Tracking and locating components in a precast storage yard utilizing radio frequency identification technology and GPS

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1 Automation in Construction 16 (2007) Tracking and locating components in a precast storage yard utilizing radio frequency identification technology and GPS Esin Ergen a, Burcu Akinci b,, Rafael Sacks c a Department of Civil Engineering, Istanbul Technical University, Istanbul, 34469, Turkey b Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA, 15213, USA c Faculty of Civil and Environmental Engineering, Technion - Israel Institute of Technology, Haifa, Israel Accepted 25 July 2006 Abstract Problems in existing manual methods of identifying, tracking and locating highly customized prefabricated components result in late deliveries, double-handling and misplacement of components, and incorrect installations that lead to schedule delays and increased labor costs. To eliminate these deficiencies, an automated system using radio frequency identification technology combined with GPS technology, requiring minimal worker input, is proposed. The requirements and approaches needed to utilize the system for locating precast concrete components with minimal worker input in the storage yard of a manufacturing plant were developed. Based on the requirements identified and approaches formalized, a prototype system was developed, assembled and tested in the field at a precast storage yard. The prototype system succeeded in automatically identifying pieces that were relocated, demonstrating feasibility of the approach Elsevier B.V. All rights reserved. Keywords: Automated tracking; Locating; Radio frequency identification; Precast; Component 1. Introduction Corresponding author. Tel.: ; fax: address: bakinci@cmu.edu (B. Akinci). In construction supply chains, problems in poorly identifying, tracking and locating highly customized prefabricated components result in late deliveries, double-handling and misplacement of components, and incorrect installations that lead to schedule delays and increased labor costs [1]. Tracking and locating prefabricated components individually at any point across a construction supply chain is a challenging task. Manufacturing plants of prefabricated components have a dynamic nature where many unique components are frequently relocated and shipped daily. Similarly, prefabricated components are frequently received, stored, relocated and installed at construction sites. The customized nature of prefabricated components amplifies the challenge of identifying and locating them along their supply-chain due to the fact that most of them are unique and therefore need to be tracked individually. Under these challenging conditions, both prefabrication companies and contractors invest significant resources to track and locate components using manual approaches [1,2]. The objective of the study described in this research was to identify the requirements for and develop an appropriate automated approach to tracking and locating precast concrete (henceforth termed simply precast ) components in a manufacturer's storage yard using advanced tracking technologies. Since just-intime delivery is required for precast pieces at construction sites, it is critical for a precast manufacturer to know exactly where any individual piece is located in a storage yard, so that any component that is requested from a construction site can be located and supplied quickly [2 4]. Precast storage yards typically cover several thousands of square yards; in the case of large-scale precast companies, yards commonly contain up to 4500 pieces at any given point in time. Accurately and reliably tracking such a large number of components in a wide area is a challenging task. Existing approaches for tracking components utilize manual tracking by paper-based documents or barcodes. Information collected using such labor-intensive methods is not reliable or complete since these data collection methods rely on workers' motivation. Unreliability in material tracking might result in /$ - see front matter 2006 Elsevier B.V. All rights reserved. doi: /j.autcon

2 E. Ergen et al. / Automation in Construction 16 (2007) delays in shipping due to pieces that cannot be located on time or misplaced pieces that have to be manufactured again [2]. Current practice highlights the need for a tracking system that provides up-to-date location information for precast pieces in the storage yard with minimum human input. Radio frequency identification (RFID) integrated with the global positioning system (GPS) provides an opportunity to uniquely identify precast components and to track and locate them using minimal or no worker input. Although RFID technology is being used to track assets in other industries, very little research has been conducted in the architecture, engineering and construction (AEC) industry or in facility management (FM) on the utilization of RFID technology for tracking components [1,3,5 8]. Among these studies, only Song et al. [8] provided a reasoning mechanism based on a proximity method for locating materials in a construction site using RFID and GPS. In this approach, construction site is scanned daily in detail to identify the location of materials on a given site. Whereas, in the research described in this paper, the goal has been to track components as they are moved to different locations within a storage yard. To identify the requirements and to develop reasoning mechanisms, an initial field test was conducted to test the performance of RFID on precast components and GPS in a precast yard. Based on the results of such an initial test, a detailed list of requirements and corresponding approaches was formalized. A prototype system, which utilizes the approaches, was then developed, assembled and tested in the same precast storage yard. 2. Background Baldwin et al. [2] identified the need for improvement in locating precast components in a storage yard and conducted a field test at a precast plant to determine the feasibility of using barcodes at the storage yard and during shipping. In spite of the labor-intensive data collection activities associated with the utilization of barcodes, during which pieces are manually scanned at each and every relocation in the storage yard, typical savings were estimated to be very high: 70% time saving during checking out precast beams while loading to a trailer, 30% time saving in locating a beam in a storage yard, and 85% time saving in clerical time for entering beam data to the company's computer system. However, our observations at a precast manufacturing plant that utilizes barcodes for tracking components during storage showed that workers try to avoid laborintensive barcode scanning activities and as a result, the data is not complete. If the data is not complete, then it is not considered reliable and therefore not considered worth maintaining. This creates a detrimental spiral of decreasing trust on the part of the workers to the data provided in such databases. In practice, it has been observed that the expected savings of using barcodes have not been fully realized. Besides barcodes, various other automated identification technologies, such as two dimensional barcodes, RFID, optical character recognition and touch probes exist [5,9 12]. Most of these technologies require line-of-sight to capture identification information, and thus have limited identification range requiring that a labor-intensive scanning activity be performed on each object. In addition, some of these technologies cannot survive in harsh environmental conditions (e.g., rain, dust, impact), typically encountered in precast storage yards. RFID technology is durable, does not theoretically require line-ofsight and some types of RFID technology have relatively long reading/detection ranges up to m. RFID has two main components: a reader and a tag. The tag, which consists of an electronic chip coupled with an antenna, is attached to an object and stores data about the object. The reader, combined with an external antenna, reads/writes data from/to a tag via radio frequency and transfers data to a host computer. Some tags have LED notifying the user with a blinking light during communication. Reading and writing ranges depend on the operation frequency (low, high, ultra high and microwave), whether the tag needs a battery to operate (active) or not (passive) and whether some materials, which can interfere with radio signals, exist in the environment (e.g., concrete, steel). Finally, unlike barcodes, RFID tags can withstand harsh conditions [1,5]. Tags operating at ultra high frequency (UHF) typically have longer reading ranges than tags operating at other frequencies. Similarly, active tags have typically longer reading ranges than passive tags. However, a limitation of active RFID technology is that it requires battery management since the lifetime of an internal battery is approximately 5 10 years. In addition, the cost of active tags and readers is relatively high compared to barcode technology. However, since it is an evolving technology, new RFID systems have consistently decreasing costs and increasing data storage capacities. In recent years, different industries started to use RFID technology to track a variety of discrete components, such as vehicles, containers, assets and people. Tracking containers in a port is similar to tracking precast components in a storage yard: Both containers and precast components are large in size, are stacked on top of each other in large storage areas and similar cranes are used to relocate the pieces. The current applications for tracking containers at a port via RFID do not utilize RFID for automated identification of containers, but only for automated tracking of containers. In those applications, container identification processes rely on optical character recognition (OCR) [13] or manual identification [14]. RFID is only used for automatically tracking the cranes that carry the identified containers' in the storage yard via real-time locating systems. However, in this research explained in this paper, the goal is to use RFID during the entire tracking and locating processes including identification of components. Previous research on RFID in the construction industry mostly focused on three areas: (1) identification of conceptual applications of RFID technology in construction [14,15], (2) performance of RFID for automated identification during delivery and receipt of components [1,5,16], and (3) for tool tracking [7] at construction sites. In all these studies, components were either manually scanned with handheld readers [5,7,16] or scanned at a fixed portal as a trailer with components passed through a gate [1]. Only one research effort focused on

3 356 E. Ergen et al. / Automation in Construction 16 (2007) locating the materials that are scattered on a construction site, and combined active UHF RFID and GPS technologies using proximity techniques [8]. This approach provides approximate locations of materials on construction site, and it can be used as a front-end solution for the research described in this paper to identify the components' initial locations in the storage yard. GPS, a relatively mature technology, is also used for tracking objects in outdoor environments. GPS was used to track the real-time location of equipment on construction sites in several research studies [17 19]. Although Navon and Shpatnitsky [20] identified GPS technology as an accurate and robust technology for automated data collection for road construction control, they acknowledged the inaccuracy of GPS data observed which was caused by objects that shielded the communication of the GPS receiver with satellites. In combination, RFID and GPS technologies present an opportunity to track large precast components with minimum labor input in a large storage yard. However, prior to the current work, no research has been pursued to identify the requirements and reasoning mechanisms needed to exploit these advanced tracking technologies for this particular application. 3. Existing conditions and high-level requirements The research described in this paper was conducted in collaboration with a large-scale precast manufacturer. The company operates a state-of-the-art information system with barcode labeling. The storage yard at the facility where the field tests were carried out has a total area of 190,000 m 2, where pieces are typically stored from 3 weeks to 6 months before shipping. The tests were performed in the double-tee component storage yard, where the majority of problems in existing practice are observed: double tee pieces are comparatively very large, they are almost indistinguishable externally (despite being unique in internal design), and generally represent the large majority of the pieces in buildings of which they are a part. A double-tee is a type of precast concrete beam that is used as a slab in precast structures. Typical dimensions of a doubletee produced by this manufacturer are 0.6 m in height, 4 to 5 m in width and 10 to 15 m in length. They are transported within a storage yard by mobile gantry cranes. Double-tees are placed on top of each other when stored; each vertical group of components is called a stack. A stack can have up to five doubletees. A storage yard is typically divided into zones, commonly called as aisles. Each aisle is composed of rows and each row has two stacks (Fig. 1) Opportunities for improvement in the current storage processes Effective tracking and locating of pieces in a storage yard is primarily needed to provide just-in-time (JIT) deliveries to construction sites. Precast components are large in size and delivered incrementally, almost everyday, to job sites; thus, everyday many pieces need to be located at a precast storage yard. Currently, anecdotal evidence shows that locating a piece takes from 5 min to 5 h depending on the accuracy and completeness of the location information. Delays in locating pieces require utilization of additional resources to find pieces, while a failure to locate a piece has a more significant impact in terms of time and cost since that piece has to be manufactured again. Moreover, delays in shipping of pieces result in wasted resources (cranes and crews) at the site and consequent delays in construction schedules. In addition to locating pieces for shipping, precast manufacturers also need to locate pieces periodically prior to shipping, Fig. 1. A portion of the double-tee storage yard.

4 E. Ergen et al. / Automation in Construction 16 (2007) Fig. 2. Schematic layout used to record locations of double-tees in a storage yard. for inspection, surface treatment, patching, etc. For example, owners' representatives visit manufacturing plants to check pieces in terms of quality and quantity. During these visits, all pieces in a building (usually hundreds and often thousands) are inspected; delays or failures to locate pieces not only result in waste of time for both manufacturers' and owners' staff, but also foster mistrust between the two parties. The current tracking process includes a data collection activity that has to be repeated each time a piece is relocated in a storage yard. The storage process for a double-tee starts with a piece being taken to the storage yard once it is cast and cured. The piece is placed on top of one of the stacks in the yard and its ID and its location are recorded. During storage, a double-tee might be moved several times if a piece underneath that doubletee in the stack needs to be treated or shipped. Each time the piece is moved, its new location must be collected and recorded. Finally, when an erector confirms the delivery date for this type of piece in the daily load list, material handling personnel retrieve the location records for the storage yard and locate the piece to load it onto a trailer for shipping. To locate pieces effectively, the location data must be collected and recorded accurately, and they should be easily retrieved by the material handling personnel. In the cases observed at two plants, both paper-based methods and barcodes were used. In the paperbased approach, the ID of the piece is written on the piece and the aisle and row IDs are written at each row. Once a piece is placed in a stack, a worker identifies in which aisle and row the piece was placed using the storage yard's schematic layout plan, and recording the ID of the piece for that location on the layout plan (Fig. 2). This process is repeated for each piece that is relocated in the storage yard, and the storage layout plan is manually updated accordingly. When a piece needs to be located, workers scan the Fig. 3. Proposed approach for automating tracking and locating of precast pieces.

5 358 E. Ergen et al. / Automation in Construction 16 (2007) layout and manually search for the ID of the required piece. This method is error-prone and time-consuming since data collection, entry and retrieval are done manually. In the barcode approach, a barcode label is attached to each piece after it is cast. In addition, each stack location has a barcode. As a piece is placed in a stack, a worker locates the barcode on the piece and climbs up the stack to scan its barcode. Then, the same worker walks up to one end of the stack, which could be m away, to scan the barcode of the stack. This data is then sent to a database using wireless communication. In this approach, data entry is performed automatically and data is retrieved more effectively since IDs are stored in a database in a searchable format. However, data collection is still manual. Both paper-based and barcode methods are not very effective because of their dependence on manual data collection. Manual data collection is labor-intensive and thus expensive and inefficient. Since workers have to spend extra time to collect information and this is considered as a secondary task, data collection is sometimes neglected or not fully performed. This results in incomplete or inaccurate data that leads to misplaced pieces in the storage yard High-level requirements for tracking precast pieces Based on the description of the current practice described in the previous section, data collection was identified as the primary focus for improvement by utilizing advanced tracking technologies. In light of the failures of the existing system, the following high-level requirements were defined: (1) The new location of a piece delivered to the storage yard shall be identified with minimal or no worker input. (2) Any piece that is relocated in the storage yard shall be identified with minimal or no worker input. (3) The accuracy with which a piece is identified and located shall be higher than with the current system; ideally, it should get close to zero error. (4) Performance reduction of any selected technology under harsh construction conditions (e.g., noise, dust, harsh light) and under the presence of metal and concrete shall be minimal: Pieces are stored in an open air environment, therefore visibility may be obscured due to sunlight. Metal cranes are used, and the pieces themselves have large volumes of steel and concrete; metal and concrete are commonly observed in the environment. 4. Automated tracking To meet the requirements for minimum worker input and minimum change to the current storage process, an automated data collection approach was developed. In this approach, RFID tags that contain unique ID numbers are placed on precast pieces, and an RFID reader is mounted on the mobile gantry crane, as indicated in Fig. 3. Each time a piece is picked up and moved, the ID information of the piece is captured by the RFID reader. At the times of pick up and release of the load which are identified by a load cell on the crane the location of the crane (and thus the piece) is read from a GPS receiver, which is also mounted on the crane. The ID and location information of the piece is then sent to a database for retrieval for shipping or for inspection by the owner or architect later. It is expected that automatic collection of location information of the pieces can minimize inaccurate or incomplete data due to minimum human involvement as long as the accuracy of the automated approach exceeds the accuracy and completeness of the manual and barcode-based approach. Accurate location information will be used to locate pieces in a timely manner. This will reduce the resources used for locating misplaced pieces, delays in shipping, eliminate the need for replacing missing pieces, and thus improve the reliability and stability of just-in-time deliveries to construction sites. Exploration of the feasibility of this conceptual approach to automation of tracking of precast pieces using existing technologies was pursued in the following steps: (a) selection of appropriate technologies, statement of detailed requirements and identification of appropriate hardware, including preliminary field testing, (b) development of a prototype and of the necessary data processing algorithms, (c) design and assembly of a physical prototype for proof of concept and (d) field tests. The first three steps are described in this section; the results of the field tests are reported in the following section Functional requirements and technology selection The identified requirements emphasize the need for minimizing or eliminating worker input when tracking and locating components. Among available identification technologies, barcode (one-dimensional and two-dimensional), optical character recognition and touch probes do not meet this requirement because they require human action to effect a reading (they all need line-of-sight or direct contact at short range). They also require clean environments. RFID technologies that have longer reading ranges (i.e., UHF and microwave frequencies) meet the minimum worker input requirement since they enable identification of objects from relatively long distances without line of sight. Thus, RFID technology operating at UHF or microwave frequencies was selected for identification of pieces. In addition to RFID, GPS technology was selected since it is a mature technology used for tracking objects in open air. What makes GPS technology applicable and feasible in this case is that precast pieces can only be moved by cranes. It is therefore ensured that the location of the crane can be used to establish the location of the pieces, using only a single GPS receiver mounted on the crane instead of a receiver placed on each piece RFID technology The technology-specific requirements for the RFID system in the proposed approach, based on the high-level requirements, were the following:

6 E. Ergen et al. / Automation in Construction 16 (2007) Fig. 4. Distances between aisles and rows in a storage yard. To automatically identify each piece that is picked up by the crane, the reading range must be at least 3 m. This requirement derives from the geometry of the crane and the pieces (Fig. 4). It is the distance between the optimal location of the reader with respect to the location of the tag on a typical piece that is being carried. The RFID tag on the piece should always be in the range of the RFID reader during identification to enable automatic identification on demand. In the case of the mobile gantry cranes, the optimal location for the tag is the center of gravity point on each piece, while the corresponding reader antenna would be placed at the center of gravity of the picking bar (a steel girder used to distribute the load of the piece). To prevent multiple simultaneous tag readings, the maximum reading range should be limited to 5 m. This requirement derives from the geometry of the pieces and the layout of the storage yard (see Fig. 4). Given the typical width of pieces, the shortest distance between the centers of gravity of adjacent pieces is at least 5 m. When a piece is placed on a stack, it is desirable that the tags on neighboring pieces should not be readable, as this would create uncertainty as to which ID is the correct one. This requirement can be relaxed by using an algorithm to distinguish between multiple readings (as described below) or if the signal strength can be measured, in which case the strongest signal read would be assumed to be the correct ID. To minimize the performance reduction of selected technology under harsh conditions (e.g., rain or possible impacts from different pieces of equipment) and while in contact with metal and concrete, RFID tags will be encapsulated or insulated. At the chosen frequencies (i.e., UHF and microwave), the reading range of the tags decreases if the tags are attached to metal or concrete objects. The degree of degradation depends on how much of the area of a tag is in contact with the object. Thus, encapsulated or insulated tags are needed to prevent direct contact with precast pieces. In addition, RFID tags contain a chip and an antenna; if the tag is not encapsulated with a durable material, both could be damaged by moisture or impact (workers continuously climb on double-tees to attach them to the crane). The RFID antenna should have wireless communication with the host computer. The reason for this requirement is that the Fig. 5. (a) Passive UHF RFID system, (b) active UHF RFID system.

7 360 E. Ergen et al. / Automation in Construction 16 (2007) picking bar is suspended by cables from the frame of the gantry crane and moves in relation to the frame, making a cable connection highly undesirable.various products were evaluated in terms of meeting these functionalities and two specific candidate RFID systems were selected: a passive UHF RFID system (Fig. 5a) and an active UHF RFID system (Fig. 5b). Both systems had the longest reading ranges in their respective categories. The active system was claimed to have up to 30 m reading range in open air, and the passive system was claimed to have a corresponding m reading range. Both active and passive tags were encapsulated or insulated to perform effectively around metal and in harsh environments. However, both technology suppliers stated that the RFID systems need to be tested in real-life conditions to determine the actual reading range when the tags are in contact with concrete and metallic objects that exist in the environment. A preliminary field test was conducted to evaluate the performance of the two selected RFID systems in real conditions at a precast storage yard, to refine the technology-specific functionalities and to identify the reasoning mechanisms needed for the prototype application. Since no RFID equipment with wireless communication with the antenna is available, the RFID tags were placed at the center points of the long sides of the pieces and the antenna was placed on the operator's cabin. To test the RFID systems, a piece was picked up by the crane and carried to another stack; tags were read continuously during the process to determine if the piece that was picked up could be detected all the time. In addition, the reading success for tags that were placed at different locations on a component was investigated. The results of the preliminary test demonstrated that the active RFID system performed well in-real-life conditions; however, the passive RFID system did not meet the requirements. For the passive system, the reading range was measured to be between 1 and 5.5 m, i.e., it was not consistently and reliably greater than the minimum requirement (3 m). In addition, detection of the tags was not reliable because tags were observed to fall out of reading range if they were not in line with the antenna both horizontally and in elevation. This demonstrated that passive RFID systems are not reliable in a dynamic environment, where the antenna and the tags are not closely in line with each other all the time. Fig. 6. Hardware setup in the prototype system.

8 E. Ergen et al. / Automation in Construction 16 (2007) The observed reading range for the active UHF RFID system was approximately m, which is 20% 25% of the nominal reading range. This result meets the minimum requirement. The reading range of the active RFID technology was sufficient to read the piece ID regardless of the location of the picking bar. However, it was not possible to read through a component. Another interesting result of the field test of active RFID tags was that, during relocation, the reader not only detected the tag that was attached to the piece being relocated, but also other tags that were close by. The inability to restrict the reading range, violating the maximum range requirement, demonstrated the need for a reasoning mechanism that can filter all the tag IDs that are collected during relocation in order to uniquely identify the piece that is actually picked up GPS The technology-specific requirements for the GPS were identified as follows, based on the high-level requirements and the proposed approach given in the previous section: To uniquely identify the locations of pieces that are placed in different rows or aisles, the GPS unit will have an accuracy of at least 2.5 m. The distance between the aisles is approximately 6 m and the distance between two adjacent rows is approximately half a meter (Fig. 4). Given the typical width of pieces, the shortest distance between the centers of gravity of adjacent pieces is at least 5 m. The GPS receiver shall have a wireless communication with the host computer. As explained above, the approach for identifying the location requires that location data be collected at the center points of pieces. Thus, the GPS receiver must be placed at the center point of the picking bar of the crane. As in the case of the RFID antenna, since the picking bar is a mobile component of the crane, wires connecting the GPS to the host computer would limit the movement of the picking bar. The GPS unit will be durable to function in open air conditions. Since the GPS unit will be placed on the picking bar of the crane, it should be durable to open air conditions such as rain. Budget constraints dictated that a GPS receiver with less than the required accuracy was used for the field tests and the prototype. The GPS receiver used had a nominal accuracy of 5 m with WAAS (Wide Area Augmentation System) data correction and 15 m without the WAAS capability. WAAS is based on a series of satellites and ground antennas that correct data transmitted between satellite and GPS devices. The GPS receiver used in the test used wireless communication with the host computer using Bluetooth technology. It was also weatherproof. In the preliminary field test, the GPS unit was Fig. 7. (a) Schematic top view and (b) schematic side view of a gantry crane carrying a double-tee at two extreme locations (positions 1 and 2).

9 362 E. Ergen et al. / Automation in Construction 16 (2007) Prototype system design Fig. 8. An example explaining the method for unique identification. The figure shows a plan view of a storage yard and movement of the crane along with the piece IDs detected. mounted on the picking bar. No problems observed in communicating with the GPS receiver placed on a metallic picking bar or receiving GPS signals, and the accuracy proved to be close to the nominal accuracy. The inaccuracy of the GPS unit was overcome by using an approximation approach to identify the location and by assuming that row-level accuracy for location is acceptable (since currently the company tracks pieces by row numbers instead of stack numbers). The approach, which is explained in the following sections, acquires location data at center points of pieces and compares them with the closest stack location. The closest distance between center points of two stacks is approximately 11 m for the pieces that are placed at two stacks in adjacent aisles. Thus, the accuracy required was half of this minimum distance, which is 5.5 m. The prototype system was developed by integrating the active RFID system with a GPS unit using a laptop as a host computer. The components of the system and their installation locations are given in Fig. 6. The RFID system was composed of a reader, an antenna and active tags. The reader is in the form of a PCMCIA card plugged into a laptop, which was placed in the operator's cabin. Because no RFID equipment with wireless communication with the antenna is available, the antenna was placed on the windshield of the operator's cabin and attached to the reader using an RF cable, and the tags were mounted at the center of one of the long sides of the piece. The operator's cabin and the center of the long sides of a piece were observed to be approximately in line with each other both horizontally (Fig. 7a) and in elevation (Fig. 7b), most of the time during which a piece is being carried by the crane. This alignment would ensure that the tag attached to the piece is in the range of the reader. Based on the relative locations of the tag and reader in the prototype setup, and the topography of the storage yard, the practical required reading range was identified as approximately between 0.3 m and 6 m 8 m to be able to read the piece that is on the far side of the crane (Fig. 7a). The tags were UHF active tags with a battery of 5 years of lifetime and a LED that can be flashed as the corresponding ID is read. The tags were hung loosely at the edge of the flange of each piece opposite its center using plastic wire and tape. The GPS receiver was placed on the picking bar using its magnetic mount and it communicated with the laptop using a Bluetooth connection Prototype application A prototype application was developed to demonstrate how the system proposed in this research will: (1) communicate with the GPS receiver and RFID reader to acquire coordinates and tag ID data, (2) identify the tag ID that belongs to the piece that is picked, (3) determine the location of the pieces that are moved, and (4) store the new location with the ID of the piece that is moved in a database. During prototype implementation, two reasoning methods were developed. The first was for Fig. 9. Identification of the location of a piece at point A using a geo-referenced site plan.

10 E. Ergen et al. / Automation in Construction 16 (2007) Fig. 10. The user interface of the prototype application for creating a geo-referenced map. identifying the piece that is picked up by the crane, and the second was for identifying which stack and row the piece was placed in. The application that integrates GPS and RFID information was developed in Visual Basic.NET Identifying a piece that is relocated This method was developed to identify a piece that is relocated in the storage yard. It filters all of the IDs that are detected in the environment as they come within the range of the reader to identify which among them belongs to the piece that is moved. The method is based on the observation that only the ID of the piece carried appears in all readings performed as it is being transported by the crane, while readings of all the other piece IDs are transient. The method requires that reading the tags within the proximity of the reader must start as a piece is picked up by the crane and continue frequently (every 2 s) as the piece is carried to another stack. Reading stops when the piece is lowered onto its new location. Once the piece is released, the proportional occurrence of each ID in the total number of readings is calculated, and the ID with the highest occurrence rate is identified as belonging to the piece being picked up. Possible limitations of this approach and how it is overcome are explained in the following paragraphs. An example can be seen in Fig. 8, where piece 421 is carried by a mobile crane from stack C to stack A. As piece 421 is picked up, the reader detects it and also piece 103 (which is in the same stack) and pieces 422 and 424 (which are behind it). As the crane carries piece 421 to stack A, the reader on the crane detects Fig. 11. The user interface of the prototype application for collecting ID and location information for a relocated piece.

11 364 E. Ergen et al. / Automation in Construction 16 (2007) Fig. 12. Picture and plan view of the test area. pieces 101 and 102 as well as piece 421. Similar behavior is observed along the way as seen in Fig. 8. Finally, when piece 421 is lowered onto stack A, the reader detects piece 100, which is in the same stack. At this point, the method calculates that ID 421 had a 100% occurrence rate, while all others were less than 100% and thus identifies 421 as the piece carried. The method overcomes two possible exceptions that could occur due to possible obstructions or interferences in the environment. The first occurs when no IDs are detected during a reading. In this case, the reading is ignored. The second exception occurs when the piece that is picked up is not detected in isolated readings due to random obstructions or interferences. In this case, the rate of occurrence of the piece carried is less than 100%, but still significantly higher than that for other pieces. Conceivably, in rare cases where the occurrence rate of some other piece or pieces is less than a user-calibrated threshold rate, the crane operator can be asked to flash the tag (perform a single reading) to visually identify the tag and confirm that it belongs to the piece that was carried Determining the location of a piece This approach is developed to determine the location of a piece in the storage yard in terms of aisle and row numbers. In the field test case, the storage yard is divided into aisles and aisles are divided into rows. In each row, there are two stacks, where pieces are stacked on top of each other. In the current practice, the piece locations are tracked by aisle and row only; stack numbers are not recorded. Nevertheless, in order to explore the boundaries of the prototype system, the tests were setup to attempt identification of location at the level of the stack position. In this method, a geo-referenced map of the storage yard is created once, and then used to identify locations of the piece by comparing the coordinates received from the GPS with those in the geo-referenced map. To generate the geo-referenced map,

12 E. Ergen et al. / Automation in Construction 16 (2007) the user collects data at two opposite corners of each stack. The method then calculates and stores the middle point and corner points of each stack. Once a geo-referenced map is created for a storage yard, it is used to identify the stack in which a piece is placed. This is done by comparing the GPS data collected to the geo-referenced map each time a piece is relocated. Since some inaccuracy exists in the GPS device, this comparison is performed by finding the distance between the current location of the piece to each stack, and identifying the stack that is the closest to the location of the piece, as illustrated in Fig User interface The user interfaces implemented for the prototype are shown in Figs. 10 and 11. The first interface enables calibration of the geo-referenced map of the storage yard (Fig. 10). To collect stack coordinates, the user stands at the first corner, enters the stack name and clicks on the refresh button. Once the incoming GPS coordinate readings converge to stable numbers (i.e., when the difference in the fourth digit after the decimal point varies less than 3), the user confirms capture of the first corner and progresses to the opposite corner. The procedure is repeated for all the stacks in the yard. The second interface screen (Fig. 11) was developed to enable the crane operator to trigger identification of a piece that is relocated and to collect its new location data. This interface is only applicable in the prototype: in a commercial application, the triggering action would be automated. For example, a load sensor on the crane can be used to issue start and stop reading signals to the GPS and RFID sub-systems when the load is lifted or when it is released, thus obviating the need for any user action at all. 5. Field tests and results The prototype system was tested in a real-life scenario, in which a piece was picked up by a mobile gantry crane and carried to another location. A person in the operator's cabin collected and recorded the ID and location information of the piece using the prototype application. The gantry crane is a critical resource for the precast company since precast components can only be relocated by gantry cranes. Also, operating cost of the crane is high. Thus, the crane was available for a day for the test. The worker, who operated the crane, hooked and unhooked the precast components to/from the crane. In the process of moving one component from one location to another, the worker climbed down the crane and up to the stack to hook the component to the crane and repeated the same steps to unhook the component. Including the set-up and initial testing time, the test could be repeated 18 times in one day. A densely populated area of the double-tee storage yard was chosen as the test location to test the effect of tags attached to other pieces in close proximity with the precast component that will be moved (Fig. 12). In preparation for the tests, the GPS coordinates for the stacks in the test area (shown shaded in Fig. 12b) were retrieved and stored in a database. The hardware was installed on the crane as shown in Fig. 6, and eight tags were attached to eight pieces in the test area. Six of those eight tags were attached to the pieces at the top of each stack. One of the tags was attached to the piece that was to be picked up, and the last one was attached to the piece at the second highest level in the stack immediately behind the piece that was to be picked up. Eighteen relocations of components were performed and they involved three types of moves (Fig. 12a): (1) three same-row moves as a piece is moved from one stack to another one in the same row, (2) six adjacent-row moves as a piece is moved from one stack to another in an adjacent row, (3) nine adjacent-aisle moves as a piece is moved from one aisle to an adjacent aisle. As explained earlier, same-row relocations are not tracked in current practice. The three type 1 moves (as defined above) were included to explore the boundaries of the prototype system. The results of the tests are given in Table 1. The prototype was 100% successful when identifying the piece during all 15 adjacent-row or adjacent-aisle moves, but of the three same-row moves, only in one instance could the piece ID be identified. In the other two instances, there were no false-negatives in the list, and the user was able to correctly select the piece ID from the shortlist. This result was expected for the same row move, because, unlike the other moves, in the same-row move, the crane is stationary; only the picking bar that holds the piece moves from one end of the crane to the other. Since the lack of wireless communication between the RFID antenna and reader in the prototype prevented mounting the antenna on the picking bar, reader was attached to the crane itself. Therefore, the tags in the range of the reader do not change during relocation of the piece unless other tags on other pieces are blocked by the carried piece. During the evaluation of the test results related to the location of the components, if the identified location of a component was in the same aisle and row as the actual location of the component, this result was considered acceptable. Of the 18 relocations, 61% were identified within the acceptable limits. This was due to the inaccuracies associated with the inexpensive GPS used in the test and cloudy and stormy weather, which might block the GPS signals, during the test. Utilizing a GPS with sub-meter accuracy, installing a reference base station for correcting the GPS readings, are expected to solve this problem. 6. Evaluation of the prototype system The first and second high-level requirements for an automated precast tracking system stated that data collection should Table 1 Field test results Move type Number of tests RFID tag readings Correct ID List of possible IDs GPS location results Exact Same row ( 5 m) 1. Same row Adjacent row Adjacent aisle Acceptable results (89%) 11 (61%) (all moves) Acceptable results (move types 2 and 3) (100%) 10 (66%) Next aisle ( 15 m)

13 366 E. Ergen et al. / Automation in Construction 16 (2007) require minimal or no user action. In the prototype system, the crane operator is required to press several buttons in the user interface in the crane cabin to collect ID and location data. No other data collection activities, such as climbing on top of a stack or walking along rows to scan barcodes, or manual data entry, are needed. In a production system, the RFID and GPS subsystems could also be integrated with a load sensor placed on the crane to eliminate the activities of the crane operator for data collection, thus achieving a fully automated system. The third requirement stated that the accuracy with which a piece is identified and located shall be higher than possible with the current system. The piece was identified semi-automatically in all cases and fully automatically in cases where the crane was not stationary. If the reader was placed closer to the tags (e.g., on the picking bar), and the reading range was limited accordingly to read only the tag on the piece, fully automated reliable identification could be achieved also for the cases where the crane does not move. The location was identified with 61% success for relocations that are currently tracked in the system. The failure of the location identification would probably be corrected using a GPS with better accuracy, and by combining the approach with tag ID and signal strength data in the environment to increase the confidence. The fourth requirement related to the need for consistent performance under harsh construction conditions and in the presence of metal and concrete. Both the RFID tags and GPS were designed for harsh environments, and no problems were observed in terms of receiving the signals from RFID tags and GPS unit in the field conditions. Apart from a small number of individual readings, the RFID tag of the piece that was carried could be consistently detected in the required range. 7. Conclusions The field tests demonstrated the basic feasibility of an automated system that integrates RFID and GPS technologies for tracking precast pieces in a storage yard. The prototype system used for the tests had all the features of the conceptual system, with three exceptions: the RFID antenna was mounted on the crane cabin rather than on the picking bar in close proximity to the piece centers, the GPS used did not meet the minimal accuracy requirement, and user intervention was required to activate the readers of both subsystems. Despite these restrictions, the prototype system was successful in semi-automatically identifying all of the pieces that were relocated. The reading range of the RFID tags was reduced to 1/4th 1/5th of the nominal reading range for open air environments; however, this reading range was still sufficient for the precast tracking case. Mounting a wireless antenna on the picking bar, and using a load sensor to activate reading, would enable the system to operate fully automatically in all cases. Analysis of the location information shows that approximately 60% of the location information was within the acceptable limits. The low success rate was attributed to inaccuracies of the inexpensive GPS that was used in this test. No performance reduction was observed due to the presence of metal or concrete in the environment, even while using Bluetooth communication. Utilizing a GPS with sub-meter accuracy or installing a base station for correcting the GPS readings (using differential GPS technology) are expected to increase the rate of successful location identification. In the prototype system, data collection activities did not require any change to the current storage process (except requiring the operator to press buttons to activate data the readers). Moreover, all data collection activities previously required, such as scanning a barcode or manually entering data to the database system, were eliminated. In the short term, similar tests should be conducted to collect more data points and further research should focus on refining the prototype system as suggested, to further validate the hypothesis that a fully automated and reliable system is feasible. In the long term, application of the same concept to monitoring of precast pieces at the construction site should also be explored. Acknowledgments This work was partially funded by High Concrete and the Precast/Prestressed Concrete Institute (PCI). The authors gratefully acknowledge this sponsorship. The authors also greatlyappreciate the assistance and support received from High Concrete. 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