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Hybrid modelling framework for synthesizingvirtual structuresMohan R. ManavazhiPublished online: 21 Oct 2010.

To cite this article: Mohan R. Manavazhi (2000) Hybrid modelling framework for synthesizing virtual structures,Construction Management and Economics, 18:4, 415-426, DOI: 10.1080/01446190050024833

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Introduction

In the global business arena of today, constructionprojects have to be conceived, planned, designed andexecuted under increasingly restrictive time, budgetand performance constraints. Construction involves the conversion of ideas depicted on plans and in spec-i� cations into a completed structure by assembling,combining and erecting a number of parts and pieces(Oglesby et al., 1989). Developments in the past twodecades in the construction industry point to increasingsize and technological complexity of the typical con-struction project. From a process-oriented perspective,the successful completion of such a project entailscareful analyses and well-planned execution of theprocesses that make up the project. It is imperative,therefore, that the modern-day construction manager,project engineer or job superintendent utilizes new and improved methods and/or tools in the planning

and analysis of construction processes in order to stayabreast of the competition.

As can be expected, the increase in size and tech-nological complexity of construction projects withconcomitant increases in demands for quantum jumpsin the degrees of economy and quality achieved poseenormous challenges for managers of constructionprocesses. Furthermore, often construction projects are shaped by a unique set of conditions. These includea volatile workforce, changing project objectives, a� uxing workfront which is exposed and adverselyaffected by changes in the physical environment, com-plex interplay of human and organizational factors, andabove all a product that is a conglomeration of a vastarray of materials put together by dynamic processes.

Construction processes are characterized by dynamicresource-to-process and process-to-process interac-tions. Furthermore, it is dif� cult to quantify preciselyand with any degree of certainty all the factors that

Construction Management and Economics (2000) 18, 415–426

Hybrid modelling framework for synthesizing virtualstructures

MOHAN R. MANAVAZHI

School of Civil Engineering, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani 12120,Thailand

Received 16 October 1998; accepted 30 September 1999

Erecting a structure in a construction project involves the use of a number of complex, interacting processes.Systematic analysis of these processes is essential for ensuring that they are executed in the most ef� cientway possible. However, traditional tools like CPM and PERT and other analytical and mathematicalapproaches cannot capture the dynamics of construction processes. These problems could be overcome byconducting experiments on the real-world processes themselves but such experiments can be prohibitivelyexpensive. Thus, a more feasible approach that can overcome these drawbacks is required. This paper presents a framework-based approach for facilitating the analyses of the operations required for theconstruction of a structure with a speci� c con� guration by building the structure virtually in a digital computerusing discrete-event simulation. The approach utilizes a hybrid framework that combines the � exibility andsimplicity of semantic networks with the power of object-orientation to facilitate discrete-event simulation.A predominantly product-centric modelling approach is used in the development of the framework. A sample application is given illustrating the application of the framework-based approach for the analyses ofthe operations required for the construction of the structure.

Keywords: Discrete-event simulation, object-oriented simulation, product synthesis, product modelling,semantic networks

Construction Management and Economics

ISSN 0144–6193 print/ISSN 1466-433X online © 2000 Taylor & Francis Ltd

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affect construction processes. Construction managershave to assess alternative technologies and resourcerequirements in addition to the effects of imponder-ables such as the weather, material shortages, labourproblems, unknown subsurface conditions, and in-accurate estimates of duration and cost (Bennett andOrmerod, 1984). All these factors contribute to theformation of a complex, dynamic problem (Riggs,1979). Successfully overcoming this problem calls fordetailed analysis and meticulous planning on the partof construction managers.

Rationale for simulating constructionprocesses

Detailed planning for the execution of a constructionprocess involves a formal, systematic exercise in assess-ing the type, number and combination of the variousresources required for completing the process. For thisto be carried out in an ef� cient and cost-effective manner, extensive process-level analysis is required.Managers responsible for the planning and execution ofconstruction processes generally have considerable lati-tude in deciding how the processes are to be executed(Bennett and Ormerod, 1984). This latitude is essentialfor both proactive and reactive planning of constructionprocesses. Traditionally, construction managers haverelied on techniques like the critical path method(CPM) and program evaluation and review technique(PERT) for planning and analysis. Although these tech-niques are popular for project-level planning and controlin the construction industry today, they have a numberof disadvantages that have been well documented(Pritsker et al., 1994). From the perspective of thisresearch, the principal shortcoming of network tech-niques is that they use a static world-view of a construc-tion process. This shortcoming abstracts away a lot ofthe dynamic process-to-process interactions andresource-to-process interactions that take place in con-struction operations. The primary reason for this staticworld-view is that these tools do not facilitate represent-ations that are adequate for a study of the internal inter-actions within complex processes. However, studies ofinternal interactions are a prerequisite for improving theproductivity of such processes. It is clear, therefore, thata more powerful tool for the analysis of constructionprocesses is required.

Theoretically, the following three mainstreamapproaches could be employed for carrying out detailed analysis of a construction process: 1. conductexperiments directly on the real-world process; 2.construct and use mathematical models of the process;and 3. conduct experiments on a simulation model ofthe process.

Conducting experiments directly on a real-worldprocess on a construction project even once can be prohibitively expensive. However, the costs associatedwith repeating the experiment on alternative scenarios ofthe same construction process could be astronomical.Furthermore, it would take an inordinate amount oftime for such experiments to be conducted, which a construction project can ill afford.

Mathematical models have been used in the opera-tions research and management science disciplines for process analysis but even in these disciplines theirlimitations have been recognized. Pidd (1992) notesthat most mathematical models cannot copeadequately with either dynamic or transient effects,both of which are obtained in signi� cant measures inconstruction processes. Additionally, the extremelyhigh levels of mathematical sophistication required forformulating models of complex, dynamic processesmake mathematical modelling an impractical tool forprocess analysis in the construction industry.

Construction operations involve dynamic, process-level, (i) resource-process interactions and (ii) process-process interactions. In addition, dynamic interactionscan take place between entities in the system.

Frequently simulation has been viewed as anapproach of ‘last resort’, but this view has changed inrecent years. It has become almost indispensable as atool for solving problems involving the analysis anddesign of complex systems (Shannon, 1992). Banks et al., (1996) note that simulation is intuitivelyappealing as it mimics whatever occurs in a real-worldsystem or what is perceived for a system that is in thedesign stage. Furthermore, ‘bottleneck’ analysis, ‘what-if’ analysis, studying the interaction of variables, andcompressing and expanding time can all be performedon real-world systems without actually committing anyresources to the associated processes and withoutdisrupting them in any way (Pegden et al., 1995). Theadvantages of using simulation from the constructionindustry perspective have been well documented inNunnally (1981) and Bennett and Ormerod (1984).

State-of-the-art in construction simulation

Following the early work of Teicholz (1963) andGaarslev (1969), in the � eld of construction simul-ation, Halpin (1973) developed the ‘cyclic operationnetwork’ ‘Cyclone’ methodology. Cyclone uses a graphical modelling format to model work states and the � ow of entities through the system. Thedevelopment of the Cyclone modelling methodologyinspired a number of other simulation systems likeInsight (Paulson, 1978), Resque (Chang, 1987), Um-Cyclone (Ioannou, 1989) and Disco (Huang

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et al., 1994). Bennett and Ormerod (1984) developed a suite of computer programs called ConstructionProject Simulator (CPS) for simulating the processesof a project,using bar charts as the primary source ofinput. The Monte Carlo simulation technique also hasbeen adapted for process modelling and analysis(Baxendale, 1984; Pilcher, 1992). More recently,researchers in construction have focused their atten-tion on the use of object-oriented concepts in construc-tion simulation (Liu and Ioannou, 1993; Oloufa, 1993; Tommelein et al., 1994; Manavazhi andAbouRizk, 1997).

Research objective

The need for this research stems from the following.1. There are inadequacies inherent in traditional

planning tools like PERT and CPM that preclude their use for the modelling and analysis of construc-tion operations that are characterized by dynamic interactions and are involved in the construction ofstructures with speci� c con� gurations

2. Simulation modelling is one of the most dif� cultaspects of discrete-event simulation, even for relativelysimple systems. The complexity of the modellingprocess is magni� ed many times when the systeminvolves structures with speci� c con� gurations

3. There has been no other research work done thus far that focuses on the analysis of the operationsrequired for the construction of structures with speci� ccon� gurations and also on relieving the end-user frommanually generating the simulation model. The manualgeneration of simulation models for simulating the operations required for the construction of structureswith well de� ned con� gurations, especially when anumber of alternative scenarios have to be tried out,could be extremely dif� cult and tedious

4. The key to process-level analysis and planning ofthe operations required for the construction of struc-tures with speci� c geometry rests on our ability toconduct detailed experiments that mirror these inter-actions with the highest degree of � delity achievable.This would require a formalism that facilitates theobservation of changes in the state of the system beinganalysed at signi� cant points in time in response tospeci� c events.

5. Almost all extant simulation research in construc-tion has focused on either the simpli� cation of existingprocess-simulation tools for use in construction or theapplication of various paradigms in computer science tofacilitate process simulation. In other words, the area ofinterest has been the construction process itself.However, most construction activity is directed towardsthe creation of structures with speci� c con� gurations.

Simulating the construction of structures with speci� ccon� gurations will require adequate representations of 1. the structure that is to be built and associated component substructures; 2. the simulation processesinvolved in building the structure; and 3. relevant con� gurational and logical constraints. Achieving theserepresentations through the manual generation of simu-lation models is both tedious and dif� cult for structureswith con� guration. The problem is compounded whena number of alternative scenarios have to be modelledand tested for analyses of the operations involved.

The primary objective of this research is the devel-opment of a simulation-based approach that willfacilitate (i) the analysis of the operations required for the construction of structures with speci� c con� gurations; and (ii) the automated generation ofsimulation models based on high-level descriptions ofthe structure, characteristics of resources and element-ary task durations.

Terminology

System: In the context of this research, a system willbe de� ned as a well identi� ed set of interdependententities that act together in a coordinated effort withthe intent to achieve some previously established goal.Entity: An entity is an object of interest in the system.For example, in an earthmoving system each piece ofequipment used for earthmoving, like a truck, a bull-dozer, or a loader, is an entity.Simulation: Simulation is the representation andmanipulation of a real-world system external to itsnatural environment with the aim of observing thebehaviour of the system in response to events that takeplace over a speci� ed period of time.Simulation model: A model is an abstracted represent-ation of the system (Pritsker et al., 1997). A modeladapted for simulation on a computer should consistof the following: 1. representations of the entities takingpart in the activities of the system; 2. constraintsimposed by logical relations and operational rules; and3. representations of elements of the environmentwhich affect the operation of the system.Activity: From the perspective of this research, anactivity is de� ned as the smallest unit of work accomp-lished by an entity consistent with the user’s view ofthe system. Examples of activities are loading, hauling,dumping, etc.

Synthesizing virtual structures

Instead of using the process plan as the starting point for conducting the simulation, the methodology

Hybrid modelling framework 417

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developed in this research uses a predominantlyproduct-centric approach in which the structure itselfis used as the starting point. The methodology is basedon the notion that a composite structure can be puttogether by the synthesis of smaller component struct-ures that make up the composite. Each of these smallerstructures can in turn be constructed from theirrespective substructures. This recursive process cont-inues till a stage is reached where the substructuresinvolved can be constructed by a clearly identi� ablesequence of activities. For the reasons cited earlier inthis paper, generally it is not feasible to perform processanalysis by actually constructing the structure. Insteadthe paradigms of discrete-event simulation and objectorientation, aided by the power of modern computers,are utilized to build digitally a virtual representation ofthe actual structure and to simulate its constructionevent by event and component by component.

Conceptual modelling

Two approaches that potentially could be used to builda virtual representation of the structure and simulate itsconstruction are: 1. the black-box approach; and 2. the framework-based approach. The black-boxapproach to tackling the simulation-modelling probleminvolves the creation of a packaged ‘black-box’ applica-tion that will not require the end-user to possess muchexpertise in discrete-event simulation and other pert-inent computer science concepts. However, there are anumber of disadvantages with such an approach. First,the approach has limited � exibility, that is, it is restrictedto the type of project for which it has been developed.Second, there is a lot of useful information about theworking of the system being modelled that can beobtained from standard simulation outputs like the sim-ulation trace that cannot be utilized. Finally, adoption ofthis approach could inhibit the gradual upgrading ofcomputer related knowledge and skills in the construct-ion industry. The upgrading of computer related knowl-edge and skills is critical to the construction industryespecially if it has to keep pace with other industries likemanufacturing with respect to the introduction and useof sophisticated computer based techniques.

The framework-based approach attempts to addressthe � exibility issue through the development of astructured, semantically rich yet computationally tract-able representation scheme which provides modellingconstructs for piecing together a conceptual model of the system being studied. The model is thentransformed into executable code. While this approachprovides a far greater degree of � exibility in terms of thevarious types of systems that could be modelled, its dis-advantage lies in the modeller having to possess the

expertise required to perform the modelling. However,this is not an insurmountable problem for two reasons:1. the expertise required can be inducted into the con-struction industry from outside. This is a commonapproach used by other � elds like manufacturing andthe process industries. 2. There is a new breed ofgraduates coming out of construction programmes inuniversities who are well versed in the required conceptsin computer science and allied � elds, some of whom willbe employed in the construction industry in the future.

The � rst step in the methodology involves thedevelopment of a conceptual model of the real-worldsystem that is to be simulated. A conceptual modelportrays entities within a system and their relationshipto one another. It can serve as a knowledge base forall knowledge about the system (Fishwick 1995).

There are a number of modelling formalisms thatcould be used for developing a conceptual model of asystem. One such formalism is the semantic network,which is a graphic formalism for representing conceptsrelated to a system and concerning which somereasoning has to be performed (Randal, 1988; Dymand Levitt, 1991). The main advantages of a semanticnetwork, are (a) its � exibility, as it allows almost anyconcept to be represented, and (b) its simplicity, whichmakes it a good communication tool. In this research,an enhanced semantic network (ESN), which is amodi� ed form of the general semantic network, is used to represent the model. The nodes in a semanticnetwork usually represent concepts (e.g. beam,column) and the links usually represent relations (e.g.‘part-of’ and ‘supports’). The convention adopted isthat names of concepts will start with upper-case lettersand relations will have all lower-case letters.

In Figure 1, the two columns (C1 and C2) and thebeam (B1) form a frame structure. Thus the framecould be considered a composite of its componentsubstructures: the beam B1 and the two columns C1and C2. Figure 1 includes also a semantic networkrepresentation of the frame structure. The represent-ation shows that the frame is composed of two entitytypes one called the beam type and the other calledthe column type. C1 and C2 are instances of thecolumn type. B1 is an instance of the beam type, andcolumn supports beam.

The following sections will describe key conceptsand mechanisms that help transform the conceptualmodel developed thus far into a programmaticallytractable representation of the system.

Taxonomic organization

One of the powerful concepts in the area of knowledgerepresentation in general and the representation and

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manipulation of real world systems in particular is thatof abstraction. Abstraction provides a mechanism by which representations of entities within a system are arranged in a taxonomic organization. In such anorganization, a node can represent a class, a subclass oran instance of a class or subclass. A class represents atype of entity within a system, a subclass represents asubtype, and instances represent examples of types orsubtypes. A class could be considered as a blueprintfrom which representations of system entities or objectscan be created. The blueprint contains informationrelated to the form (data) and behaviour (functions)pertaining to a system entity. Figure 2 shows a con-ceptual representation of a column class. The processof creating instances of classes or subclasses is calledinstantiation. Annotated arrows can represent one ormore of the following connections in a network: (a)class to subclass; (b) class to instance; or (c) subclassto instance.

Class, subclass and instance represent a decreasingorder of abstraction, that is, class is more abstract than subclass and subclass is more abstract thaninstance. A collection of nodes representing concepts insome domain playing the roles of classes, subclasses orinstances can form a classi� cation hierarchy. Figure 3shows beam as a subclass of the structural-memberclass, and beam1 as an instance of the beam subclass.

Here the classi� cation hierarchy consists of twobranches, a structural-member–beam–beam1 branchand a structural-member–column–col1 branch. A dist-inction is made between class-to-subclass connectionsand class/subclass-to-instance connections. Arrows rep-resenting class-to-subclass connections are annotatedwith ‘is-a’ while those representing class-to-instanceand subclass-to-instance connections are annotated

Hybrid modelling framework 419

Figure 1 Semantic network representation of a frame struc-ture

Figure 2 Conceptual representation of a class

Figure 3 Use of abstraction and inheritance

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with ‘instance-of’. Thus beam is a kind of structural-member, whereas beam1 is an entity of the beam type.The three types of connections listed above facilitate thetransfer of information about the form and behaviourof components of the structure from a more abstractnode to a less abstract node through a mechanismknown as inheritance. In Figure 3, the structural-member node transfers information about form andbehaviour to the beam and column nodes. Although the‘weight’ attribute is de� ned only at the structural-member node, both the beam and column nodes auto-matically obtain this attribute (indicated by dottedarrows). Furthermore, speci� c information that appliesonly to a subclass and not its parent class also can beincorporated in the node representing the subclass.This concept is illustrated in Figure 3, by the availabil-ity of a ‘feature’ attribute in the column node that thestructural-member node does not possess. The columnnode thus has all the information that is available in thestructural-member’ node plus speci� c informationabout ‘feature’. The concepts of class, subclass, instant-iation and inheritance are similar to those detailed instandard works on object-oriented programming anddesign (Booch, 1991; Rumbaugh et al., 1991).

Structural aggregation

In addition to concepts and relations, the conceptualmodel must facilitate the representation of the synthe-sis of component substructures to arrive at higher lev-els of composition. In the frame example, the beam andthe columns combine to form a frame structure. Thismethod of combining together two or more componentsubstructures to form a composite is represented as anaggregation operation. Each aggregation operation isformed by the combination of two or more special linkscalled P -links. Details of the P -link are described in thenext section. In the frame example, the aggregationoperation is performed on the beam class and the column class to produce the frame class. Thus a composite structure can be constructed by employingeither one or a number of successive aggregation oper-ations with the end product of lower-order aggregationsserving as inputs to higher order ones. Therefore applic-ation of aggregation operations in an ordered sequencewill result in the progressive construction of the � nalstructure with a desired con� guration.

The P -link

In the ordinary semantic network (shown in Figure 1),there is no explicit encoding (at the class level) of the number of beams of the beam type or the number

of columns of the column type that are included in the system. In other words, the fact that C1 and C2are the only columns in the structure or that B1 is the only beam in the structure is not transparent to acomputer-based reasoning system. This anomaly isaddressed in the ESN by establishing a P -link betweenthe component substructure class and the compositeclass. The special property of the P -link is that itspeci� es explicitly an aggregation index, which de� nesthe number of component substructures that combineto form the composite. The representation of thesynthesis of substructures (structures of lower granu-larity) to form a composite (structure or substructureof higher granularity) is facilitated by means of suchP -links. The P -link is represented by means of an arcannotated with the label P (n), where n represents theaggregation index. Figure 4 shows an ESN represent-ation of a frame structure. The representation showsthat two entities in the column class and one entity inthe beam class are associated in P (n) relationships with the frame structure.

In the process of identifying substructures whoseaggregation produces a composite of higher granularity,a stage will be reached when the substructures with the lowest granularity identi� ed thus far can beconstructed through well de� ned constructionprocesses. From the perspective of this research, a con-struction process is well de� ned if its resource require-ments and duration model characteristics are known.The ESN representation for the simulation of the con-struction of the full structure will be a mixed product-cum-process model. Figure 4 shows such a model thatcombines elements of process modelling with a productmodel. In the mixed model, each branch terminates inone or more processes represented by the nodes Act. 1,Act. 2, . . . Act. N. These nodes are called terminalnodes. Nodes at higher levels, that is, ancestors of theterminal nodes except the root node, are called compo-nent nodes and represent component substructures.The root node represents the structure that is to be con-structed. While it is possible to have pointers from thenodes at the penultimate level to the nodes at the � nallevel, namely, Act. 1, Act. 2, . . . Act. n, which representsimulation processes in Figure 4, the current version ofthe proof-of-concept prototype implements the simula-tion processes (represented by Act. 1, Act. 2 . . . Act. n)as object-behaviours of the penultimate nodes (objects).

Component integration through virtualconnectivity

Each component-node of the ESN maintains repre-sentations of its state and behaviour. The state and

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behaviour can be controlled only from within the node itself. This ensures modularity and streamlinedexecution of the simulation processes required toconstruct the various components of the structure.However, a prerequisite for the virtual synthesis of astructure is that the representations of the disparateentities that collectively make up the system be linkedtogether in some manner so as to form an integratednetwork of component nodes. The virtual connectivityrequired for network integration is achieved by meansof inter-nodal communication links that facilitateinformation transfer to and from a component node.There are two kinds of inter-nodal informationtransfer: 1, inheritance-based information transfers;and 2, request-based information transfers.

Inheritance-based information transfer occurs whena component node (child node) selectively inheritsproperties and behaviour from its parent componentnode (the node immediately above it in a classi� cationbranch) and it takes place automatically. Information� ow due to inheritance is unidirectional, that is, as inany parent–child relationship, properties and behaviourcannot be inherited by a parent node from its childnode. However, the ESN contains a hybrid collectionof both abstraction and aggregation links. Inheritance-based information transfer can occur only throughabstraction links, but has to stop once the informationreaches a component node with an aggregation link.Request-based information transfer is used as amechanism for circumventing this problem. Request-based information takes place on the directed requestsent by a node and is omni-directional in character.

Requests can be for information on the status of thestructure (e.g. current volume) or for initiation of some action (e.g., commence simulation). The mech-anism for facilitating directed communication betweencomponent nodes is explained in the next section.

Inter-nodal communication mechanism

Each component node in the integrated network,except for the terminal nodes, has two clearly de� nedzones, a structural zone and a behavioural zone.

The structural zone, which will be called the struct-ural information cell (SIC) serves as a repository ofinformation pertaining to the form (structure) of theentity represented by the component node. Thecontents of the SIC are available only to the compo-nent node, that is, the node does not permit another(external) component node from accessing the contentsof its SIC directly. However, the methods that de� nethe behaviour of a particular component node can bothaccess and change speci� c information contained in itsown SIC. The double-headed arrows drawn across theBRC-SIC boundary illustrate this concept in Figure 5.

The behavioural zone is made up of a behaviourresponse cell (BRC) and a behaviour stimulationprotocol interface (BSPI). The BRC and BSPI togetherprovide the required functionality to enable eachcomponent node in the network to play its role in thevirtual construction of the � nal structure.

The BRC contains a collection of methods thatdetermines the behaviour of the entity represented bya particular component node. The BSPI provides aprede� ned bandwidth that facilitates the elicitation ofdesired but permissible responses from a particularcomponent node in response to a signal sent by someother node in the network. The BSPI also contains aprede� ned protocol that enables it to � lter out anyincoming signals that do not match criteria for legality.Dotted arrows depict the protocol portion of the BSPIin Figure 5.

Each component node is equipped with a signaltransmitter that is attached to the BRC. This allowsthe node to transmit signals to other component nodeswhen it requires individual or concerted behaviourfrom these nodes.

Communications in the form of requests (in theproper format) either for action or structural inform-ation can be sent from one component node toanother. Such communication between componentnodes is facilitated through the BSPI, which restrictsthe kinds of message that it will pass on to the nodefor stimulating component behaviour. For example, a‘column’ node can request a beam node to commencesimulation. An ‘initiate simulation’ signal or message

Hybrid modelling framework 421

Figure 4 ESN representation of frame structure

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will be sent by the column node to a speci� c beamnode through its transmitter. The BSPI in the beamnode will conduct a check to see if the ‘initiate simu-lation’ signal is within its prede� ned bandwidth, andis one that has been issued in the proper format. Ifthis is the case the beam node will carry out the modeof behaviour corresponding to its ‘initiate simulation’speci� cations stored in the BRC. The beam node’sBSPI will not allow illegal signals to pass through toits node. Similar signals and behaviour responses canbe obtained from other non-component nodes repre-senting various system entities. Thus a concerted andaggregate behaviour response from the network systemas a whole can be developed which results in the virtualconstruction of the structure. The BSPI thus preventsnot only the occurrence of chaotic component behav-iour but also unplanned and unscheduled changes inthe state of component structures.

Generation and execution of simulationmodel

The model generation process results in the creationof a simulation model of the operation being analysed.A computer-based prototype system that automaticallygenerates the simulation model and executes the simu-lation was developed. The inputs to this process consistof high level descriptions of the relevant details of the structure, the environment and details of theresources taking part in the simulation. Such high leveldescriptions can be provided in the form of ASCII � les, through a GUI or provided in CAD models. Theinformation also can be provided in any convenientcombination of these input modes.

Model generation is accomplished through a two-stage process. The � rst stage involves the creation ofsubmodels of the environment, resources and compo-nents of the structure based on user inputs. In thesecond stage, the submodels are linked by means of

communication links to form an integrated collectionof component-nodes, environment entities representingrelevant parts of the environment and resource enti-ties. Integration is achieved as described in thepreceding sections.

Once the integration of the submodels has beenachieved, execution of the simulation is initiated. Thetype of simulation used in this research is called discrete-event simulation. A detailed discussion of discrete-eventsimulation is provided in Pidd (1992) and other stan-dard works on the subject. During the simulationprocess, the penultimate node (i.e. the node next to theterminal node) in each branch of the network � rst con-ducts a resource-level check against the availableresources in the project resource pool. If the resourcerequirement in a module cannot be satis� ed by theresources available in the project resource pool, the useris alerted with an appropriate message that points outthe particular module that does not have the requiredresources, and simulation is terminated. Then the theuser can rectify the problem before initiating the modelgeneration and simulation processes again. However, ifthe resources available in the project resource pool cansatisfy the resource requirement for the module, thenthe simulation process is continued. In this case, thesimulation is terminated once all the processes requiredto construct the entire structure have been simulated.The user is then presented with a report that is used forthe analysis of the operation.

Sample application

The structure selected for the sample application was anearth-� lled embankment. The structure had two zonesas shown in Figure 6. Zone 1 was built using materialfrom source 1 and zone 2 was built using material fromsource 2. Material from the sources was brought to therespective zones using a truck and loader operation. Theschematic (plan view) of the layout of the project isshown in Figure 7.

The total volume of material for building the struct-ure selected was 2000 m3. The volume of materialrequired for material in zone 1 was 1200 m3 and that for zone 2 was 800 m3. The material for the twozones came from two separate sources. Each sourcehad a CAT-235C front shovel loader. The 235C loads3.0 m3 of material on to the truck per (loader) cycle.The duration of each loader cycle was 0.5 min. Thehauling units for each section was made up of twoCAT-D250B and two CAT-D300B articulated dumptrucks. The D250B carries 10.0 m3 of material per tripand the D300B carries 12.0 m3 of material per trip.The project has been assigned a � eet of three numberseach of CAT-235C loaders, CAT-D250B trucks and

422 Manavazhi

Figure 5 Component nodes showing mechanism for inter-nodal communication

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CAT-D300B trucks. It was decided that for thissimulation trial each section would be allotted a � eetconsisting of two CAT-235C loaders, two CAT-250Btrucks and two CAT-300B trucks.

The sample application described above involves theconstruction of a structure with a speci� c con� guration.The structure is made up of two component substruc-tures. Examples of dynamic interactions that occur inthe system are those between ‘loader 1’ and its � eet oftrucks, ‘loader 2’ and its � eet of trucks, and between theloading process and hauling process. These dynamicinteractions are associated with each of the componentsubstructures. Given the resources available, � ve questions that need to be asked for performing process-level analysis of the construction of the structure.

1. How long will it take to construct the entirestructure?

2. How long will it take to construct each of thecomponent substructures?

3. What is the level of utilization of resources?4. What is the throughput of the system?5. What are the effects of varying the quantities

and characteristics of resources available on theparameters noted above?

To answer these questions, representations of boththe embankment that is to be constructed and the

processes required for constructing the embankmentare required. The ESN framework facilitates the repre-sentations of the embankment and its components (thetwo zones which make up the embankment), represen-tations of entities (loaders and trucks) that take part inthe processes and communication between these repre-sentations. Simulation processes are represented asobject behaviour of component nodes.

Each component zone is represented in terms of its‘form’ and ‘behaviour.’ Enactment of the behaviour ofa zone results in the ‘virtual’ construction of that par-ticular zone. The enactment of the construction of thenext zone is initiated automatically without the bene� tof any form of user-intervention.

It is important to note here that tools like PERT andCPM do not have the means (required constructs) torepresent form and behaviour. They also do not havethe means to facilitate the required communicationbetween representations to generate and execute thesimulation model automatically. Thus neither CPMnor PERT can be employed for the analysis of opera-tions that are characterized by dynamic interactions.

The ESN model developed for the sample applica-tion is shown in Figure 8. The root node is called‘Structure’ and is P-linked to a zone class node. TheP-link has an aggregation index of 2. Zone 1 and zone 2 are two objects instantiated from the zone class. A predecessor relationship is also establishedbetween nodes zone 1 and zone 2. This is essentialbecause zone 1 must be constructed before zone 2.The ESN is thus far a purely product-centric repre-sentation of the structure. Terminal nodes representingthe various activities that have to be executed for theconstruction of each zone are shown as activities Act.1,Act. 2, . . . Act. N.

Since the main aim of this work was to develop ahybrid modelling framework that would facilitate virtual construction of a structure in a computer, a uniform distribution was adopted for representingactivity durations. Other distributions that � t the dura-tion data can be also be employed. Furthermore, inthis particular example all durations were assumeddeterministic by making the value of the high and lowparameters of the uniform distribution equal. This wasdone in order to facilitate comparison of the resultsproduced by the prototype-generated simulation modelagainst the results provided by the execution of amanually generated simulation model, details of whichare presented in Manavazhi (1998). The results of thesimulation are shown in Table 1, which shows that thestructure is completed in 489.5 minutes with the � rstand second zones taking 293 minutes and 196.5minutes, respectively. The utilization factors for theloaders at source 1 and source 2 are 0.77 and 0.76,respectively.

Hybrid modelling framework 423

Figure 6 Embankment showing zones

Figure 7 Plan view (environment) for the sample applica-tion

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There is a discrepancy between the volume ofmaterial required in the two zones and the actualmaterial dumped. For example, the volume of materialrequired in zone 1 is 1200 m3 while the volume ofmaterial actually dumped appears to be 1208 m3. Thisdiscrepancy occurs because the system computes theoutput in terms of full truckloads. In the present case,a trace of the simulation showed that at the end of the penultimate dump the total volume of materialdeposited was 1198 m3. The � nal dump by a CAT-D250B truck with a capacity of 10 m3 resulted in thesystem recording the total volume of material depositedas 1208 m3. This could be corrected by making thevolume of the last dump to be exactly equal to thevolume still required to achieve the target.

In contrast to a manually developed simulationmodel that may require tedious and often complexchanges to the ‘hard-wired’ simulation model itself, theapproach presented in this paper would involve merelychanging input data in ASCII � les. The simulationmodel is automatically generated from the input data.Thus ‘what-if’ analysis could be performed simply bychanging the input data provided in the ASCII � les.For example, it is relatively easy with the approachsuggested in this paper to study the effects of varyingthe con� guration of the hauling � eet on the time takento complete the component substructures, the degreeof utilization of resources and the time required tocomplete the entire structure. Table 2 provides dataobtained from successive simulation runs each with adifferent hauling � eet combination for the construc-tion of the same structure. Such an exercise couldprovide useful information for a planner to decide onthe most appropriate haul � eet con� guration based on:(i) the availability of trucks, the time available forcompleting the structure, and the required level ofutilization of loaders (optional); and (ii) information

such as the time required for the construction of thestructure and level of utilization of loaders for variouscombinations of the hauling � eet (obtained from simulation runs).

The four hauling � eet con� gurations used in thecurrent experiment were: 1, one D250B truck and oneD300B truck; 2, one D250B truck and two D300Btrucks; 3, Two D250B trucks and one D300B trucks;and 4, two D250B trucks and two D300B trucks.

The results of the simulation experiment presentedin Table 2 show that if the time available to completethe structure is greater than or equal to 16.03 hoursthen it would be advisable to use one D250B and oneD300B. However, if the structure is to be completed inabout 9 hours then it would be better to use twoD250Bs and two D300Bs. Cost comparisons amongthe options could be made by computing the hourlyowning and operating (O & O) costs of the equipmentbeing used for each alternative. In the case of the haul-ing � eet consisting of two D250Bs and two D300Bs,the ef� ciency of utilization of the loader resource by thehauling � eet is the highest among the four options. Thethroughput of the system is also the highest for thishauling � eet. Again a hauling � eet consisting of oneD250B and two D300B trucks will complete the job inslightly less time than a � eet consisting of two D250Bsand one D300B, with almost the same ef� ciency of uti-lization of the loader resource. Similarly a number ofother desired � eet con� gurations can be tried out veryquickly. Various other scenarios incorporating changesin the con� guration of the structure resulting in changesin the overall volume and/or volume of component sub-structures could be tested similarly.

Conclusions

The computer-based method described in this paperfor the ‘virtual’ synthesis of structures provides aconvenient platform for staging the enactment of theprocesses planned for the construction of a structurewith a speci� c geometry. While the discrete-eventsimulation paradigm sets into motion the processes

424 Manavazhi

Figure 8 ESN representation for the sample application

Table 1 Simulation results for sample application

Item Values

Project duration 489.5 minZone 1 duration 293 minZone 2 duration 196.5 minDumped qty. zone 1 1208 m3

Dumped qty. zone 2 802 m3

Loader 1 utilization 0.77Loader 2 utilization 0.76System throughput 4.11 m3/min

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that make up an operation event-by-event and state-by-state for observation, the ESN framework facilitatesthe representations of the processes, representations ofentities that take part in the processes and communi-cation between the representations. The frameworkprovides for modularity by exercising strict control over the interactions that take place between modules(ranging from a single simulation entity to the entirestructure) through well de� ned communication inter-faces called behaviour stimulation protocol interfaces(BSPIs). It is this modularity coupled with hierarchicaldecomposition that forms the basis for performingcomplex simulations required for the analysis of struc-tures with speci� c con� gurations.

The ESN framework also provides:

1. an intuitive developmental scheme of the systembeing analysed;

2. an interpretative scheme of the functioning ofthe system being analysed; and

3. the means to transform subtask-level (micro-level) knowledge of elementary tasks like theloading of a truck to operation-level (macro-level) information such as the time required for the entire operation. All other things beingequal, since the accuracy of subtask-level information is relatively higher than operation-level information, could expect the overall accuracy of the operation-level informationobtained from the simulation to be higher thanthat obtained by traditional means.

In contrast to the ESN framework-based approachproposed in this research, the dynamic nature of the interactions precludes the use of traditional toolslike PERT and CPM because they do not facilitaterepresentations adequate enough to capture the inter-actions. Tools like PERT and CPM also do not have the facilities to activate and sustain adequaterepresentations of construction processes for observa-tion. In the absence of such capabilities it would beimpossible to obtain information such as the timerequired for completing the operation and utilization

of various resources in the system. It would also notbe possible to obtain such information for any desiredcomponent of the structure. Therefore PERT andCPM cannot be used to perform process level analysisof the construction of structures with speci� c con� g-urations to obtain results that are even comparable withthose obtained from the approach described in thispaper.

In the sample application described in this paper,examples of dynamic interactions that occur in thesystem are those between loader 1 and its � eet oftrucks, loader 2 and its � eet of trucks, and betweenthe loading process and hauling process. The simula-tion results obtained show that it is possible to obtaincomponent-level information like duration, resourceutilization, throughput and quantity of materialdumped in each case in addition to obtaining perti-nent information for the structure as a whole. Theresults are obtained almost immediately, and a numberof simulation runs could be executed with variationsin system inputs. This will allow the user to try outvarious scenarios (‘what-if’ analysis) and select the bestalternative for a particular operation. In future versionsof the prototype, a major part of the data for gener-ating the simulation model will be available from CAD� les instead of ASCII � les, thus substantially reducingthe amount of data entered manually.

The major contribution of the research presented inthis paper is the development of a framework-basedapproach for the analysis of the operations required forthe construction of structures with speci� c con� gura-tions using discrete-event simulation. The advantagesof using the framework are the following. (a) It servesas a medium for facilitating simulation of the construc-tion of structures with speci� c con� gurations bycombining product modelling with the modelling andexecution of simulation processes. (b) Due to itssemantic richness and representational power, theframework facilitates the capture of dynamic process-to-process and resource-to-process interactions, whichare common in construction operations. It overcomesrepresentational and functional inadequacies in tradi-

Hybrid modelling framework 425

Table 2 Results of simulation trials with various haul-� eet compositions

Item Fleet con� guration

235C (1) + 235C (1) + 235C (1) + 235C (1) +D250B (1) + D250B (1) + D250B (2) + D250B (2) +D300B (1) D300B (2) D300B (1) D300B (2)

Project duration 962 min 626 min 661.5 min 489.5 minLdr 1 utilization 0.38 0.58 0.57 0.77Ldr 2 utilization 0.38 0.57 0.57 0.76Zone 1 duration 576 min 375 min 398 min 293 minZone 2 duration 386 min 251 min 263.5 min 196.5 minSystem throughput 2.09 m3/min 3.21/min 3.04 m3/min 4.11 m3/min

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tional planning tools like CPM and PERT. (c) Itembodies an approach to simulation modelling thatclosely ties the construction simulation modeller to themodelling process. (d) It provides a semantically rich,structured, metaphorical and thus intuitive set ofmodelling constructs: i.e. (i) that the modeller canpiece together to develop a conceptual model ofsystems with a high degree of complexity which arecharacteristic of structures with speci� c con� gurationsand (ii) that the modeller can map to the elements ofthe familiar object-oriented programming paradigm.(e) It combines the � exibility and simplicity of semanticnetworks with the programmatical tractability andrigour of object-orientation. (f) It supports the auto-mated generation of simulation models given high leveldescriptions of the structure, resources and elementarytask durations. This obviates the need to constructsimulation models manually for each scenario that hasto be modelled and tested

At the level of simulation-modelling constructs, theconcepts of structural aggregation, the P -link, taxo-nomic organization, inter-nodal communication andthe metaphorical interpretations of object-orientedsimulation presented in this paper are novel conceptsin the � eld of construction simulation.

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