Data-Driven Monitoring System for Preventing the ...

Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures

Chunhee Cho1, Kyungki Kim2, JeeWoong Park3*, and Yong K. Cho4

1

Post-Doctoral Scholar, Department of Civil and Environmental Engineering and Construction, University

of Nevada, Las Vegas, Email: [email protected] Phone: 404-626-7054 2

Assistant Professor, Department of Construction Management, University of Houston, 4734 Calhoun

Road #111, Houston, TX 77204-4020, E-Mail: [email protected], Phone: 770-361-2262 3

Assistant Professor, Department of Civil and Environmental Engineering and Construction, University of

Nevada, Las Vegas, Email: [email protected], Phone: 702-895-1568 (*corresponding author) 4

Associate Professor, School of Civil and Environmental Engineering, Georgia Institute of Technology,

790 Atlantic Dr. N.W., Atlanta, GA 30332-0355, United States, Email: [email protected]

Abstract As temporary structures, scaffolds have essential roles to hold workers, materials, and equipment throughout construction activities. However, since a safety inspection for scaffolds is mainly visual and labor intensive, the OSHA standards related to scaffolds are frequently violated. Improper management of scaffolds has caused scaffolding collapses that have a potentially detrimental effect and liability on workers’ lives. This paper discusses the significance of scaffolding collapses and explores a method to perform scaffolding monitoring. To establish an integrated method, this research cross-connects various components (e.g., strain data, finite element model (FEM)-based structural analysis, machine learning, and an actual scaffold) in the presented framework. More specifically, this framework for a smart monitoring system is involved with 1) developing a wireless strain sensing module for data collection, 2) modeling an FEM and learning data for failure mechanisms through FEM to characterize scaffold behaviors under certain loading conditions, and 3) investigating a machine-learning algorithm (i.e., support vector machine) for decision 1

Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) making. The FEM simulation analyzes a scaffolding to calculate strain values for each scaffolding column from randomly generated 1,200 load cases. Load-related strain data form training and testing sets for the machine-learning algorithm that enables the distinguishing of scaffolding conditions such as safe, overturning, uneven-settlement, and over-loading conditions. In the experimental validation, the developed wireless strain sensing modules perform the real-time strain measurement and the machine-learning algorithm to successfully estimate the status of the scaffolding structure with 97.66% accuracy on average. The proposed method could escalate a monitoring paradigm for temporary structures from a labor-intensive manual inspection to a systematic real-time approach. Keywords: Temporary structures, Smart monitoring, Machine learning, FEM, Scaffold

INTRODUCTION Approximately 2.3 million construction laborers, or 65 percent of the construction industry, work on scaffolds according to Occupational Safety and Health Administration (Occupational Safety and Health Administration (OSHA) 2017a). However, recent statistics (Occupational Safety and Health Administration (OSHA) 2016) have indicated that scaffolds, the OSHA standards of which have been cited as the third most frequently violated, are inadequately managed on site. Such improperly managed scaffolds have led to approximately 60 deaths and 4,500 injuries and costs exceeding $90 million a year (Occupational Safety and Health Administration (OSHA) 2003). A Bureau of Labor Statistics study identified three major categories associated with accidents: scaffolds collapsing, workers slipping, and objects falling (Occupational Safety and Health Administration (OSHA) 2017a). Although the potential for accidents relating to the two latter cases exists, they typically do not result in accidents if workers adhere to preventive measures. However, whether or not such care is taken, if scaffolds are not properly managed/inspected, such preventive action may not be helpful at preventing accidents resulting from the collapse of scaffolding, which is not only catastrophic in itself but also financially damaging both to companies and individuals. For example, the collapse of a scaffold that occurred at the John Hancock Center in Chicago in 2002 killed 2

Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) and injured several bystanders (Dalrymple 2011). Another recent collapse of a scaffold in downtown Houston in 2015 led to the injuries of six workers and citations for two companies for not properly inspecting the conditions that affected the structural integrity of the scaffolding (Chron 2016). Such accidents prove that current safety practices have not been effective at protecting workers or the general public and that further improvements ensuring a safer construction environment are required. Thus, we propose a systematic approach to safeguarding scaffolding through smart autonomous monitoring. The current approach to monitoring the condition of scaffolds is an inspection by a knowledgeable, experienced individual (Occupational Safety and Health Administration (OSHA) 2002). However, visual inspection of individual parts (e.g., pipes and boards) and the structural integrity of an entire structure is subjective, error prone, and labor intensive. Because of the inability of a site manager to be ubiquitously present throughout the entire construction site, the continuous monitoring and inspecting of multiple scaffolds, even those temporarily relocated within site, is not possible. In addition, because of a lack of systematic tools, engineers encounter difficulty tracking and inspecting scaffolds on a construction site. To address these challenges, a recent study (Yuan et al. 2016) initiated an effort to automatically monitor temporary structures with sensors including load cells, switch sensors, and accelerometer, and a displacement sensor. Despite the advance made towards autonomous monitoring approaches, this state-ofthe-art method still suffers from several drawbacks, including insufficient level of structural analysis capabilities; while the presented approach demonstrated an integrated method of monitoring a temporary structure, it did not fully disclose its ability to conduct thorough structural analyses against various loading cases, which are, in fact, utmost critical to ensure worker safety. Despite the dire need to ensure the safety of scaffolds, an effective and practical method of monitoring and analyzing scaffolding structures have not been developed. Thus, to promote the safety of workers and the general public, the construction industry requires a technique that allows the continuous and reliable analysis of the condition of scaffolds via the automated monitoring of scaffolds.

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Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) To prevent the collapse of scaffolding structures, the research team proposes a framework for a smart and autonomous monitoring system that enables real-time monitoring of scaffolding structures. While many developed approaches for monitoring a temporary structure (e.g., Moon et al. 2011) require various sensors (strain gage, gyro sensor, displacement sensor, etc.), the proposed method requires only four sets of strain data to analyze a one-bay scaffolding structure. Because many structural responses are related to strain distribution, investigating strain patterns enables the estimation of structural behaviors of scaffolds. Thus, the machine-learning algorithm, which is an innovative tool for analyzing data patterns, investigates strain patterns to describe the structural conditions of scaffolds such as safe, over-loading, over-turning, and uneven-settlement conditions. This paper is structured as follows. The related literature section presents a review of conventional temporary structure monitoring, including state-of-the-art approaches in automated monitoring of temporary structures followed by research objectives and scope. Then, the following sections present the framework, the wireless strain sensing module design, the finite element method (FEM) modeling, and implementation of the machine-learning algorithm of the proposed method. The experimental validation proves that the proposed method enables the performance of real-time monitoring of the scaffolding structure. The Summary and Discussion section discusses results, limitations, and future works.

LITERATURE REVIEW Conventional Scaffolding Inspection Conventionally, the construction industry uses regulations and safety training programs in order to use scaffolds safely. OSHA provides detailed requirements and recommendations for designing, erecting, dismantling, and inspecting scaffolds (Occupational Safety and Health Administration (OSHA) 2002). Regulations regarding onsite scaffolding inspection mandate that a competent person should inspect each scaffold before each work shift and after any incident that might have affected its structural integrity. Structural integrity, as well as components (such as pipes and boards) of a scaffold, should be thoroughly 4

Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) inspected by a person with knowledge and experience. Also, safety training programs, such as OSHA 10and 30-hour construction training, are used to educate workers as to how to identify and prevent potential safety hazards related to scaffolds. While these regulations and training programs are useful in delivering essential knowledge, the actual application of the knowledge to a complex construction site is still challenging, for many reasons. First of all, in the absence of integrated information on scaffolds in schedules, drawings, or building information models (BIM), manually tracking and inspecting individual scaffolds in a complex construction environment require a considerable effort. In most construction projects, plans for scaffolds rarely exist as part of the main construction plans (Ratay 1996). Therefore, onsite managers use inefficient paper-based tools and forms to plan, manage, and inspect scaffolds. Furthermore, visual and manual inspection of the overall structure and individual parts of a scaffold relies on subjective judgment, and the process is error prone and labor intensive. Managers, who have a limited capability of presence over a construction site, cannot monitor multiple scaffolds continuously throughout their lifecycles. This limitation of monitoring scaffoldings in real time is further exacerbated by an external work environment under pressure, difficulty in identifying defects in scaffolds at a full scale, and simple human errors (Fabiano et al. 2008).

Computer-Aided Design and Planning of Scaffolding Structures The identified limitations in managing scaffolding structures triggered researchers to seek more advanced methods to deal with temporary structures. Researchers have taken advantage of technological evolution and successfully included temporary structures in project plans. Incorporating scaffolds as part of a construction plan is essential for achieving systematic monitoring of multiple scaffolds in complex projects. There have been efforts that contributed to enhancing the modeling, planning, and visualization of scaffolds during the pre-construction stage. Many approaches successfully generated designs for scaffolds. Sulankivi and Kähkönen (2010) created scaffolding objects and manually inserted them into a 4D BIM to identify potential safety hazards. Safety devices, such as guardrails, were designed as part of their scaffolding 5

Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) design. Scia Scaffolding is a commercially available software program that provides functions to manually model scaffolds in detail (SCIA Nemetschek 2015). The software program supports automated analyses for compliance with safety regulations and structural stability. Smart Scaffolder (2017) automatically generates scaffolding designs based on the basic geometric information of a building. Even though these commercial tools (SCIA Nemetschek 2015; Smart Scaffolder 2017) have a useful function to support a fast and accurate design process, the resultant scaffolding objects are not used for onsite management or inspection of scaffolds. The automated scaffolding planner developed by Kim and Teizer (2014) also generated BIM objects for scaffoldings complying with OSHA regulations and incorporated them in 4D BIM. Jongeling et al. (2008) integrated work sequences and temporary structures for concrete construction (formwork and shoring) in 4D BIM. The reviewed studies demonstrated that scaffolds can be incorporated into a 4D BIM and other types of construction plans for various purposes.

Automated Process for Monitoring Scaffolding Structures Besides the efforts on the planning of scaffolds, academic studies have been conducted to automate the process of monitoring scaffolds with the help of advanced technologies. Yabuki and Oyama (2007) use passive radio frequency identification (RFID) tags and a database to track and manage individual parts of a scaffold. This study identified optimal locations of RFID tags on pipes and boards to maximize the readability of the tags. The database was used to identify old scaffolding parts needing replacement. This study presented a robust way of managing a large number of scaffolding parts and preventing overuse of old materials. However, their approach using passive RFID tags and a database is limited to tracking of parts and does not offer an automated ability to analyze the status of a scaffold after installation. As an effort toward monitoring the status of temporary structures, Jung (2014) applied a method of image processing to automatically identify defects in scaffolding and shoring. Jung’s system analyzed video streams to characterize deformations of targeted temporary structures so that potential failures could be identified in the early stages of deformation. Moon et al. (2011) installed a network of sensors to analyze 6

Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) the condition of a scaffold with respect to deflections, loads, and strains. This approach used various types of sensors, such as inclinometer, ultrasonic, and strain gages, on different locations of pipes and boards. Similarly, Yuan et al. (2016) proposed a Cyber-Physical System that integrates the virtual model of a scaffolding and sensor-based monitoring of its physical status. Conceptually, the ubiquitous network of sensors (Moon et al. 2011) and the integration of sensor data with virtual models and simulation (Yuan et al. 2016) can provide data needed for real-time monitoring of scaffolds over a construction site. However, considering the cost and complex conditions of construction sites, these state-of-the-art approaches require unrealistic setups and potentially result in additional costs and safety hazards (e.g., wires, power cables) during both system installation and implementation; thus, not practical to be adopted by the practitioners. To develop an effective scaffolding monitoring method, it is critical to select proper ones among many monitoring sensors such as RFID tags, camera, inclinometer, and strain gage. Selection of proper sensors is particularly important because of the nature of scaffolds that interfere the monitoring process by certain sensors. Scaffolds are usually installed in congested and dynamically changing construction environments where worker and materials continuously make traffic on them, and wind blocks and safety nets are often installed for safety purposes. These often decreases visibility – as a result of obstructed views – that is required by vision- or light-of-sight-based sensors (e.g., camera and ultrasonic), which in turn limits the applicability of cameras for scaffolding monitoring. While RFID and ultrasonic can be used to detect nearby objects, their generated data are not suitable for monitoring the micro-behavior of the elements of a scaffold. Although the performance of an inclinometer is not impacted by nearby objects and it presents the state information of scaffolding elements, the angle information itself generated by an inclinometer does not provide sufficient information that is needed to accurately analyze the overall stability of the scaffold. The strain is one of the most critical information about the status of structural elements. Among many sensors discussed above, strain gages can be considered as a reliable solution that can provide necessary information for structural analysis without being impacted by the adverse environmental nature

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Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) of scaffolds. Therefore, this study adopted strain gage sensors as the method of collecting data from a scaffold.

Machine Learning Algorithms for Civil Engineering Applications Machine learning (ML) techniques have often been used in various domains of research where data play a crucial role in understanding a system from which the data are obtained. In civil engineering research, the importance of real-time data collection has been consistently recognized, and consequently, researchers have employed various types of sensors (e.g., laser (Chen et al. 2017b; Golparvar-Fard et al. 2011),video (Gong and Caldas 2011; Jung 2014; Park and Brilakis 2012), radio (Li et al. 2014; Park et al. 2017; Skibniewski and Jang 2007; Soleimanifar et al. 2014), strain gage (Cho et al. 2016, 2017b), audio (Brizuela et al. 2011; Cheng et al. 2016; Cho et al. 2017a), and motion (Kim et al. 2016; Park et al. 2016)) to collect real-time data from construction sites. The abundance of data collected by such sensors allows researchers to learn and extract new findings from data analytics. For example, Cho’s research team at Georgia Tech introduced an unsupervised learning method (Chen et al. 2017b) and supervised learning methods (Chen et al. 2017a; Fang et al. 2016) for various construction objects recognition on point cloud data. Yang et al. (2015) developed an ML-based method of detection of near-miss falls. Their approach leveraged the motion data collected by workers in predicting possible motions that may result in near-miss falls in real time. Fingerprinting as a tracking method—although arguable with respect to effectiveness in construction sites—has been widely explored by researchers (Pradhan et al. 2009; Woo et al. 2011). Jung (2014) proposed a Hidden Markov Model-based algorithm to monitor temporary structures. Although it presented an effort towards automated monitoring of temporary structures, Jung’s proposed approach is still limited because it requires manual inputs of an area to be monitored and thus it only analyzes the selected area. Another drawback is related to difficulty in continuous video-based data collection from temporary structures at a dynamic, complex construction site. While data-driven approaches such as ML have been

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Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) the focus of extensive studies, very little research has explored methods for automated monitoring of scaffolding structures using a data-driven approach instead of conventional analytical methods.

OBJECTIVE AND SCOPE The goal of this research is to develop a data-driven methodology for the monitoring of scaffolding structures by using strain-gage sensors for collecting physical data and an ML algorithm for analyzing the strain data. Capitalizing on recent technological advances, this research proposes a framework for an autonomous scaffolding monitoring system that can be used in both field and office. The proposed project entails three objectives. The first is to implement a wireless strain sensing module for smart autonomous scaffolding monitoring. The strain patterns from each scaffolding column are key indices for estimating the structural condition of a scaffold. The second is to build an FEM model that accurately describes the behavior of a scaffold by an FEM model updating technique that investigates data from the FEM simulation to precisely reflect real structural responses under given loading cases. Therefore, to emulate various scaffolds’ structural conditions and obtain corresponding strain data, randomly generated load cases were applied to the FEM simulation for learning training data sets. The third is to implement an ML algorithm to the proposed framework that enables the distinguishing of categorized scaffolds’ structural conditions (i.e., safe, over-loading, over-turning, and unevenly settled conditions). The scope of this research is limited to the analysis of a one-bay scaffold structure with respect to the relationship between load and strain and the safety condition of the scaffold structure. As a structure becomes larger, it entails more complexity with respect to its behavior in response to loadings. However, regardless of the complexity, for example, whether it is a multi-bay structure or a one-bay structure, the principles of structural analysis remain the same except that it requires more data to account for more responses. Therefore, the processes developed in this research can be equivalently applied to multi-bay scaffold structures, as the theory of autonomous monitoring remains identical regardless of the size of the scaffold; it would only require the deployment of more strain sensors and more data sets for training 9

Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) purposes. Also, this study is limited to automating scaffolding monitoring process by cross-connecting two elements in three sequential processes: 1) modeling FEM simulation, 2) learning data through FEM and 3) implementing a machine-learning algorithm for prediction, which is sufficient to demonstrate the purpose of the study.

FRAMEWORK FOR SMART AUTONOMOUS MONITORING SYSTEM This section presents the proposed framework and detailed methodology for smart scaffolding monitoring. Fig. 1 illustrates the main idea for the proposed method described with three steps. In Step 1, the wireless sensors capture strain data from each column of a scaffolding structure and transfer the data to an FEM model. Then, the FEM model starts optimizing material and geometric parameters of the scaffold by processing model updating until strain responses from the FEM analysis are close to measured values. In Step 2, in order to generate training and testing data sets for an ML algorithm, thousands of loading cases are randomly generated and applied to the FEM model for structural analysis. Structurally analyzed results are classified into four categories as follows: safe, over-loading, over-turning, and unevenly settled conditions. The ML algorithm takes on a training step to regulate and optimize its parameters with the strain data and the corresponding structural conditions until the algorithm enables the performance of a high accuracy estimation. In Step 3, the deployed wireless strain sensors capture microscopic strain data in real time and wirelessly and seamlessly transfer the data to the trained ML algorithm. Then, the ML algorithm conducts a data-driven analysis of the transmitted strain data and uses the result in a decision-making process to identify the status of the scaffolding structure.

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Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted)

Design of Wireless Strain Sensing Module For attaining reliable strain measurement in real time, the research group developed the wireless strain sensing module (Fig. 2). Each of the sensors consists of four components: 1) an Arduino Yun board, internet of things (IoT) open source hardware, which converts an analog signal to a digital signal from strain gages and has a Wi-Fi communication capability; 2) a strain gage board that manages the measured voltage difference by amplification; 3) commercial strain gages that are 350 Ω and their gage factor is 2.18; and 4) 11

Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) an interface shield that provides electrical connections to integrates the three components (Arduino Yun, strain gages, and a strain gage board). The designed wireless strain sensing module requires five volts for operating, and the wireless interrogation distance is 30 m of line-of-sight communication. The range of strain sensing is from –1,000 με to 1,000 με with the resolution of 2 με. The accuracy of the wireless strain sensing module is 1.44με, and the sampling rate is one Hz.

Design of an FEM Model This section presents a real scaffold model for laboratory testing and the corresponding numerical FEM model for machine-learning processing. In the following sections, we 1) describe the dimensions of the scaffold model and a computational FEM modeling technique for handling the boundary conditions of a scaffold under various loading conditions and 2) present FEM design parameters and FEM model updating.

Computational Modeling Technique for a Scaffold Ring-Lock scaffold (Atlantic Pacific Equipment, Inc.) was employed in this research. The dimension of the built scaffolding structure used in the laboratory testing is 84 in. × 62 in. × 152 in. (2,133 mm ×1,575 mm ×3,861 mm) as shown in Fig. 3 and Fig. 4 (a). The scaffold is a one-bay scaffold with two stories that consists of four columns and 24 side frames with a circular pipe section of 5 mm-thickness in diameter. Six

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Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) steel scaffold planks with the dimension of 84 in. × 10 in. ×0.12 in. (2,133 mm ×254 mm ×3 mm) are placed on each floor (Fig. 4 (a)).

Fig. 4 presents a photo of the scaffold (a) and its numerical FEM model (b and c). For modeling the scaffold, the research group used a commercial FEM software package, COMSOL Multiphysics (COMSOL Multiphysics 2012). As shown in Fig. 4 (b), the frame of the scaffold is modeled by beam-column elements, and shell elements are used to model the scaffold planks. Fig. 4 (c) shows an example of a deformed shape as a result of the structural analysis. Because of the 3D analysis, each node has six degrees of freedom.

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Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted)

As shown in Fig. 4 (a), the columns of the scaffold stand on the wooden foundations that are not the same as conventional structural boundaries such as hinge and fixed boundaries. A common practice is to not attach the columns of a scaffold to the foundations because of convenience. In the perspective of structural analysis, these boundaries cause instability because they are considered to be a free boundary condition. Therefore, this study suggests a new methodology to analyze scaffold structures while compensating for the boundary conditions of a scaffold. If light loadings are applied to a scaffold, each boundary has three reactions as a pin connection. In the case of high lateral loads or over-turning moments that cause slippage or uneven settlement of a scaffold, the boundary conditions are transformed as follows: -

Case I: when later forces (𝑅𝑥 or 𝑅𝑦 ) are bigger than the vertical reaction force (𝑅𝑧 ) multiplied by a friction coefficient (μ), a hinge is transformed to a roller boundary with the lateral reaction force of μ𝑅𝑧 ; the research team uses the value of a friction coefficient obtained by experimental results. The FEM simulation re-analyzes the structure with the transformed boundaries as shown in Fig. 5 (a). 14

Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) -

Case II: if one or two columns have tension or a zero force (𝑅𝑧 ≤ 0), the associated columns are unevenly settled or a full structure is bounded to an over-turning condition. Because such columns cannot hold any reactions, the hinge boundary conditions of the columns are transformed to a free condition. The FEM simulation re-analyzes the structure with the transformed boundaries as shown in Fig. 5 (b).

Model Updating An FEM model usually uses nominal values of material properties and geometric parameters. Although necessary for modeling, these factors lead to a discrepancy between numerical analysis and experimental measurements. To resolve this discrepancy, a model updating technique was adopted to extract more precise values of the parameters from experimental data. Upon updating the material parameter and geometric parameter in the model, the FEM model better reflects the physical reality of the scaffold as compared with the initial model, which relies on nominal parameter values. The optimization problem for model updating is formulated as: 15

Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted)

𝑚

minimize ∑[StrainExp (𝑷𝒊 ) − StrainFEM (𝑷𝒊 |𝐱)] 𝐱

2

(1)

𝑖=1

subject to 𝐱L ≤ 𝐱 ≤ 𝐱U where 𝑚 is the number of strain measurements; 𝑖 is the step of the measurement; 𝑷𝒊 is a loading vector at the 𝑖 𝑡ℎ measurement; 𝐱 is the updating parameter of a 2D array that consists of Young’s Modulus E and the thickness of a structural pipe t (𝐱 = [𝐸, 𝑡]); StrainExp (𝑷) is a vector function to generate experimental strain values against loading 𝑷𝒊 ; StrainFEM (𝑷𝒊 |𝐱) is a vector function to generate simulated strain values with loading 𝑷𝒊 and the updating parameter 𝐱; 𝐱L and 𝐱U are (element-wise) lower and upper bounds of the updating vector parameter 𝐱. Initial parameters of vector 𝐱0 are chosen as: 𝐱0 = [200×109 Pa, 3 mm]

(2)

the lower and upper bounds are set with the 10% difference from 𝐱0 : 𝐱L = [200×109 Pa, 3mm] × 0.9

(3)

𝐱U = [200×109 Pa, 3 mm] × 1.1

(4)

In the experiment, the applied loading ranges from 100 kgf (kg-force) to 400 kg-force with a step of 100 kgf. Strain values are measured from the four columns of the scaffold. The FEM model simulates structural analysis with the identical loading condition to obtain analytical strain values. As a global optimization solver, MultiStart in MATLAB optimization toolbox was adopted to solve the model-updating problem. Using MultiStart, 1,000 trial starting points for the updating variables 𝐱 = [𝐸, 𝑡]) for each of the parameters are randomly generated. Starting from each of 1,000 points, the lsqnonlin (nonlinear least-squares solver) with the “trust-region-reflective” algorithm finds a local optimum around the point. Among local optimums, the most optimized value is:

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Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) 𝐱 ∗ = [198.12×109 Pa, 2.612 mm]

(5)

Fig. 6 presents the model updating result. The Y-axis indicates average strain values from the four columns. Fig. 6 reveals an intriguing finding that the model updated by the proposed method shows more accurate behaviors for the tested scaffold, compared with the initial model with nominal parameters.

Machine Learning for Smart Temporary Structures This section describes the applied ML algorithm for real-time monitoring of the scaffolding structure. We adopted the support vector machine (SVM) (Cortes and Vapnik 1995) as an ML algorithm because the method effectively solves nonlinear classification problems by introducing a kernel function (Anguita et al. 2010; Auria and Moro 2008). This section 1) introduces the brief concept of SVM, 2) explains nonlinear hyperplanes used for classification of the result, 3) defines the four structural conditions of the scaffold used in this research and generates the corresponding training and testing data sets for the ML process, and

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Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) 4) presents the one-vs-all (OVA) classification technique (Vapnik 2013) using binary classifications, the ML implementation, and prediction results for parameter learning.

Support Vector Machine (SVM) The SVM is a binary classification that separates elements with two groups and formulates a plane as: (6)

𝝎T 𝒙(𝑖) + 𝑏 where 𝑥 (𝑖) is the feature vector at the 𝑖 th order; 𝝎 and 𝑏 are updating parameters.

A classifier function with classification labels of y (𝑖) ∈ {1, −1} is described with the following equation: (7)

ℎ(𝑥) = 𝑔(𝝎T 𝒙(𝑖) + 𝑏) if 𝝎𝑇 𝒙(𝑖) + 𝒃 ≥ 0, 𝑦 (𝑖) = 𝑔(𝝎𝑇 𝒙(𝑖) + 𝑏) = 1 Otherwise, 𝑦 (𝑖) = 𝑔(𝝎𝑇 𝒙(𝑖) + 𝑏) = −1

(8) (9) (10)

r (𝑖) = y (𝑖) (𝝎T 𝒙(𝑖) + 𝑏)

where 𝑔(·) is a sigmoid function; r (𝑖) is the functional margin at the 𝑖 th order. Because the large functional margin represents a confident and correct prediction, the optimized formulation of the fictional margin is written as: maximize 𝑟,𝝎,b

𝑟 ‖𝝎‖2

(11)

subject to y (𝑖) (𝝎T 𝒙(𝑖) + 𝑏) ≥ 𝑟,

𝑖 = 1,2 … , 𝑙

where 𝑙 is the total number of training data.

Nonlinear Classification For nonlinear classification problems, several supervised ML algorithms require feature mapping, which usually generates quadratic or high polynomial forms. In other words, instead of using original feature 𝒙, 18

Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) the output vectors of a mapping function 𝜙(𝒙) are used as new feature vectors. For example, if feature vector 𝒙 is defined by three components (𝒙 = [𝒙𝟏 , 𝒙𝟐 , 𝒙𝟑 ]𝐓 ), the output vectors from the mapping function 𝜙(𝒙) typically have more components (e.g., 𝜙(𝒙) = [𝒙𝟏 𝒙𝟏 , 𝒙𝟏 𝒙𝟐 , 𝒙𝟏 𝒙𝟑 , 𝒙𝟐 𝒙𝟐 , 𝒙𝟐 𝒙𝟑, 𝒙𝟑 𝒙𝟑, ]T ). However, this method is not computationally efficient. Furthermore, it is difficult to determine the number of dimensions for effectively solving nonlinear classification problems. To address this issue, the Gaussian kernel is adopted in SVM as a mapping function and is formulated as: 2

(𝑖)

(𝑗)

𝐾(𝒙 , 𝒙

) = exp (−

‖𝒙(𝑖) − 𝒙(𝑗) ‖2 2𝜎 2

)

(12)

where 𝒙(𝑖) is the 𝑖 th feacture vector; 𝒙(𝑗) is the 𝑗 th feacture vector; 𝜎 is stand deviation. The Gaussian kernel provides the measurement of similarity between 𝒙(𝑖) and 𝒙(𝑗) . If two vectors are close, the value of kernel will be 1.0. On the contrary, if two vectors are far from each other, the value will be zero. Therefore, the final form of the hyperplane is as: 𝝎T 𝒙(𝑖) + 𝑏 = 𝜔1 𝐾(𝒙(𝑖) , 𝒙(1) ) + 𝜔2 𝐾(𝒙(𝑖) , 𝒙(2) ) + ⋯ 𝜔𝑙 𝐾(𝒙(𝑖) , 𝒙(𝑙) ) + 𝑏

(13)

Generating Training and Testing data This section presents the training and testing procedure for the ML algorithm, which is a core step in the proposed framework to decide on the structural condition of a scaffold. To distinguish structural conditions by the SVM algorithm, four categorized cases are identified as follows: 1. Safe condition: The scaffold is structurally robust and safe. The tested scaffold model supports up to 1,000 kg-force according to the OHSA standard and the average strain level at the maximum support is –75 με. Strain levels of the four columns must be higher than –75 με to remain in a safe condition.

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Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) 2. Over-loading condition: Applied loads on the scaffold over its maximum capability may cause local and global structural failure. One or more columns’ strain levels lower than –75με indicates the over-loading condition. 3. Unevenly settled condition: uneven settlement causes differences in ground levels in the columns of the scaffold. In this case, the foundation does not properly support one or two columns; such columns are indicated by a zero-force or zero-strain member. Since the other two or three columns are responsible for supporting the applied load on the scaffold, loads lower than 1,000 kgf may cause a collapse. 4. Over-turning condition: Mainly lateral loads (wind load) cause an over-turning structural collapse. The sign of an over-turning collapse is that the strain from two columns are close to zero and the other two columns have high compressive strains. To generate training and testing data sets of the SVM, a random generation algorithm was used to create thousands of load cases that are the input data for the FEM model designed in the Design of an FEM model section. Then, the FEM simulation analyzed each load case and classified its structural condition with the corresponding strain vectors from the four columns of the scaffold. The number of the training set for each condition is 300; the number of the validation set is 50. Therefore, the total prepared data set for training and validation are 1,200 and 200, respectively.

SVM Implementation Since the SVM is a binary classifier, the one-vs-all (OVA) classification technique (Vapnik 2013) was adopted in this research. The formulation of OVA-SVM generates the same number of classifiers as classes (conditions in this study). Each different classifier involves only a single training class. Fig. 7 presents an example of OVA-SVM including four classes. The classifier A accepts only A-class data and rejects the other class data. In the same manner, other classifiers accept only their classes. In other words, OVA-SVM requires unanimity from all classifiers when it decides on the data class. Since the four types of structural 20

Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) conditions are defined in the Support Vector Machine section, the OVA-SVM generates four classifiers and trains them with 1,200 strain vectors. To improve the computing speed, the Gaussian kernel was adopted in SVM as a mapping function. By trial and error, the outlier fraction was set to 0.05. After ML training, the validation procedure was conducted with 200 strain vectors to estimate the accuracy of the trained ML model. The correct prediction rate for each classifier can be calculated by the following equation: Prediction Rate (PR) =

TP + TN × 100 TP + TN + FP + FN

(14)

where TP is true positive; TN is true negative; FP is false positive; FN is false negative.

Table 1 summarizes the accuracy of the trained OVA-SVM model. The four classifiers identify structural conditions with the high prediction rates of which safe, over-loading, unevenly settled, and over-turning

21

Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) conditions are 98.5%, 98.0%, 96.0%, and 96.5%, respectively, which verifies that the classifiers can accurately describe the safety condition of the scaffold quantitatively. Therefore, the trained OVA-SVM model was able to successfully fit to distinguish the four structural conditions, and the research team can proceed with real-world validation on the actual scaffold.

Table 1. Prediction results Simulated structural conditions Safe Over-loading Unevenly settled Over-turning

Safe TP(47) FP(3) TN(46) FN(4) TN(50) FN(0) TN(50) FN(0)

Classifier type Unevenly Over-loading settled TN(50) TN(50) FN(0) FN(0) TP(50) TN(50) FP(0) FN(0) TN(50) TP(46) FN(0) FP(4) TN(50) TP(47) FN(0) FP(3)

Over-turning TN(50) FN(0) TN(50) FN(0) TN(46) FN(4) TP(46) FP(4)

Prediction Rate 98.5% 98.0% 96.0% 96.5%

If a classifier accepts a data point, it counts as one. Otherwise, it counts as zero. Calculation example (Unevenly settled):

TP(46) + TN(50 + 50 + 46) × 100 = 96.0% TP(47) + TN(50 + 50 + 46) + FP(4) + FN(4 + 0 + 0)

Experimental Validation To demonstrate the proposed method, we conducted extensive experimental validations. Fig. 8 (a) presents experimental setups for emulating two of the four cases (i.e., unevenly settled and over-turning conditions). For real-time strain measurement from the four columns of the scaffold, strain gages (CFLA-3-350) were installed and connected to the wireless module developed in the Design of Wireless Strain Sensing Module section. Then, the strain data were transmitted to the analyzer where the strain data were fed into the trained OVA-SVM model for deciding on the structural condition of the scaffold.

22

Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted)

As shown in Fig. 8 (a), the forklift generated unevenly settled and over-turning conditions by lifting one or two columns. For safety reasons, the height of lifting is limited to two inches; however, this small displacement is enough to emulate unevenly settled and over-turning conditions. For validating the reliability of the purposed system, we repeated each case 50 times, including changing the forklift position to lift different columns. Fig. 8 (b) presents an example of times series readings when the structural condition changes from “safe” to “over-turning.” Strain calibration for the four columns is conducted. Then, the strain data are collected every second. Due to the 2 με resolution of the developed wireless strain sensing unit, the strain values fluctuate within a difference of 2 με in the safe condition. After nine seconds, the forklift introduces the over-tuning condition to the scaffold by pivoting the scaffold about the long edge (line connecting columns 2 and 3). Columns 2 and 3 to support more portions of self-weight. As a result, Columns 2 and 3 support more weight, so their strain levels drop to approximately –14 με (compression).

23

Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) On the contrary, the strain levels of Column 1 and 4 increase to 6 με; because Columns 1 and 4 initially support approximately half of the self-weight when the strain has been calibrated, the release of the compressive load result in positive strains in Columns 1 and 4. Fig. 8 (c) shows another example that the structural condition changes from “safe” to “unevenly settled.” From zero to ten seconds, the scaffold is classified as safe. After ten seconds, the strain level of Column 1 increases by approximately 6 με, which implies that the loading in Column 1 has been released. The strain levels from Column 2 and 4 maintain similar levels as the safe condition or slightly decrease (they fluctuate from -2 με to 0 με). Since more loads are subjected to Column 3 as a result of lifting Column 1, the strain level from Column 3 decreases, which implies that Column 3 holds more compressive loading. As shown in Fig.3, the span size of the scaffold is 84 in. (width) × 62 in (depth), and the maximum loading capacity can be calculated based on OHSA standards specifying the maximum weight exerted on a heavy-duty scaffolding not to exceed 75 pounds per square foot (Occupational Safety and Health Administration (OSHA) 2017b): 7 ft. (84 in. ) × 5.16 ft. (62in. ) × 75

lb = 2,709lb = 1,229kgf ft 2

(15)

However, we limited the loading size up to 400 kgf for safety reasons. We recruited six subjects and measured their weights prior to the experiment (Fig. 9). The subjects stepped on the scaffold one by one until the weight reached 400kgf. When the total loads approached the 400kgf, the average strain level was close to –75με. At the same time, the trained OVA-SVM model detected the over-loading condition. We also simulated 50 cases of over-loading condition for repeatability. Table 2 summarizes the experimental results for the validation of the proposed method. For all three emulated structural conditions, each trained OVA-SVM classifier successfully identified, on average of 97.66 % accuracy, the case given strain data sets and rejected the other cases that are not relevant. Therefore, the proposed method successfully detects structural conditions, and can therefore serve as a real-time scaffolding monitoring system.

24

Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted)

Table 2. Detection results Emulated structural conditions Over-loading Unevenly settled Over-turning

Classifier type Safe

Over-loading

Unevenly settled

Over-turning

TN(50) FN(0) TN(50) FN(0) TN(50) FN(0)

TP(50) FP(0) TN(50) FN(0) TN(50) FN(0)

TN(50) FN(0) TP(47) FP(3) TP(48) FP(2)

TN(50) FN(0) TN(46) FN(4) TP(45) FP(5)

Prediction Rate 100.0% 96.5% 96.5%

SUMMARY AND DISCUSSION Although a scaffold is one of the essential elements required during construction, the current approach to monitoring scaffolding structures is ineffective, as it is only a visual-based and labor-intensive inspection

25

Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) conducted by a knowledgeable, experienced individual. In addition, previously developed research for monitoring temporary structures are inadequate to be an integral solution for real-time monitoring, including analyzing the structural condition of a scaffold. However, this study proposed the framework for a smart autonomous monitoring system for scaffolding structures to provide an early warning of a structural collapse, via data-driven decision-making. In the developed framework, various components (e.g., strain data, finite element model (FEM)-based structural analysis, machine learning, and an actual scaffold) are cross-connected to overcome the limitations discussed with past research by automating the monitoring of a scaffold via a data-driven scaffolding monitoring approach. Such a warning system, if implemented in the field, offers great potential to prevent disastrous structural collapses that have a potentially detrimental effect and liability on construction workers’ lives. The advantages of such a monitoring method under the proposed framework are its capabilities, which should facilitate the continuous monitoring of scaffolds deployed over a site without requiring human resources. We developed the wireless strain sensing module using handy open-source hardware, proposed the new simulation technique that transforms traditional structural boundary conditions to compensate for unstable connections between columns and foundations of a scaffold, and demonstrated the feasibility and effectiveness of the automated method of monitoring scaffolds–detecting scaffolds structural conditions by processing only strain data with the aid of the ML algorithm (OVA-SVM). From the extensive experimental validation, the research proved the performance of the proposed algorithm with respect to its capability of detecting structurally unsafe conditions of a scaffold in real time. By developing the wireless scaffolding monitoring system using strain gage sensors, we eliminated extra processes (e.g., installation for monitoring of specific locations (especially for camera), and recoordination of the system to accommodate changes in the setup of the scaffold, and wired sensor installations) required during implementation conducted in previous research. Despite this advancement, for practical implementation of the proposed monitoring approach, a certain amount of manual effort is still required for the preparation of the system prior to implementation. During the construction planning stage, 26

Cho, C., Kim, K., Park, J., Cho, Y. K., (2018) “Data-Driven Monitoring System for Preventing the Collapse of Scaffolding Structures,” Journal of Construction Engineering and Management. (March, 2018, Accepted) construction planners and scaffolding vendors should incorporate detailed scaffolding objects into BIM, which is technically possible (SCIA Nemetschek 2015; Smart Scaffolder 2017). Despite the achievements of this study, there are limitations to be addressed in future research. The developed monitoring system needs precise scaffolding models as an input to the system. However, in current practices, detailed designs of scaffolds are rarely created before they are installed. Even with wide acceptance of BIM for construction management, it is not common to have temporary structure models available along with the main building models. In addition, a calibration technique should be developed to address environmental issues (humidity, temperature, etc.) that may affect system accuracy. As a resolution, future research may use a dummy strain gage along with every strain gage such that the dummy strain gage does not experience mechanical strain but experiences only strain caused by environmental effects (e.g., humidity and temperature). The results of the tests depended on multiple factors including the number of test categories, the number of cases in each category, the sensitivity of the cases, and the test parameters. Complex systems typically introduce more complexity in these factors. Because of this additional complexity, although the procedures developed in this study can be applied to complex structures, researchers should collect more structural response data to use as training and validation data sets in ML algorithms and verify the performance of advanced ML approaches for a wider range of categories and test cases.

Data Availability Statement

All data generated or analyzed during the study are available from the corresponding author by request.

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