A BIM-based monitoring system for urban deep excavation projects
© Wu et al.; licensee Springer. 2015
Received: 11 September 2014
Accepted: 26 December 2014
Published: 16 January 2015
Deep excavations in urban areas have the potential to cause unfavorable effects on ground stability and nearby structures. Thus, it is necessary to evaluate and monitor the environmental impact during deep excavation construction processes. Generally, construction project teams will set up monitoring instruments to control and monitor the overall environmental status, especially during the construction of retaining walls, main excavations, and when groundwater is involved. Large volumes of monitoring data and project information are typically created as the construction project progresses, making it increasingly difficult to manage them comprehensively.
To address the abovementioned issues, this project develops a Building Information Model (BIM)-based monitoring system to integrate and visualize monitoring data for risk assessments during urban deep excavation projects. A BIM can be used to establish a comprehensive model of managing a construction project. The system developed herein can access required data from BIM models, and allow complicated numerical data to be displayed effectively in an easily understandable visual format. It is composed of intelligent building components, which includes data attributes and parametric rules for each object. This system can provide a construction project team with a full monitoring view of the ongoing project, along with functions to integrate the information and display it in various ways to present complex engineering monitoring information quickly and clearly.
Testing examples of the developed system on the excavation of the O6 station in the Kaohsiung metro system in Taiwan are presented to illustrate the improvements of safety management for adjacent structures in urban deep excavation projects. The risks and issues affecting the safety of excavation activities and proximal structures during a project can be identified earlier through effective visualization of information in the system, enabling construction project teams to address them promptly and appropriately by performing accurate risk assessments and decision making.
This system assists construction project teams in identifying and understanding possible blind spots when attempting to achieve risk assessments during urban deep excavation projects, and further enables the adoption of mitigation measures to reduce risk levels.
KeywordsBIM Monitoring Data visualization Data management Risk assessment
As a result of rapid urbanization, underground structures such as deep excavations and tunnels, have been widely used to increase the availability of space by moving underground. As these structures are often constructed below mature and densely populated city areas, any associated accidents can easily result in serious damage to adjacent structures or cause human casualties. Ground settlement is especially thought to have the greatest impact on the nearby environment. At present, numerical analysis methods and field monitoring are two commonly used tools to predict and assess the ground settlement induced by an excavation.
Numerical analysis method
Through the collection and evaluation of data of surface settlement from underground construction in various locations, Peck (1976) first defined the possible zone of influence around an excavation. Bowles (1988) recommended a procedure for estimating the excavation-induced ground surface but required the lateral wall deflection to first be calculated. Clough and O’Rourke (1990) proposed various types of envelopes of excavation-induced ground surface settlements and these were mainly classified by ground conditions. Ou (2006) developed a method to predict the details of ground surface settlement on the basis of studies, and concluded that in principle there are two types of settlement curves, the spandrel and the concave; and that the magnitude of wall deflection in a cantilever model would define the type of settlement curve. Similarly, various studies have been conducted on the application of numerical analyses in deep excavations, but they are not addressed here owing to limits on paper length.
The influence of adjacent structures can only be identified with the effective use of monitoring data obtained from field instruments. Professional engineering knowledge and experience can be used to interpret the above-mentioned results to avoid and reduce accidents from occurring. Related studies, such as that by Qiu et al. (2009) described a monitoring and analysis information system to analyze the data of construction monitoring and provide timely information feedback for safety in urban subway construction. Shao and Macari (2008) presented a systematic procedure, referred to as “Information Feedback Analysis”, which is used to predict excavation-induced deformations by collecting field information such as displacement. Ran et al. (2011) designed and implemented a long-term monitoring and safety evaluation system for the deep excavation of a metro station. However, the system behavior could only be identified with the effective use of monitoring data obtained from field instruments.
Data visualization and communication
2D Data Visualization
As an example of a GIS application, Zhou et al. (2012) developed a natural environment information management system. Its core concept lies in collecting effective and reliable environmental information, increasing utilization rates, sharing a degree of environmental information by advanced information technology, and providing timely and scientific foundations for environmental monitoring and management. Li and Li (2012) designed a monitoring and warning system for facilitating the reduction of capital investment and preventing geological disasters. The automation and information processing for landslide hazards in this system can provide a basis for early warning of landslides in a village called Diao Zhongba. Şalap et al. (2009) presented the development of a GIS-based monitoring and management system for underground mine safety in three levels, namely constructive safety, surveillance and maintenance, and emergency. The proposed system is expected to be an efficient tool for improving and maintaining healthy standards in underground coal mines and has the potential to be extended to a national GIS infrastructure. GIS facilitates monitoring underground safety by allowing the use of spatially referenced data, in which the system functions through its database and management tools, processing and analysis tools, and featured display tools (Dijk, PM van et al. 2004). However, a GIS application mainly focuses on a 2D plan, and thus cannot provide an accurate source of information about the height and depth in a 3D space.
3D Data Visualization
Meanwhile, BIM is a relatively new technology that facilitates better information integration and management (Eastman et al. 2008). Many engineering companies employ BIM for information integration, visualization, and parametric design to reduce both the duplication of work and the complexity of interface integration; and to assist construction project teams in managing risks and making project decisions. BIM offers detailed 3D visualization and the ability to organize large volumes of data related to buildings for management. Hsieh and Lu (2012) demonstrated that a visualization system enables users to interpret monitoring data effectively and intuitively, reduces misinterpretation, improves communication efficiency, and facilitates efficient decisions that are supported by the monitoring data gathered. Kim et al. (2012) focused on the monitoring and visualization of aggregated and real time states of various energy usages represented by location-based sensor data accrued from city to building scale. Design representations are no longer 2D drawings. Instead, designers use 3D BIM models that are assembled in the same way a building is constructed. BIM can be used in construction firms to better reuse the accumulated management information. Employing information within the BIM model allows everyone on the project team to make better and more-informed decisions across the entire project lifecycle of building and infrastructure projects.
Project aim and objective
A BIM-based monitoring system was developed in this project. The system is implemented based on commercial hardware and software comprising the Bentley AECOsim Building Designer, which supports visualization of a 3D model with some capabilities for 3D object manipulation and information query with Application Programming Interfaces (API) for functionality extensions. A Mass Rapid Transit (MRT) deep excavation project was chosen as a case study for demonstrating the results of this project. Finally, environmental and monitoring data were integrated and visualized to allow users to quickly obtain the information required to make decisions and conduct evaluations to solve problems and plan appropriate emergency measures. A detailed discussion is presented in the next section.
Building information model
Project data is required for project participants to understand background information of the project, such as the project name, address, owner, and the construction company.
BIM technology currently focuses on the visualization of the changing status of 3D shapes during the construction process. Therefore, this project integrates different types of 3D models into the BIM system and includes the building model, excavation model, retaining wall model, and supporting elements for visualization.
The excavation performance differs according to different ground conditions. This system integrates soil properties and groundwater statuses for assisting construction project teams to understand the environmental status for making decisions.
Generally, construction project teams will analyze and predict ground settlement and the influence zone using empirical methods prior to a deep excavation. In this project, these results were integrated and visualized in the BIM system to assist construction project teams to assess the environmental impact in a synthesized manner.
Construction project teams normally set up various types of monitoring instruments to collect the necessary data to monitor the status of different environmental variables. The related data integrated into the BIM system include types, standard values, and units recorded by the various field-monitoring instruments.
Monitoring data management
Data inputThis system provides two methods for data input, as shown in Figure 4. For visualizing the location of monitoring instruments accurately in the BIM system, the different kinds of monitoring instruments are represented by the different 3D primitive solids shown in Additional file 1: Table S1. A new 3D representation of a monitoring instrument can be created from the system and then the field data of the monitoring instrument can be entered or updated individually. Moreover, as monitoring instruments are updated frequently, the system needs to provide a batch input function to effectively deal with the large amount of monitoring data arriving from the database at any one time.
Data AccessAs shown in Figure 5, this project designed and developed an integrated a BIM model to describe and store information including 3D models, stratum data, analysis data, project data, and monitoring data for an entire project. All project data can be divided into two types: non-graphic data, which is stored into a database; and a 3D geometry, which is stored in the AECOsim object model (a .dgn file). Each object (graphic element) that is linked to a database has a database linkage attached to the element. This is sometimes called the MSLINK in Bentley Systems. Users can double-click on the 3D object in AECOsim and summon detailed information on the 3D object that was extracted from the database and vice versa, as shown in Figure 6.
Monitoring data visualization
1D & 2D VisualizationsAs shown in Figure 7, 1D Visualization produces text and numeric values for displaying attributes of objects, general project information, and monitoring data; while 2D Visualization shows graphs and charts for presenting the results of calculations and analyses between objects. A user can select a monitoring instrument for query and the system will show its information and status in detail.
3D VisualizationAs shown in Figure 8, 3D Visualization shows 3D models of buildings for presenting the actual status. In a 3D environment, this system resembles a virtual comprehensive central control room; it not only provides 3D manipulation functions such as fly, walk, and rotate, with no blind spots from the point of view of a user, but also controls all statuses of monitoring instruments in a construction site.
Retaining Wall Construction
According to the monitoring results of the rapid transit system in Hong Kong, after the completion of the diaphragm walls and before the main excavation, the accumulated deformation was found to be 40–50% of the total deformation after the completion of the main excavation (Clough and O’Rourke, 1990). Clough and O’Rourke (1990) found that the ratio of the maximum settlement induced by the construction of diaphragm walls to the depth of the trench was 0.15% according to in situ monitoring results.
Most problems encountered in deep excavations have a direct or indirect relationship with groundwater. Groundwater-induced problems in an excavation may arise from insufficient investigation of groundwater or geological conditions. Thus, it is necessary to perform detailed investigations of groundwater and its influences on soil or structures during excavations (Ou, 2006).
From observations of the shapes or types of ground surface settlement, it can be observed that the soil at the back of a retaining wall moves forward and down as the retaining wall deforms under normal conditions, producing ground settlement. Peck (1976) proposed that the zone of influence of a settlement should be two or three times that of the excavation depth. Clough and O’Rourke (1990) proposed that excavations in sandy soils might induce a zone of influence of settlement about twice that of the excavation depth (Ou, 2006).
Adequate risk management during construction is dependent on accurate and on-time reporting of key measures from the intensive monitoring system. The system can evaluate the probable environmental impacts from the critical monitoring data and the building information model and can enable construction project teams to identify and manage risk. Three colors are defined to visualize the different risk statuses in a 3D environment. The different kinds of monitoring instruments are represented by the different 3D primitive solids. Additional file 3: Table S3 shows the color scheme implemented for displaying each risk status. Furthermore, the 3D objects are highlighted in different colors according to their risk status. For example, if the field value of an instrument is more than the limit value, the representative 3D primitive solid will be showed in red, and the construction project team must urgently adopt mitigation measures to reduce the risk level.
Results and discussion
Monitoring provides tangible information on a regular basis over an extended period of time about past and present conditions of the environment and thus, generates large amounts of data. Until now, many construction project teams have collected and analyzed monitoring data for each instrument manually, consequently wasting a lot of time. Moreover, these data will be stored in a file format. This approach is difficult for handling monitoring data used for querying and reviewing. In this project, all monitoring data will be collected and stored into a database and visualized based on a BIM system. A construction project team can manage monitoring data via a database or a visualization environment. These systems provide many functions for assisting a user to manage data efficiently and effectively.
Read and discriminate
After collecting monitoring data, a construction project team needs to read and discriminate the data to determine the monitoring status for making decisions. Currently, analysis of monitoring data is conducted manually, and is error-prone. In this project, functions of risk assessment were implemented into the system to automatically facilitate reading and discriminating. This approach assists in reducing computational time and errors in the risk assessment processes.
CommunicationListon et al. (2000, 2001) studied the problem of communication in engineering workspaces, which could be variably defined as physical or virtual spaces where people work, share, and use information. From observations of numerous designs and construction review meetings, they discovered that teams spent most of their time on descriptive, explanative, evaluative, and predictive tasks. All such tasks are critical to enable improved decision-making by project managers. In this project, the developed system provides three styles of visualization for communication among the project participants; namely 1D (text), 2D (graph and chart), and 3D (3D Model). Figure 12 shows the overall monitoring data in a 3D visualization environment. A construction project team can control the construction status in single view. Figure 13 shows the results of a comprehensive evaluation via text and graphs. These visualization styles can conveniently provide different and sufficient information for different requirements for communication.
Risk assessment is an important task for urban deep excavation projects. This project took advantage of the information integration and visualization capabilities of BIM to assist construction project teams with assessing possible environmental risks. The BIM-based monitoring system can provide users with a full overview of the ongoing urban deep excavation project, along with functions to integrate the information and display it in a multi-dimensional view. There are three main positive characteristics of the system. First, with an easy-to-use GUI, the efficiency of BIM manipulation and information acquisition is increased. Building information and monitoring data are also robustly stored and managed throughout all stages of an urban deep excavation project. Second, by representing the monitoring data and the results of an environmental risk assessment visually, the project team can quickly grasp the current state of an ongoing project, and communicate and coordinate accordingly with other project participants more efficiently. Third, through the comprehensive evaluation functionality, the project team can accurately monitor the project status and risks. This study demonstrated the use of a BIM of a deep excavation project to integrate a 3D model and relevant information about the retaining walls, excavation, and adjacent buildings; and then visualized all analysis and assessment results to present the likely locations and degrees of risk and safety under different situations. This was done to enable users to interpret monitoring data effectively and intuitively, reduce misinterpretation, improve communication efficiency, and facilitate efficient decisions that are supported by the gathered monitoring data.
- Bowles, JE. (1988). Foundation Analysis And Design (4th ed.). New York, USA: McGraw- Hill.Google Scholar
- Clough, GW, & O’Rourke, TD. (1990). Construction-induced movements of in situ walls. Design and Performance of Earth Retaining Structure, ASCE Special Publications, 25, 439–470.Google Scholar
- Dijk, PM van, Wang, HY, Genderen, JL van, (2004). Earth observation knowledge transfer: the example of ITC's coal fire project. Proceedings of the 20th International Society for Photogrammetry and Remote Sensing (ISPRS) Congress, Istanbul, TurkeyGoogle Scholar
- Eastman, C, Teicholz, P, Sacks, R, & Liston, K. (2008). BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers and Contractors. USA: Wiley.View ArticleGoogle Scholar
- Elbeltagi, E, & Dawood, M. (2011). Integrated visualized time control system for repetitive construction projects. Automation in Construction, 20(7), 940–953.View ArticleGoogle Scholar
- Howell, I, & Batcheler, B. (2005). Building Information Modeling Two Years Later - Huge Potential, Some Success and Several Limitations. The Laiserin Letter, MayGoogle Scholar
- Hsieh, YM, & Lu, YS. (2012). Visualization system for field monitoring data and its effectiveness. Automation in Construction, 26, 54–68.View ArticleGoogle Scholar
- Hsiung, BC. (2009). A case study on the behaviour of a deep excavation in sand. Computer and Geotechnics, 36(4), 665–675.View ArticleMathSciNetGoogle Scholar
- Kim, SA, Shin, D, Seibert, T, & Walz, SP. (2012). Integrated energy monitoring and visualization system for Smart Green City development: Designing a spatial information integrated energy monitoring model in the context of massive data management on a web based platform. Automation in Construction, 22, 51–59.View ArticleMATHGoogle Scholar
- Li, XP & Li, YA. (2012). Design of GIS-based Monitoring and Early-warning System of Landslide Hazard in Diao Zhongba. Energy Procedia, 16(B), 1174–1179.Google Scholar
- Liston, K, Fischer, M, & Kunz, J. (2000). Designing and evaluating visualization techniques for construction planning, Proceedings of the 8th International Conference on Computing in Civil and Building Engineering, Stanford University, CA, USA, 1293–1300Google Scholar
- Liston, K, Fischer, M, & Kunz, J. (2001). Requirements and Benefits of Interactive Information Workspaces in Construction, CIFE Technical Report, Stanford University.Google Scholar
- Ou, CY. (2006). Deep Excavation: Theory and Practice. Netherlands: Taylor & Francis.Google Scholar
- Peck, RB. (1976). Deep excavations and tunneling in soft ground. Proceedings of the 7th International Conference on soil mechanics and foundation engineering, Mexico City.Google Scholar
- Qiu, D, Liang, Q, Li, Q, & Wu, R. (2009). Safety monitoring information system of urban subway. Proceedings of the 2009 WASE International Conference on Information Engineering, July 10–11, Taiyuan, Shanxi, China.Google Scholar
- Ran, L, Ye, X, & Zhu, H. (2011). Long-term monitoring and safety evaluation of a metro station during deep excavation. Procedia Engineering, 14, 785–792.View ArticleGoogle Scholar
- Şalap, S, Karslıoğlu, MO, & Demirel, N. (2009). Development of a GIS-based monitoring and management system for underground coal mining safety. International Journal of Coal Geology, 80, 105–112.View ArticleGoogle Scholar
- Shao, Y, & Macari, E. (2008). Information feedback analysis in deep excavations. International Journal of Geomechanics, 8, 91–103.View ArticleGoogle Scholar
- Treicher, DG. (1967). Are you missing the boat in training aid? Film and AV Communication, 1, 14–16.Google Scholar
- Zhou, Q, Yuan, Y, & Kang, Y. (2012). GIS application system design applied to information monitoring. Physics Procedia, 25, 2235–2241.View ArticleGoogle Scholar
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