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Automating and Optimizing Spatial Data Processing Workflows for Civil Infrastructure Inspection Pingbo TANG1, Anu PRADHAN2 1
Civil and Construction Engineering Department, Western Michigan University, 4601 Campus Drive, Kalamazoo, MI 49008-5316; PH (269) 276-3203; FAX (269) 276-3211; Email:
[email protected] 2 Department of Civil, Architectural and Environmental Engineering, Alumni Engineering Labs Room 273-E, Drexel University, Philadelphia, PA 19104; PH (215) 571-3540; FAX (215) 895-1363; Email:
[email protected] ABSTRACT Limited resources are available to timely inspect and maintain the aging civil infrastructure across the United States. Reality capturing technologies, such as laser scanning, is replacing visual inspection and manual surveying for improved data qualities and reduced resource requirements, while bringing challenges of timely processing terabytes of spatial data. Even using state-of-art 3D reverse engineering environments, inspectors need to manually select data processing algorithms, compose and configure data processing workflows, and verify the correctness of these workflows. Such manual design and execution of spatial data processing workflows are tedious, and result in sub-optimal workflows that do not fully utilize time and resources for producing accurate and detailed spatial information needed by domain applications. This paper proposes a computational framework that will assist in the infrastructure inspection process through streamlined spatial data processing workflow generation, execution, and optimization. Based on previous studies on spatial information query, spatial data processing, and building information modeling (BIM), the authors are exploring the feasibility of automatically generating and optimizing spatial data processing workflows based on formalized representations of these workflows. Keywords: Civil Infrastructure Inspection; Scientific Workflow; Laser Scanning; Workflow Generation; Automated Planning INTRODUCTION The aging civil infrastructure systems in the United States (U.S.) have become a severe national concern across economic development, public safety and social welfare (ASCE 2009). The necessity of effective inspection and proactive management of U.S. civil infrastructures poses pressing challenges in terms of capturing, processing and interpreting their spatial information within stringent resource limits. About $2.2 trillion are needed across the U.S. for effective civil infrastructure management, while the resource and budget for infrastructure
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management are limited (ASCE 2009). As visual inspections are tedious and labor intensive (Tang and Akinci 2011), state DOTs across the U.S. are seeking methods for improving the efficiency of spatial data collection, processing, and management while reducing the resource consumptions (Brilakis et al. 2011). Reality capturing technology (e.g., laser scanning, photogrammetry) brings great opportunities by collecting 3D point clouds that digitize detailed spatial information of civil infrastructure systems in shorter durations (Golparvar-Fard et al. 2011; Jaselskis et al. 2005). However, there are significant challenges related to processing large amounts of 3D point clouds (Tang and Akinci 2011). First, manually processing large amounts of point clouds is tedious and impeding the effective usage of these technologies for civil infrastructure inspection (Tang and Akinci 2011). For example, some laser scanners are able to collect dense 3D point clouds capturing detailed bridge features as small as 1cm from 50m within minutes, but manually processing such point clouds and extracting useful geometric measurements can take weeks (Tang and Akinci 2011). Since the number of geometric measurements needed for a comprehensive assessment of a bridge is significant, manually obtaining these measurements for thousands of bridges across the U.S. is impractical (Tang and Akinci 2011). The manual spatial data processing and analysis has, therefore, become a serious bottleneck. The second challenge of using reality capturing technology for civil infrastructure inspection is the lack of quantitative assessments of the quality of the acquired spatial information. Various measurements (e.g., lengths, distances, areas) can be extracted through spatial data processing workflows, which are sequences of data processing algorithms linked by input-output connections called data links. These workflows have different performances. For example, to extract the cross-sectional areas of rectangular piers, inspectors will measure the lengths and widths of the cross-sections and calculate their areas; alternatively, they could use the convex-hull algorithm to derive polygons representing these cross-sections, and thus obtain their areas. The former cannot produce areas as accurately as the latter when the piers are not exactly rectangular. In addition, workflows can have different network structures (node as algorithms, data links as edges), and each node in that network can have different algorithmic implementations. For different 3D point clouds, the best workflow setting (network configurations, algorithm selections, and parameter values) will vary (Tang and Akinci 2011). It is cumbersome to consider all these factors manually while designing data processing workflows. The third challenge is the difficulty in identifying and evaluating all data processing workflows capable of generating the desired measurements using given point clouds. Given the desired measurements, there can be multiple workflows capable of extracting them. These workflows may have different performances in terms of measurement errors and the computational efficiency (Tang and Akinci 2011). In current practice, inspectors decide which procedure to follow and which algorithm to use based on their experiences (Tang and Akinci 2011). Such manual workflow design and execution limit the capabilities of inspectors in terms of comprehensively identifying and evaluating all workflows that can derive the needed
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info ormation. Without W an approach a forr automaticaally generatting and evaaluating all worrkflows giv ven the desirred measureements, des ign and exeecution of sspatial data pro ocessing worrkflows will be ad-hoc, in ncomplete, aand sub-optiimal. This pap per proposess a computattional frameework for adddressing thee challenges desscribed abov ve. Figure 1 shows an ov verview of thhis framewoork, which iss composed of three t compo onents: 1) Wo orkflow form malization, w which focusees on developping formal representations of spatial queries of informationn models oof infrastrucctures (e.g., Briidge Inform mation Modeels, or BrIM M) and datta processinng workflow ws, so that com mputers can automaticallly constructt and execuute workflow ws on as-is iinformation models of infraastructures augmented a with w 3D poinnt clouds (e.g., an as-is BrIM with 3D data points associated with w each brridge elemennt); 2) Workf kflow generaation, which focuses on dev veloping approaches for automaticallly generatinng large num mber of data pro ocessing wo orkflows caapable of generating g ddesired spattial measurrements; 3) Workflow optim mization, which w focusees on charaacterizing w workflows inn terms of measurement errors e and co omputational complexitiies, and devveloping appproaches for automatically optimizing o th he parameterrs of these w workflows too achieve peerformances satiisfying the in nformation requirements r s (e.g., accurracy) within time limits.
Fig gure 1 Overview of a computatiional frameework for aautomatingg workflow gen neration and d optimizatiion RE ELATED ST TUDIES Previou us studies in i the dom mains of spaatial inform mation queryy, building info ormation modeling m (BIIM), and in nfrastructuree sensing sshow the ppotential of automating spaatial data pro ocessing wo orkflows. Asssuming thaat a 3D BIM M is aligned mputationallly efficient object recognition algoorithms can witth 3D point clouds, com recognize whicch points beelongs to wh hich objects (Bosché 20010); in adddition, some algorithms can n automaticaally recogniize spatial relationshipps among oobjects, and or querying BIM and sppatial data asssociated witth this BIM utillize such relaationships fo (Bo orrmann and d Rank 2009;; Tang et al. 2010). Multiple research projects p dev veloped varioous 3D moddeling and sspatial data pro ocessing algo orithms (Tan ng et al. 2010 0). These stuudies show thhat an as-is B BIM can be con nstructed fro om 3D pointt clouds, and d can be us ed as an intterface to quuery spatial data. The prop posed reseaarch plans to t build on a BIM-bassed spatial data query mechanism deetailed in (T Tang and Akinci A 2011), and devvelop data processing worrkflows thatt can autom matically reco ognize data points relevvant to a sppatial query (e.g g., all points belonging to t piers for the t query “crross-section areas of all piers”) and exeecute a sequeence of 3D data d processsing algorithm ms on thesee recognized data points
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for answering that query. Based on previous studies, the research presented in this paper proposes an approach for automatically generating and optimizing spatial data processing workflows based on formalized representations of such workflows. WORKFLOW FORMALIZATION Workflow formalization aims at developing formal representations of spatial data processing workflows and automation mechanisms for enabling automated construction, execution and extension of workflows. Table 1 shows two aspects of workflow formalization: a) workflow representation, and b) mechanisms for workflow design and execution. Workflow representation focuses on identifying elements of an inspection process and developing syntaxes for representing these elements. Overall, a query guides the design of a data processing workflow. An inspection starts with defining the targeted geometric information (Query), then moves on to identify data processing algorithms (Operation) that are needed to answer the query, and then construct a sequence of algorithms (Workflow) connected by input-output relationships transferring the processed data (Data). Spatial queries express the information needs of inspectors. In the domain of bridge inspection, our analysis of the coding guide of the National Bridge Inventory (NBI) program indicates that among 116 NBI data items, 27 are spatial information queries. These queries can be classified into two major categories: Attribute and Relationship Measurements. Attribute measurements can be further categorized as “Location,” “Object Dimension,” “Area;” Relationship measurements can be further categorized as “Space Clearance,” “Distance,” “Angle,” “Deviation from a Reference.” Such classification of spatial queries can be extended to a variety of inspection scenarios in the civil engineering domain, as detailed in (Tang and Akinci 2011). Different categories of spatial queries need different types of data processing operations and workflows to generate the needed measurements. Our previous studies indicate that various data processing operations can be categorized into nine types (Tang and Akinci 2011): 1) Object recognition; 2) Geometric primitive extraction; 3) Sampling points on geometric primitives; 4) Grouping objects; 5) Extracting spatial relationships; 6) Observation of geometric attributes; 7) Identification of objects with user-specified attribute values; 8) Identification of objects based on their spatial relationships with a reference object; 9) Analysis of the accuracy of geometric primitives. Object-Oriented modeling of these nine types of operations result in nine generic “Operation” classes, and objects of these classes can be combined into different “Workflow” objects, which depict data processing procedures for answering spatial queries. Generally, spatial queries in the same category share similar sequences of operation types. For example, queries of category “Area” can be answered by a workflow composed of operations capable of extracting cross-sections of objects and derive cross-section areas; no matter whether the measured object is a deck or a pier. A generic workflow, therefore, can be applied to a category of spatial queries as a “workflow template”. Previous studies show that all NBI spatial queries can be answered by workflows composed of the nine types operations, and each category of NBI spatial queries can be answered by workflows based on a workflow template (Tang and Akinci 2011). Currently, we are extending these bridge related
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queries and workflows to general civil infrastructures. In Figure 2, “Extract surface-surface distances” is an “Operation” class encapsulating an algorithm for deriving the distances between two surfaces. The inputs of it are the targeted surfaces, and the outputs are the sampled distances. This operation should be configured by setting its parameters and constraints. For example, this operation can sample distances according to selected sampling constraints (either randomly sample for N times, or sample on grids with user-defined grid sizes) along the vertical direction (a constraint on measurement direction). Table 1 Aspects of Workflow Formalization Elements Definitions A specification of targeted Query Workflow geometric attributes of civil Representation infrastructure components Data Operation
Workflow
Mechanisms of Extension Workflow Design and Execution Construction
Configuration
Control
or spatial relationships among components Data items that can be inputs and outputs of data processing algorithms Objects encapsulating data processing algorithms in an Object-Oriented method A sequence of spatial data processing operations interconnected by input-output relationships Mechanisms for enabling automated definition of new operations or workflows based on existing operations or workflows Mechanisms for enabling automated identifications of correct input-output connections among operations to form workflows Mechanisms for enabling automated identifications of correct settings of operations and workflows for ensuring successful executions of workflows Mechanisms of executing workflows handling various events during the workflow executions
Examples Areas of cross-sections of piers; Distances between the bottom of a bridge’s superstructure and the highway below the bridge Lines; Point Clouds; Bridge Superstructure Objects Recognize bridge superstructure from point clouds; Extracting lines from point clouds A workflow taking point cloud of a bridge as inputs for generating the areas of cross-sections of all piers Define operation “RecognizeColumn” based on operation “RecognizeComponent” Given available operations and spatial data sources, identify and validate workflows capable of generating the areas of cross-sections of all piers Given a workflow capable of generating the areas of cross-sections of all piers, automatically identify the valid parameter value of all operations Exception handling mechanisms of workflow execution, mechanism of changing measurement sampling methods according to abnormal measurement results
The mechanisms of workflow design and execution include four elements: 1) Extension; 2) Construction; 3) Configuration; and 4) Control. Table 1 lists these mechanisms and their definitions. The first three elements have been discussed in
Construction Research Congress 2012 © ASCE 2012
detail in (Tang g and Akincci 2011). Th he last one iis a new cooncept exploored in this reseearch: Contrrol Mechaniisms can guiide the event nt/exception handling proocess while exeecuting work kflows. Such h event hand dling can ennable adaptivve workflow w parameter adju ustments forr improved efficiency e of data proceessing. For eexample, in Figure 2, if the inspector observes o smaall clearancees gathering around a pllace, they m may want to exp plore that reegion in dettail through re-executingg the workfflow with smaller grid sizees in that region. Manually trying multiple m gridd sizes and defining thee regions of inteerests is tediious. Contro ol mechanism ms can modeel the know wledge about such event detection and exception e han ndling to en nable adaptivve workflow w parameter aadjustments and d re-executio ons according to anomaliies in the meeasurement rresults generrated so far. Analysiis of Table 1 highligh hts two elem ments of w workflow foormalization req quiring furtheer investigattions. First, limited studdies focused on represennting spatial queeries in the domain off civil infraastructure m management. Based on the spatial info ormation representation ns developed d in the doomains of sspatial queryy language (Bo orrmann and d Rank 200 09) and dataa fusion (Prradhan and Akinci 20111), we are dev veloping exttensible Objject-Orienteed representtations of sppatial queriies of civil infrrastructure inspectors. Second, liimited studdies are exxploring thee “Control Meechanisms” of o spatial datta processing workflowss for civil innfrastructure inspection, while studies in i the domaain of Geosp patial Data Processing shows the ppotential of adaaptive spatiaal data proceessing (Yang g et al. 20088). Currentlyy, we are exxploring the eveent-condition n-action (ECA) paradiigm (Almeeida et al. 2007) andd dynamic con nstruction operation o viisualization methods ((Kamat andd Martinez 2005) for dev veloping adaaptive workfl flow configurration and exxecution conntrol mechannisms.
Fig gure 2 (leftt): A Work kflow for Extracting tthe Verticall Clearancees under a Briidge; (right)) Vertical Clearances Generated G (C Color-coded d by Distancce Values) WO ORKFLOW W GENERA ATION Workflo ow generatio on can be modeled m as a planning prroblem, whicch needs to iden ntify a set of o data proccessing algo orithms in a particular ssequence baased on the inittial state (raw w 3D data) and goal staate (requiredd measurem ments). This is akin to a path h planning problem p wh here the currrent locationn (i.e., initial state) and destination location (i.e., goal g state) are a known but b a set of operations to reach thee goal state from m the initiall state is unk known. To address a this problem, w we are investtigating two types of plann ning algoritthms: (a) domain-indep d pendent annd (b) domaain-specific algorithms. The differencee between th hese two is based on thhe fact that whether an ge (e.g., heuuristic rules) of problem m solving to algorithm leverrages domaiin knowledg gen nerate work kflow(s). Do omain know wledge helpss to reducee the searchh space of
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worrkflows therreby improvees the compu utational effficiency. How wever, somee workflows may y not be cap ptured in a kn nowledge baase, and thuss be missed bby such algoorithms. The Domain-Indepeendent Algorrithm explorred in this reesearch is based on the GraaphPlan algo orithm (Avrrim L. Blum m and Merricck L. Furst 1997). Thiss algorithm emp ploys two stteps to geneerate a workflow: 1) graaph expansioon, and 2) pplan search. Thee graph expaansion step expands e the graph data structure. T The graph daata structure prim marily consiists of two levels, which h are propossition (state)) and action (operation) levels. The leveels alternate between proposition annd action levvels: a propoosition level con ntains propo osition nodess, and an acction level ccontains a sset of operaation nodes. Eacch propositio on node keep ps track of the t predecesssor and succcessor operaation nodes. Sim milarly, each h operation n node keep ps track off the predeecessor andd successor pro oposition nod des. During the graph expansion, e thhe algorithm m will checkk for mutual excclusions (co onflicts) and d propagatee these muutual exclussions relatioons among pro oposition nod des. Two prroposition no odes at a givven proposiition level arre mutually excclusive if no valid workfllow could make m both proopositions trrue. Once a graph structure is creatted, the algoorithm will uuse backwarrd search to find d a valid workflow. w The T backwarrd search sttarts with tthe goal prooposition(s) speecifying the needed meaasurements in i the last proposition leevel. A grapph structure can n contain larrge number of paths deepicting posssible workfflows. Thuss, backward seaarch can tak ke a consideerable amou unt of compputational tim me. In suchh situations, mem morization can help to o speed up the computaation duringg the backw ward search (Av vrim L. Blum B and Merrick L. L Furst 11997). The advantagees of this dom main-indepen ndent algoriithm include: 1) it does nnot need to eencode domaain-specific (e.g g., inspectio on process) knowledge to perform m graph exppansion andd backward seaarch, 2) it is i theoreticaally guaranteeed to findd all possiblle workflow ws that can gen nerate the desired measu urements (com mpleteness) . The Do omain-Specif ific Algorith hm exploreed in this research is based on Hierarchical H l Task Nettwork (HTN N) planner (Pradhan ( annd Akinci 20011). An HT TN planner starts s with ann abstract pllan, which cconsists of a set s of comppound taskss that captuure domain knowledge k about spaatial data processing workflows. w S Specifically,, generic woorkflows for answering a caategories of spatial querries serve as abstract a planns for the H HTN algoritthm. These generic g woorkflows arre designedd by civil infrastructur i re inspectoors and hhave been validated v inn the previoous studies (Tang and Akinci A 20111). Figure 3((a) shows aan example. This T workfloow containss a compounnd task that can c be decom mposed into simpler taskks: “Extract Fig gure 3 Decom mposition of o a Surface-Surf S face Disstances” can be Compound Ta ask decomposed d d into: (a) S Sample Surfface Points and d (b) Extracct Distances at Sampled d Points. Inn a HTN, eeach compouund task is recu ursively deccomposed into i simplerr tasks untiil reaching primitive ttasks. Such deccomposition is done with the help p of methodds, which aare recipes on how to
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decompose a task. We are currently conducting data processing experiments and interviewing inspectors for developing recipes for civil infrastructure inspection. Compared to domain-independent algorithms, the HTN algorithm leverages domain knowledge (e.g., heuristic rules or templates) to generate workflows with relatively lower computational complexities. However, it is not guaranteed to find all possible workflows given the desired measurements, and rely on the completeness of its domain knowledge base (Pradhan and Akinci 2011). In this research, we are evaluating the performances of both domain-specific and domain-independent algorithms, and quantifying the trade-off between computational efficiency and the completeness of workflow generation. In addition, we are exploring combinations of two types of algorithms to achieve more efficient and effective workflow generation. WORKFLOW OPTIMIZATION Workflow optimization focuses on characterizing and optimizing spatial data processing workflows to deliver accurate measurements in a timely manner. There might be multiple workflows for answering a given query, and these workflows can have different performances in terms of measurement errors and computational complexities. A given workflow usually has multiple possible parameter settings with different performances. Generally, preferred workflows are those that can extract measurements with lesser errors. On the other hand, the error and the workflow execution time are two competing objectives: extracting accurate spatial information usually need more time. Two aspects of workflow optimization therefore are: 1) characterize the performances of spatial data processing algorithms and workflows with well-defined metrics on measurement errors and computational complexities, and 2) explore automated approaches to optimize spatial data processing workflows, and enable inspectors to efficiently prioritize workflows and parameter settings of workflows according to their performances and their domain requirements. In this research, we are characterizing typical spatial data processing algorithms (e.g., extract planes) and workflows using two categories of performance metrics: measurement errors and computational complexities. To quantify measurement errors, we are exploring the geometric errors introduced by various algorithms and workflows frequently needed in civil infrastructure inspections. Different types of measurements are associated with different types of errors. Generally, 3D data processing algorithms can introduce two basic types of errors: a) location and b) orientation errors. These two types of errors will propagate and influence other types of errors (e.g., distance errors, angle errors). Based on the expected errors introduced by individual data processing algorithms, error propagation models can derive the expected means and standard deviations of the errors of measurements generated by workflows (Chiu et al. 2009). Currently, we are classifying 3D data processing algorithms and geometric errors associated with them, and exploring how various types of errors propagate through workflows and influence the errors of the measurements output by workflows. To quantify the computational complexities of algorithms and workflows, we are extending the theory of computational complexity developed by computer scientists (Arora and Barak 2009) in the domain of civil infrastructure inspection. The computational complexity of an algorithm is used to characterize a function that
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defines the order of growth of the running time of an algorithm. The running time of an algorithm is evaluated based on the size of its inputs (Arora and Barak 2009). In this research, the input size (n) is the number of data items, which can be geometric primitives (e.g., points, lines, surfaces), or any other objects (e.g., bridge components). Generally, three approaches can characterize the order of growth of the running time: (a) worst-case running time, (b) best-case running time and (c) average running time. The worst-case running time is preferred for describing the computational complexity of an algorithm. Currently, we are quantifying the worst-case running times of different data processing algorithms using existing computational complexity tools (e.g., divide and conquer, probabilistic analysis) (Arora and Barak 2009). Based on the computational complexity analysis of individual algorithms, we will profile the computational complexities of various data processing workflows. While characterizing the performance of spatial data processing workflows, we are also exploring approaches for optimizing workflows so that inspectors can select and configure workflows with the awareness of expected measurement errors and computational complexities. Such awareness will assist inspectors to identify the workflow parameter settings satisfying their time limits while delivering results of the least measurement errors. In this case, two competing objectives exist: “minimizing errors” and “minimizing computational complexities.” During the inspections, inspectors may have constraints on one or both objectives, and need to know the expected performance of various workflows and their parameter settings on one or both objectives. In this research, we are modeling workflow selection and parameter setting as a multi-objective optimization problem, and develop approaches for optimizing and prioritizing workflow settings based on the preferences of inspectors. CONCLUSION AND FUTURE RESEARCH This paper presents a computational framework for automating and optimizing spatial data processing workflows in civil infrastructure inspections. Three components of this framework (workflow formalization, generation, and optimization) respectively focus on developing computer interpretable representations of data processing procedures conducted by inspectors, developing automated approaches for generating, evaluating, and optimizing workflows capable of delivering the measurements targeted by civil infrastructure inspectors. Preliminary explorations described in this paper show the potential of this framework, while highlighting several critical directions that are being researched. These issues include: 1) modeling control mechanisms of workflows for adaptive spatial data processing; 2) implementing and testing planning-based workflow generation algorithms; 3) characterizing the performance of various 3D data processing algorithms; 4) developing workflow optimization models and algorithms. REFERENCES ASCE. (2009). “Achieving the Vision for Civil Engineering in 2025: A Roadmap for the Profession.” Civil Engineering.
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