacement, axial forces acting on the columns, column tilting angle, and wall anchors/ties/braces removal or failure.
The essence of creating smart scaffold system is to automate the inspection process. Greater digitization of the criteria used during a normal physical assessment by a site supervisor can help to create a wide list of parameters to be monitored.
Sensor Technologies Used For Scaffold Monitoring
A sensor is simply a device that can detect a physical parameter and transduce it to produce an electrical output signal. You could imagine the sensor to be a black box; the physical parameter – force, weight, etc.—is applied to it and the output you get is an electrical signal.
There is a plethora of sensor types to choose from for scaffold monitoring, however, it can be reduced to a particular type of sensor called a strain gauge load sensor[7]. The strain gauge is able to produce an electrical signal once any of its dimensional parameters–length and cross-sectional area—is changed physically. Therefore, it can be used to provide a response to each of the parameters of interest mentioned earlier.
According to the parameters, there would be a need for the following sensors:
- Displacement sensors for monitoring slab settlements and displacements
- Force/load sensors for detecting axial forces acting on the columns
- The angle sensors, which operates like the axial force sensor but mounted to record angular displacements
- Fracture wire or tension link load cells to detect fracture/removal of anchors
Furthermore, research has shown that a large amount of the structural response of a scaffold is a function of the strain distribution[8], hence the more reason for the widespread use of varieties of strain sensors for scaffold monitoring.
Mounting the Strain Sensors
The mounting of the sensors requires the determination of the exact locations where these various sensor types can be placed to accurately monitor the desired physical parameters of the scaffold system.
The monitoring points for such sensors can be chosen so the movement of the slab causes a physical change of the dimensions of the underlying strain gauge; it could be mounted to lie between the horizontal/beam and vertical/column members of the scaffold system.
The angular displacement can be used installed at the topmost story, as it is said to be the weakest part of the structure—it will detect the response to any tilting movement of the column.
The mounted sensors need to communicate their data remotely, so it makes sense that wireless communication technology is what makes this possible. Hence, this eliminates the need for unnecessary wirings that could add to the safety concerns of the scaffold system design.
Analyzing the Sensor Data and Model Generation
The sensors actively collect information about the structure and keep monitoring the system, and this creates a huge field of data that can be analyzed to improve the monitoring process. It enables the development of a feedback mechanism in the whole monitoring process, as well as failure prediction and generation of set points or thresholds.
With the current trends of technology in data science, researchers have applied some machine learning (ML) tools to analyze, study, and classify the data stream coming off the sensors. This makes it possible to perform a structural analysis of a scaffold system.
The analyzed data can be displayed graphically in real-time, and it can be programmed to trigger alarms at the construction site or workplace whenever a failure is about to occur or before it occurs. The alarms can be placed at strategic locations and triggered remotely via wireless communication links. Such alarms can be speakers or warning lights.
Current research has advanced this concept by generating models of scaffold systems using software tools such as MATLAB, GMNAF, COMSOL Multiphysics, and NIDA. With this software, a finite-element 3D model of a scaffold is developed. These models are used to study the load-bearing capacity of the systems. The parametric data obtained from the sensors and analyzed by the ML algorithms is used to update and fine-tune the computer models of the scaffold[8].
Limitations to Implementing a Smart Scaffolding System
There is still a lot of research to be performed in this area to reduce the time of deployment of monitors, develop precise scaffold models for finite-element analysis software, and improve the calibration of sensor units.
A major challenge is the power supply for the sensor units. More work needs to be done on how to efficiently consume and supply power and still assure that the scaffold structure is being actively monitored. Hopefully, advances in wireless electricity may help solve this problem
Contributing Author: Joe Flanagan, Project Engineer, Tacuna Systems
Joe Flanagan has over 12 years in electronic engineering and design and is the current Project Engineer at Tacuna Systems. They provide hardware and service solutions for the for the strain measurement industry across a range of applications.
References
| [1] | Park, Jeewoong & Cho, Chunhee & Kim, Kyungki & Sakhakarmi, Sayan., “Scaffolding Modelling for Real-Time Monitoring using a Strain Sensing Approach,” in 35th International Symposium on Automation and Robotics in Construction (ISARC 2018), Berlin, Germany, 2018. |
| [2] | Christopher Ullrich, “Implementation of sensor technology in scaffolding- An application of technological brokering and smart product design,” Centre for Entrepreneurship, University of Oslo, 2016. |
| [3] | Su, Jian & Li, Jieming & Tan, Lingsheng & Huang, Xiaodan., “Development of the IoT-based Monitoring System for Scaffold Shoring System of Concrete Formwork,” in MATEC Web of Conferences, 2018. |
| [4] | Pieńko, Michał & Robak, Aleksander & Błazik-Borowa, Ewa & Szer, J., “Safety Conditions Analysis of Scaffolding on Construction Sites,” International Journal of Civil, Environmental, Structural, Construction and Architectural Engineering., pp. 72-77, 2018. |
| [5] | Desai, Jaydeep & Pitroda, Dr. Jayeshkumar & Bhavsar, Jaydev., “SCAFFOLDING: SAFETY AND ECONOMICAL ASPECT FOR SCAFFOLDINGS IN CONSTRUCTION INDUSTRY.,” in National Conference on: “Trends and Challenges of Civil Engineering in Today’s Transforming World”, 2014. |
| [6] | T. Chandrangsu, K.J.R. Rasmussen, “Review of past research on scaffold systems,” University of Sydney, Department of Civil Engineering, 2009 |
| [7] | The Essential Guide to Load Cells, Tacuna Systems |
| [8] | Park, Jeewoong & Cho, Chunhee & Cho, Yong & Kim, Kyungki., “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management, 2018. |
