Spatio-Temporal Safety Analysis of Construction Site Operations using GPS Data
Nipesh PRADHANANGA1 and Dr. Jochen TEIZER2
1 School of Civil and Environmental Engineering, Georgia Institute of Technology,
790 Atlantic Dr. N.W., Atlanta, GA 30332; email: [email protected]
2 School of Civil and Environmental Engineering, Georgia Institute of Technology,
790 Atlantic Dr. N.W., Atlanta, GA 30332; Corresponding author email: [email protected]
ABSTRACT
Recording the continuous location of equipment and workers with Global Positioning System (GPS) units can contribute in the analysis of how safely a construction site operates. Automated data gathering and analysis for safety become even more valuable when reliable methods exist that record and report events that otherwise would not be recorded because they are labor intensive in observation or prone to human error in judgment. This paper presents a new safety approach that features automated analysis of continuously collected proximity data between construction workers, heavy construction equipment, and hazardous construction spaces in outdoor construction environment.
We recorded field data using small GPS units that were mounted on construction helmets or attached to construction equipment. This paper first evaluates the performance of the technology that was used to gather continuous location data to construction resources (workers and equipment). It then explains how the generated spatio-temporal information can be communicated to decision makers so it improves the safety performance of workers near equipment or other hazards.
The results that are presented include a case study to outdoor construction environment. It demonstrates how potential users can measure the safety performance of construction resources (workers, equipment) automatically and use the generated information as new knowledge in safety training and education.
Keywords: Accidents, construction equipment, GPS, proximity, safety, workers.
INTRODUCTION
Outdoor construction operations take place in dynamic environment where construction resources (equipment, materials and workers) continuously interact with each other. This vigorous interaction involves interference of work zone of one resource to another, thus, creating hazards. The topology of the construction site, like working at heights and near trenches, also exposes the resources to potential hazards. It has been reported that 25% of all construction fatalities are related to collision with equipment (Teizer et al. 2010). Fatalities and injuries in a work site leads to significant loss of time, money and momentum in a project. Hence, ongoing construction operations should be properly observed and the potential hazards identified.
of measurement (Cheng et al. 2011). By implementing technology, events of critical proximity can be automatically detected. Cases of near misses, which would have not been recorded or quantified otherwise, can be identified and recorded. Real time tracking of construction resources has been tried and proven with different emerging technologies. Radio Frequency Identification (RFID) (Jaselskis and Misalami 2003, Ergen and Akinci, 2007), Ultra Wideband (UWB) (Bohn and Teizer, 2010, Cheng et al. 2011) and fusion of multiple sensors (Behzadan et al. 2008, Razavi and Haas 2010) are some examples of successfully implemented technologies. Pro-active real time proximity and alert technology based on radio frequency signal has been used solely for proximity detection and alert on construction site (Teizer et al. 2010).
However, Global Positioning System (GPS) based technique is the only known tracking technique that does not require pre-installed infrastructure (Behzadan et al. 2008). This paper presents the implementation of GPS units for tracking construction resources and analyzing safety conditions in a site. The results can provide a guideline for hazard identification, safety training and education.
BACKGROUND
Hinze (2005) stated that measures like injury rates that are being used to evaluate safety performance are lagging indicators of safety and do not provide insights on the existing safety conditions. The problems that construction industry suffers today are inability to recognize existing risk, after-the-fact gathering of data and unavailability of real time data of an incidence (Fosbroke 2004, Teizer 2010). Technology capable of tracking such real time spatio-temporal data required for proactive measure of safety are cost, maintenance, size, scalability, reliability, data update rate and social impact of the technology (Cheng et al. 2011).
GPS is a satellite based navigation system constituting of 24 satellites orbiting around the earth controlled by United States Department of Defense. A clear line-of-sight to the sky and adequate satellite signal is required to use this system. The GPS system consists of space segment, control segment and user segment. Space segment is composed of the satellites orbiting the earth while control segments are located at ground monitoring the position of the satellites. The user segment is the GPS units used by the end user to locate its own position.
Oloufa et al. (2002) developed a collision detection system for construction equipments using GPS technology. In hazardous environment where human presence is not encouraged, this system could wirelessly connect the GPS data to the central system and collision scenario among equipment would be identified. A study focusing on analysis of construction operations using GPS data was done by Hildreth et al. (2005). The purpose of the study was to reduce the amount of data stored from continuous recording by identifying key times required for construction operation analysis from rest of the dataset. Real-Time Kinematic (RTK) GPS was used for controlling vertical accuracy of earth surface profiling equipment where the highest z accuracy is needed (Peyret et al. 2000). This kind of GPS comprises of a precisely positioned station and a rover whose position is calculated based on the station.
OBJECTIVES AND SCOPE
This research focuses on implementing commercially available GPS units to track critical construction resources in a real construction site. There are two major objectives of this research, i) to evaluate the performance of the technology in construction environment and ii) to illustrate how continuous tracking data obtained from this technology can be utilized to reflect the safety conditions of the site. This research implements low cost GPS data loggers without the need of any pre-installed infrastructure on the site. More accurate GPS technologies are available in the market. This research does not intend to compare the available alternatives but demonstrates the use of data gathered from such system. Limited by technology, this research only deals with outdoor construction environment.
RESEARCH METHODOLOGY
The outline of the research method followed for this study is shown in Figure 1. Workers Equipment Data •ID •Latitude •Longitude •Altitude •Time Data Analysis •Zone •Proximity
Site Layout map
•Drawings •Satellite Imagery
•Internal Traffic Control Plan
Reporting User specified zones •Work zone •Hazard zone •Material zone •Travel zone •Loading zone •Dumping zone •……….. Instrument Error Analysis
Deploy GPS tags on site
Validation Data Processing
•Lat-lon to UTM •Filtering
GPS Data Site geography
Analysis tool
Figure 1. Research Methodology
Before deploying the technology, error analysis was done to evaluate its performance in different environmental conditions. Instrumental error can play a significant role in experimental design and analysis of data. The following section gives the detail of error analysis performed on the GPS units. Data was collected by mounting the GPS units on hardhats of the workers and installing them into equipment. The obtained continuous location data was processed and fed into analysis tool. The analysis tool incorporated user created zones from site layout with the
sections.
INSTRUMENT ERROR ANALYSIS
Commercially available data loggers were used for the study. The dimensions of the units were 64 x 40 x 17 mm and weighed 55g with battery. These units could be mounted on the hard hats of the workers without interference to their regular activities. Data was collected in a continuous manner into a single data file. The attributes of the data file included index of the point, latitude, longitude, elevation and timestamp for each reading. It also had a push-to-log button for marking a push log point. These push log points could be identified by their index in the recorded dataset. The unit was equipped with motion sensor to avoid recording redundant data when the unit was not in motion.
The accuracy of the units was tested in two scales, absolute global scale and relative scale. Absolute scale is the measure of correctness of the global coordinates logged by the units. Relative scale is the measure of deviations exhibited by the units when exposed to same environmental conditions at same time. Since multiple units are deployed in the same construction site at the same time, relative accuracy of the units can give a good vision of the level of accuracy that can be expected while studying spatio-temporal interaction of the resources.
The measure of accuracy of the units in absolute scale was done by comparing the coordinate logged by the unit on a point of known coordinate point. Benchmark point DG2790 with coordinate N 33° 46.594 W 084°23.898 in NAD 83 coordinate system was selected as the known coordinate (see Figure 2(a)). Ten GPS units were placed on a wooden board in two rows with five units in each row as shown in Figure 2(b). The offset distances from the centre of the board to centre of each of the units were measured. This was done so that readings can be taken on all the ten units at the same time and corrections can be made to obtain the coordinate of the center. The wooden board was oriented to the north for proper correction of these offsets. A guiding rectangle was marked around the benchmark point in such a way that the center of the board coincides with the center of the benchmark point (Figure 2(b)).
Since the GPS units had motion detectors, leaving the units at a point over a period of time produced identical data points. Hence, the units were placed on the guiding rectangle around the benchmark point and push log points were created on each unit by pressing the push-to-log button to identify the point. Thereafter, the units were carried more than 10m away from the benchmark point to ensure that the next reading was independent of the previous one. This process was repeated 36 times and the following observations were made. The benchmark point had a wide angle of open sky and the experiment was conducted on a sunny day.
Figure 2. (a) Benchmark point DG 2790 (b) Apparatus setup for test
Table 1 shows the error analysis of distance between the coordinate recorded by GPS units with each other as well as with the benchmark point. The values above the diagonal are the mean of the distances between the GPS units while the values below the diagonal are the standard deviations of the distances between corresponding units. The mean and standard deviation of the distance between the GPS units and benchmark point are listed at the right hand side of the table.
Table 1. Error analysis of GPS units placed on Benchmark point (N = 36)
Tag ID 1 2 3 4 5 6 7 8 9 10 Benchmark
Mean distance between tags (m) Distance (m) Std. Dev.(m)
1 0.64 0.55 0.52 0.75 1.06 0.85 0.84 0.70 0.59 1.06 0.43 2 0.44 0.36 0.55 0.68 0.83 0.64 0.64 0.36 0.67 1.02 0.42 3 0.47 0.37 0.58 0.63 0.96 0.71 0.66 0.43 0.60 1.00 0.41 4 0.45 0.41 0.41 0.73 1.12 0.90 0.87 0.73 0.54 0.96 0.40 5 0.58 0.50 0.53 0.57 0.87 0.77 0.78 0.72 0.82 1.08 0.37 6 0.66 0.59 0.58 0.69 0.50 0.55 0.68 0.89 1.15 1.36 0.41 7 0.44 0.42 0.48 0.40 0.51 0.45 0.50 0.68 0.93 1.24 0.44 8 0.45 0.52 0.58 0.55 0.48 0.54 0.32 0.68 0.94 1.31 0.33 9 0.51 0.49 0.42 0.52 0.47 0.71 0.57 0.67 0.69 1.04 0.51 10 0.48 0.39 0.44 0.44 0.54 0.66 0.50 0.50 0.47 0.87 0.39
Standard deviation of the distance between tags (m) Mean distance(m) = 1.10
The mean distance between the actual coordinate and the coordinate read by the tags was 1.1m. The maximum distance was observed in Tag 6 (1.36m) and minimum distance was observed in Tag 10 (0.87m). The standard deviation was high indicating high deviation of individual values from the mean. The box plot in Figure 3 shows the distribution of values in each tag. However, in real construction site, tags installed inside equipment and hardhats will not get as suitable environment as a tag is completely exposed to the sky without obstruction. Roof or moving parts in an equipment or orientation of head of the worker continuously changes the exposure of the tag to the sky. Moreover, obstacles like buildings or trees cannot be avoided. Hence, same accuracy cannot be expected in a construction site. It was also observed that the maximum relative deviation was found between Tag 6 and Tag 10, which
Figure 3. Box plot of the distance between the GPS units and the benchmark
An arbitrary point in a construction site was chosen to test the performance of the units in actual data logging environment (see Figure 4). The point was exposed to obstacles like building around its vicinity, canopies and construction equipment with moving parts. The same procedure was followed while recording points.
Figure 4. Apparatus setup at an arbitrary point in construction environment
The error table (see Table 2) showed a higher deviation compared to the above case. The mean of the deviation was found to be 2.15m which is significantly higher than the previous case. The maximum deviation was seen to be 4.36m between Tag 2 and Tag 3 and minimum deviation was 0.68m between Tag 9 and Tag 10.
Table 2. Error analysis of GPS units at an arbitrary point (N = 34) Tag
ID
1 2 3 4 5 6 7 8 9 10 Mean distance between tags (m)
1 2.03 2.81 2.25 1.59 1.44 1.92 1.33 1.55 1.71 2 2.74 4.36 4.03 3.21 2.67 3.59 2.62 3.26 3.41 3 0.68 2.15 1.79 2.62 3.71 3.49 3.41 3.20 3.35 4 1.55 4.14 1.44 1.57 2.49 1.90 2.15 1.78 1.86 5 1.64 4.27 1.93 1.16 1.45 1.83 1.09 1.32 1.64 6 1.24 3.96 1.33 1.11 0.77 1.88 0.95 1.55 1.72 7 2.79 5.58 2.55 1.35 1.14 1.45 1.59 0.73 0.79 8 1.59 4.35 1.73 1.44 0.67 0.55 1.29 1.16 1.50 9 2.25 5.00 2.12 0.98 0.87 0.95 0.62 0.88 0.68 10 2.39 5.19 2.27 1.11 1.12 1.05 0.50 0.92 0.52 Standard deviation of the distance between tags (m)
Hence, the GPS tags were found to perform better under clear view of sky while the performance degraded due to obstacles. Standard deviations of the deviations were high in all cases indicating that the readings were not consistent and can vary significantly. It should be understood that error level can change from tag to tag and from time to time with changing exposure condition. The above tests give a general idea about what extent of accuracy can be obtained from the tags.
DATA COLLECTION AND PROCESSING
A case study was done utilizing ten GPS units. Each unit was set to high accuracy mode with a data logging frequency of 1 Hz. The GPS units were mounted on hard hats of the workers so that it does not interfere in their regular activities (Figure 5(a)). In case of equipment, the units were installed inside the equipment at the spot with maximum exposure to the sky (Figure 5(b)). Some equipment were tagged with two units for redundancy and relative error analysis (Figure 5(c)). Data was collected for entire work shift which ranged from four to more than twelve hours of continuous data per day.
Figure 5. (a) GPS unit mounted on the hard hat of a worker, (b) GPS unit mounted on a loader, (c) two GPS units mounted inside an excavator
The case study involved construction of an indoor football practice facility. The operations being tracked were earth moving operation involving excavation and
like sheep-foot roller, the equipment operator was tagged instead of the equipment. Data was downloaded for each tag in csv format. The data was changed from latitude-longitude to UTM for ease in computing and analysis. Before importing the data into the tool, it was also filtered using Robust Kalman Filter (Durovic and Kovacevic 1999). Robust Kalman Filter also rejects outliers in addition to smoothing the signal.
DATA ANALYSIS AND PRELIMINARY RESULTS
An analysis tool was developed in Visual C# 2010. Raw data can be imported into the tool using a user interface. Data is filtered and stored into a Microsoft Access (.mdb) database. Tags and zones can be created in the tool for storage and analysis. Recorded data can be stored corresponding to the tag ID and coordinates of the polygon forming the zones can be stored under different names (for example work zone, hazard zone and material zone).
A sample analysis is shown where a worker tagged with Tag 2 is working at an area named “WorkZone” on the roof. A zone named “HazardZone” was also created near the edge of the roof for analyzing the movement of the worker near the edge of roof. The zones were created by entering the coordinates of the polygon forming the zones from site layout map into the tool. Figure 6 shows the user interface for analyzing the trajectory of a tag in a zone. The entry and exit time to the zone, number of times the tag entered the zone and time spent inside the zone are the result of this analysis. The results in Figure 6 indicate that Tag 2 entered the “HazardZone” seven times on that day and the time spent in that zone was 37 min 14 sec.
Similar analysis done for work zone showed that the worker entered the working zone 30 times on that day and total time spent in “WorkZone” was 7 hr 35 min 5 sec. The total data available on that tag for that day was 10 hr 34 min 42 sec. Hence, working time and time spent in hazard zone for the worker can be calculated from this analysis. After other activities are also identified and their respective zones specified, time spent on each activity can be analyzed and total distribution of time in different activities can be determined.
Proximity is another type of analysis that can be done with spatio-temporal data. Proximity of workers to equipment or equipment to equipment can be analyzed using a proximity threshold distance. The instances where the proximity threshold is breached can be determined. Previous analysis dealt with analyzing a worker, this example will deal with proximity of equipment with equipment. Figure 7 shows the user interface for analyzing proximity between two tags. Tag 3 (selected as first tag) represents an excavator and Tag 6 (selected as second tag) represents a skid steer. The analysis is done to determine when these two tags come ten meters (see Figure 7) or closer to each other. The result shows that the number of times these two tags come closer than ten meters is thirteen. The result also shows when do they enter the proximity zone, when do they leave, when do they come closest to each other and what is the closest distance in each instance. This analysis gives a vision of how close are the resources operating to each other and when do they come very close to each other.
Figure 7. User interface for proximity analysis
The preliminary results were verified with the help of site notes, site pictures and manual calculations. In case of roofers, video was also taken for comparison with the results from analysis.
CONCLUSION
This paper demonstrates that GPS technology can be used to track critical resources in a construction site. Data obtained from continuous tracking of resources can be used for analyzing safety conditions on a site. By knowing where and when unsafe activities are taking place, proactive measures can be taken to prevent hazards in a site. Current approach involves analysis of data after the activity has happened on the site but this data can be used as a leading indicator to identify the zones where most unsafe activities take place and use the knowledge in future plans for safety. By recognizing the workers exposed to hazardous conditions, necessity of training or education for that worker can be determined. Location based safety analysis can also detect which area of the site is prone to hazards and hence, measures can be taken for improving safety in that zone by proper site layout planning and internal traffic control planning.
Behzadan, A. H., Aziz, Z., Anumba, C. J. and Kamat, V. R. (2008) “Ubiquitous location tracking for context-specific information delivery on construction sites”, Automation in Construction, 17, 737-748.
Bohn, J. S. and Teizer, J. (2010). “Benefits and barriers of construction project monitoring using hi-resolution automated cameras”, ASCE Journal of
Construction Engineering and Management 136 (6) 632–640.
Cheng, T., Venugopal, M., Teizer, J. and Vela, P. A. (2011). “Performance evaluation of ultra wideband technology for construction resource location tracking in harsh environments.” Automation in Construction 20, 1173-1184.
Durovic, Z., Kovacevic, B. (1999) “Robust Estimation with Unknown Noise Statistics”, IEEE Transactions on Automatic Control 44(6) 1292-1296.
Ergen, E. and Akinci, B. (2007). ” An Overview of Approaches for Utilizing RFID in Construction Industry”, RFID Eurasia 1st Annual
Fosbroke D.E. (2004), NIOSH reports! Studies on heavy equipment blind spots and internal traffic control, 2004 Roadway Work Zone Safety & Health
Conference, Baltimore, MD, 2004, 2004, <https://www.workzonesafety.org/files/documents/news_events/wz_
conference_2004/heavy_equipment.pdf> (Nov. 13, 2011).
Hildreth J., Vorster M., and Martinez J., (2005) “Reduction of Short-Interval GPS Data for Construction Operations Analysis”, J. Constr. Eng. Manage. 131, 920.
Hinze J. (2005) “Construction Safety in the 21st Century”, Keynote lecture 2, Third International Conference on Construction in the 21st Century (CITC-III),
Advancing Engineering, Management and Technology,15-17, September 2005, Athens.
Jaselskis, E. J., El-Misalami, T. (2003). “Implementing Radio Frequency Identification in the Construction Process”, Journal of Construction
Engineering and Management, 680-688.
Oloufa, A. A., Ikeda, M. and Oda, H., (2002) “GPS Based Wireless Collision Detection of Construction Equipment”, Proceedings of 19th ISARC, National
Institute of Standards and Technology, Gaithersburg, Maryland. September
23-25, 461-466.
Peyret F., Bétaille D. and Hintzy G., (2000) “High-precision application of GPS in the field of real-time equipment positioning”, Automation in Construction, 9, 3, 299-314.
Razavi, S. N. and Haas, C.T. (2010) “Multisensor data fusion for on-site materials tracking in construction”, Automation in Construction, 19, 1037-1046.
Teizer, J., Allread, B. S. and Mantripragada, U. (2010). “Automating the blind spot measurement of construction equipment.” Automation in Construction 19, 491–501.
Teizer, J., Allread, B. S., Fullerton and C. E., Hinze, J.W. (2010) “Autonomous pro-active real-time construction worker and equipment operator proximity safety alert system”, Automation in Construction, 19 (5), 630–640.
