Computer simulation and analysis framework for floating caisson construction operations

AUTCON-01570; No of Pages 12 Automation in Construction xxx (2013) xxx–xxx

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Computer simulation and analysis framework for floating caisson construction operations John-Paris Pantouvakis, Antonios Panas ⁎ Centre for Construction Innovation, Faculty of Civil Engineering, National Technical University of Athens, 9, Iroon Polytechniou, Zografou Campus, 15780 Athens, Greece

a r t i c l e

i n f o

Article history: Accepted 17 April 2013 Available online xxxx Keywords: Floating caissons Model Productivity Resources Simulation

a b s t r a c t The aim of the research is twofold; the development and implementation of a floating caisson fabrication process simulation platform (CaissonSim) which will also cater for statistical analyses and the founding of a methodological framework investigating system response variations. A general purpose simulation language (Stroboscope) was used for modeling and the analysis took into consideration variables such as resource deployment strategies, task execution sequence and physical parameters. Following a brief presentation of CaissonSim development process and main components, a real large-scale infrastructure project is examined to ensure the system's applicability and usefulness. In addition, critical production parameters are analyzed for sensitivity. The slipforming activity is the key driver of the whole production process, a fact which may be elusive by using different approaches to the problem. As such, CaissonSim is a practical tool of use to both academics and practitioners in analyzing floating caisson fabrication activities. © 2013 Elsevier B.V. All rights reserved.

1. Introduction Floating caissons are prefabricated concrete box-like elements with rectangular cells that are suited for marine and harbor projects, such as ports, breakwaters, wharves, berthing facilities and docks, dry docks and slipways, fishing ports, marinas and container terminals [1] (see Fig. 1). Due to the size of the investments involved, the implementation of high-level analytical techniques, such as simulation modeling, for the study of floating caisson construction, ensures the quality of the proposed solutions and minimizes the risk of design problems that will affect the project operation, once they have been implemented. In view of the fact that construction operations call for decisions to be made that should be based upon credible information, simulation models are able to assist the decision-making process by highlighting the inherent characteristics of the modeled system [2]. However, despite its obvious advantages simulation has been often criticized for being more of a “black art”, which yields misleading or non-applicable results [3]. As such, construction practitioners and the industry at large seem to remain unconvinced of the merits stemming from the application of simulation for modeling and analyzing construction operations [4]. It has been argued that some of the key reasons for doubting the effectiveness of simulation as a decision tool are the failure to adequately demonstrate the effect of critical physical factors that directly affect the system's performance in a clear and understandable manner

⁎ Corresponding author. Tel.: +30 210 772 3644; fax: +30 210 772 3781. E-mail addresses: [email protected] (J.-P. Pantouvakis), [email protected] (A. Panas).

[5], the reporting of simulation models that lack basic information which would enable the repeatability of the experiments for verification purposes and the interpretation of simulation results without delineating the contextual framework within which the study was conducted [6]. The latter is especially important in the project planning phase, since the contextual background of key productivity or cost data determines the scope of their applicability in the estimating process [7]. A fundamental prerequisite for successfully estimating future activities is the proper analysis of historical data from completed activities, where simulation has been extensively used. As such, this research opts for clarifying the interactions among major production parameters involved in the construction of floating caissons by analyzing actual field data. Understanding and interpreting statistical simulation results which are not explicitly associated with a specific operational setting, especially when the system under study is a complex one, can be a very difficult task [8]. Hence, the aim of the research is the development of a simulation platform that enables the statistically valid analysis of construction operations and the establishment of a methodological framework that allows the investigation of system response variations in terms of selected factors, with a focus on the floating caisson construction operations. The overall objective of this paper is the application of simulation concepts, in order to automate the modeling and analysis of the caisson fabrication process by taking into account key productivity parameters relating to the workflow sequence, the adopted construction method and the characteristics of the deployed equipment. The study neither pursues the development of just another simulation application, nor the demonstration of the computational capabilities of simulation, since the latter have been extensively explored in published literature, as will be shown in the next sections. The aim is to present a simulation

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Please cite this article as: J.-P. Pantouvakis, A. Panas, Computer simulation and analysis framework for floating caisson construction operations, Automation in Construction (2013), http://dx.doi.org/10.1016/j.autcon.2013.04.003

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2. Background 2.1. Literature review

Fig. 1. Floating caissons positioning for quay wall construction.

tool whose functional characteristics allow the quantitative assessment of the impact that dynamically changing operational parameters have on construction productivity. In this manner construction operatives will be able to make informed decisions for the execution of largescale operations, such as the construction of floating caissons, where variations in key operational parameters may cause significant differentiations in cost and productivity estimates. In addition, the contribution of the modeling effort presented in this paper is threefold: first, the construction of floating caissons is a specialized, resource-intensive activity, which despite its popularity in contemporary marine infrastructure projects has not been adequately explored in the literature. Second, the developed model encompasses the whole production cycle for the construction of floating caissons including the assembling and dismantling phases at the beginning and the end of the production process respectively. Third, a simulation model library containing seven sub-models has been developed, so as to accommodate variations in workflow sequence, taking into account the possibility for parallel execution of activities, changes in the deployed crews and differentiations in key project resources and equipment, such as the floating dock. The research explores the use of information technology applications from the aspect of simulation modeling, which is an approach directly linked to automation in construction [9]. In addition, the automated processing of the simulation models contained in the platform's library extends the research scope to cover for alternative implementation scenarios, so as to enhance the system's versatility. Hence, a practical tool is established which facilitates the computer-aided analysis of construction processes by taking into account critical system parameters. The structure of the paper is as follows: a literature review of pertinent research on construction simulation studies is going to be provided, followed by a concise description of the floating caisson construction process. Then, the computer simulation platform development process is going to be delineated and, subsequently, the results of its implementation on a case study will be presented, so as to demonstrate its applicability. The results of the sensitivity analysis along with the main emerging inferences and proposals for future research will conclude the study.

Discrete-event simulation has been extensively used for the analysis of construction systems [9,10] and covers a wide spectrum of projects ranging from large infrastructure projects, such as the construction of bridges [11], to specific machine-intensive [3] or labor-intensive operations [12]. Using computer simulation tools, models can be built that represent the overall logic of various activities required to construct a facility, the resources involved in carrying out the work (e.g. crews, equipment, management) and the environment under which the project is being built (e.g. weather, ground conditions, labor pools, market situation) [4]. From the pool of available discrete event simulation languages, this research adopted the Stroboscope simulation language which has its roots in the CYCLONE approach to modeling, analyzing and controlling construction operations [13]. This choice is justified by their broad popularity within the academic community as indicated by the numerous simulation models that have been developed and applied in a variety of construction projects [2]. Indicatively, CYCLONEconfigured simulation tools have been developed for the last 30 years starting with INSIGHT, RESQUE, UM-CYCLONE and Micro-CYCLONE in the '80s [2,13], going over to COOPS, CIPROS, DISCO, and STROBOSCOPE in the '90s [14] and reaching more modern research efforts such as EZstrobe [15], HKCONSIM [16], GACOST [17] and Bridge_SIM [11]. In terms of the application scope in construction, in the last decade CYCLONE and Stroboscope have been applied in bridge construction operations [11,18], concrete paving operations [19], concrete formwork operations [12], pile productivity assessment [20], sewer line installation operations [17], asphalt paving operations [21], and concrete batch plant production operations [22] with numerous other applications in the past years. In view of the major limitations identified by published literature in the previous section, as well as the simulation models presented above, this research aims to contribute to the existing body of knowledge as follows: (i) The developed simulation platform enables the automated generation of alternative scenarios based on combinations of the model resources and main production parameters through a specifically designed module. (ii) The system produces flexible and dynamic reports with full control over the simulation process and the respective results of every single replication within a specific experiment. Thus, the results are interpreted against a clearly defined operational setting. (iii) The study examines production parameters which have not been scrutinized in the context of the caisson fabrication process before, as will be shown in the next paragraph. Floating caisson construction from a productivity-related standpoint has not been adequately explored thus far. Halpin and Martinez [1] report on the application of PROSIDYC, a computer based system for analyzing construction job site production processes, for simulating floating caisson construction operations [23]. However, since the paper was mainly intended to demonstrate the practical benefits of simulation implementation for a large international construction company, the scope of the analysis is limited in several ways: (a) no exact description of the computer system components or structure is provided, besides a generalized and descriptive delineation of its main features, (b) the presented analysis focuses only on the caisson pouring sub-cycle and the model structure is not explicitly explained or justified, (c) the research is confined to reporting on the benefits of simulation modeling in terms of productivity improvement as a result of a change in the concrete pouring strategy, without providing explicit contextual information regarding input modeling, processing and output analysis. As such, this paper is intended to build upon the

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previously identified limitations and provide an integrated simulation and analysis framework for the floating caisson production cycle, as will be shown in the next sections. In addition, the simulated construction method by [23] differs from that adopted within the framework of this research, which serves as another point of differentiation of the current study. 2.2. Floating caisson construction process Floating caissons are commonly termed as box caissons and are cast on floating dry docks. The term “floating” stems from the fact that, upon completion, they are transported to their final position by towing boats and their cellular cavities are filled with granular material, so as to be firmly founded on the leveled underwater layer (see Fig. 1). The caisson production cycle commences with the construction of the foundation slab, which requires the deployment of formwork crews, reinforcement bar placement crews and concrete pouring crews on the floating dry dock. Upon completion, secondary reinforcement steel is placed (e.g. horizontal bar placement, rebar overlapping, rebar ties) and the concreting phase of the main caisson body may commence. It should be noted, that due to the standardized shape of caissons and the repetitive nature of the works, since caissons are always constructed in batches, the concreting process is most commonly executed with the use of the slipforming construction technique. Slipform is a sliding-form construction method, which is used to construct vertical concrete structures and the reader is referred to [24] for further information. The level of operational sophistication within which the slipform activities are going to be executed varies, depending on the type of equipment used. More specifically, in the case of [23] the caissons are constructed on a floating dock that is specially designed by the contracting company and, hence, concrete is delivered through a dedicated piping network built-in on the dock. Given that such solutions are costly and the required investment is often not justified for medium-sized contractors, this research adopts a more generic approach and analyzes the caisson construction cycle on a submersible floating dock, where concrete is delivered by concrete pumps directly onto the caissons. Generally, the concreting and slipforming process comprises three sub-phases (see Fig. 2): (i) slipform assembling phase, (ii) slipforming phase and (iii) slipform dismantling phase. In the assembling phase, metal formwork boxes are inserted in each caisson's cell by a specialized crew, followed by the installation of the outer formwork walls. All form units are leveled for both internal and external faces and yoke units are completed by installing the yoke channels across the walls. Then, the preparation for the slipforming phase commences by conducting a safety check on all hydraulic systems and fixing all handrail timbers and protection boards of the scaffolding. After the completion of the final survey, slipforming may commence. The initial concreting phase comprises the concrete pouring in layers of 200 mm

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to 250 mm, with a tolerance of 50 mm. After the setting of the initial concrete in a period of 7–8 h, the slipforming activity starts. The slipforming pace, or in other words, the sliding rate (expressed in m/h or cm/h) is a key productivity factor, whose magnitude depends on concrete properties, weather conditions and managerial capabilities. During slipforming, both concreting and rebar installation crews work continuously as the structure gains height. On reaching the top level of concrete the form will be progressively jacked free to ensure separation from the concrete structure and the washing phase commences, where a washing crew utilizes water jetting to clean the forms. Then, the floating dock is submersed and the constructed caisson is removed, which dictates the commencement of the dismantling phase. The dismantling crew lifts all form units with the use of tower cranes and water jetting is used to clean and refurbish the equipment. Upon completion, the cycle repeats itself by starting with the foundation slab construction. The aforementioned process may be altered in a more complex way in case the floating dock cannot support the construction of the entire caisson in one phase due to restrictions on its bearing capacity. This situation is often met in real life construction projects, since the available floating dry docks on a given time period may not match exactly the demands of the caissons' design characteristics. In this case, slipforming is conducted in two phases: phase A terminates when slipforming is stopped at a certain height, which is specified such as not to exceed the dock's bearing capacity. Then, the caisson is washed and removed from the floating dock and slipforming of the floating caissons (Phase B) continues in the water until the completion of the production cycle. It should be highlighted that the second slipforming phase can only commence during daytime, so the planning objective is for the washing activity to finish on time in order to avoid delays in the process (e.g. if the washing finishes in the evening, then slipforming cannot start until the next morning). Another critical aspect of the two-phase construction process is the fact that the floating dock becomes available right after the completion of the first phase and, hence, the construction of the foundation slab of the next caisson may take place concurrently with the completion of the one under construction, depending on the availability of the required crews.

3. Computer simulation platform development The next paragraphs will demonstrate how the caisson production process described in the previous section has been incorporated within a user-friendly simulation platform called CaissonSim. The tool was developed by using the Stroboscope simulation language and additional coding was written in C#. First, the methodological framework underpinning the model developing process will be presented and, subsequently, the model components will be analyzed in detail.

Fig. 2. Floating caisson production cycle.

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Fig. 3. Methodological framework.

3.1. Methodological framework The research methodology is depicted in Fig. 3 below. First, the model is being set up by the use of the preferred simulation language (e.g. STROBOSCOPE) and graphical formats of the modeled system are produced by deploying EZStrobe [15] modeling elements. The model setup depends on the method statement which determines the type and sequence of the activities involved in the operation under study. Then, the input modeling process initiates which comprises the use of historical validated data or the direct observation of construction activities combined with experts' input (e.g. interview with project manager) to improve the robustness of the created datasets. Subsequently, the sample independence and homogeneity must be assessed. The former is evaluated by the use of ordered plots or scatter diagrams. The latter is a critical parameter of the input modeling process and should be assessed based on the Kruskal–Wallis hypothesis test for homogeneity [25]. The possible distribution functions can be selected along with their parameters based on summary statistics (e.g. quantile summaries, box plots) [10]. The goodness of fit for the selected distribution is being evaluated by the creation of specific graphs (e.g. Q–Q or P–P plots) combined with statistical checks (e.g. Chi-square, Kolmogorov–Smirnov, Anderson–Darling) [26,27]. An iterative process is being initiated until the proper distribution has been defined. After the statistical checks have been successful, it is assured that the model is verified, namely that it actually represents what the developer or engineer had in mind [28]. In the case of failure to verify, the model set-up process must be re-visited to adjust accordingly. Upon verification of the model, simulation runs can be executed by the use of the developed computer simulation platform. First, pilot runs are executed to define the model's behavior. If satisfied, the number of independent replications is determined and the simulation experiment is designed. The results are compared to the actual data and it is evaluated to what extent the abstract model corresponds to the actual situation on-site, i.e. model validation. If the validation results are not satisfactory, new data must be provided to the model, so as to improve its accuracy. Sensitivity analysis

is performed to scrutinize the model's performance under variation of the critical model parameters [18] and, ultimately, decisions are being made regarding the resource deployment and workflow strategies based on the analysis' results. Sometimes, the decision making process requires the examination of alternative scenarios (e.g. different construction methods and techniques). In this case, alterations in the model setup must be induced, so as to represent the variations in the operational setting. A detailed description of the methodological framework's implementation by developing a user-friendly computer simulation tool for the analysis of the floating caisson construction is presented in the following sections. 3.2. Model components The computer simulation tool (CaissonSim) that has been developed in order to simulate and analyze the caisson construction process along with its main components is illustrated in Fig. 4. A detailed description of each module is provided next. 3.2.1. Simulation models library The first part of the model is its input interface, where the user can choose the preferred simulation model from a library of predefined caisson simulation templates (see Fig. 5). Each template represents a different operational setting. In its current state the system contains seven simulation templates as explained below: 1. Models “5.0.1”/“5.0.2”: two-phase construction of caissons on a floating dock. In template “5.0.1” the same concrete and rebar installation crews are utilized for both the construction of the caisson foundation slab and the caisson core. Alternatively, in template “5.0.2” different concrete and rebar installation crews are deployed for the foundation slab and caisson core construction. 2. Models “5.1.1”/“5.1.2”: in this family of templates the simulation model reflects the construction process described above with a change in the workflow strategy: it is assumed that the washing

Fig. 4. Main system components of CaissonSim.

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Fig. 5. User interface in CaissonSim for simulation model definition.

and removing processes between the first and second slipforming phases are executed in a parallel rather than a serial manner. The differentiation in templates “.1” and “.2” is the same as in the previous models. 3. Models “5.2.1”/“5.2.2”: same as above, only the floating dock becomes available upon completion of the caisson core construction. In other words, the construction of the foundation slab for the next caisson cannot commence until the end of the production phase of the previous caisson. Again, the differentiation in templates “.1” and “.2” is the same as in the previous models. 4. Models “5.3.1”: construction of caissons on a floating dock in a single phase, meaning that the caissons are removed from the dock when the entire production cycle has been completed. Deployment of the same concrete crew and rebar installation crew for both the foundation slab and the caisson core construction. The models have been structured in a way that does not add redundant complexity in the operation, without, however, diminishing the accuracy of the modeled operations. The main principle underpinning the model development process was the law of parsimony, according to which simpler of more parsimonious models that describe the system adequately are more preferable than complicated ones that leave little of the variability unexplained [29]. Verification of the models has been undertaken by running test simulations under a variety of settings of the input parameters and evaluation of the results' consistency, whereas the models' face validity has been verified by analyzing the developed models' logic during workshops with subject experts [25]. For every chosen template, the user can define several input parameters such as the project resources (e.g. number of caissons to be

constructed, number of slipforming sets/floating dry docks, caisson concrete volume), activity durations, number of crews and cost data (e.g. labor cost, equipment cost, overheads cost). A built-in expressions editor aids the users in utilizing all predefined mathematical, stochastic, statistical and trigonometric functions acknowledged by Stroboscope for defining model inputs. In addition, basic simulation parameters, such as the number of replications, the seed number, the confidence level and the terminating conditions are also defined by the users, in order to create the preferred experimental framework for the analysis. Each model can be saved for future use either as a separate file or as a new default model for a specific model alternative. 3.2.2. Scenario implementation generator One of the most practical aspects of the developed tool is its capability to create different operational scenarios based on variations of the deployed resources, the assigned task durations or the expected costs prior to the commencement of the simulation experiment. More specifically, the system allows the definition of multiple input values for every resource or input variable comprised in the selected model. For example, Fig. 6 illustrates the user interface for defining three different values for the duration of the activity “Act17”. When completed with the value definition, a scenario generator is run and all possible combinations are created. Every scenario is associated with a unique id number and the varying parameters are saved in the system. In case the number of combinations is too large, or the user would like to narrow the breadth of the analysis, a filter option allows the creation of a scenario subset based on the selected parameter values and simulation is run only for that subset. In this manner, the user defines an explicit experimental framework which clearly determines the scope of the study and provides full

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Fig. 6. User interface in CaissonSim for scenarios generation.

control over the simulation process. In order to enhance the statistical validity of the simulation process, CaissonSim provides the possibility to specify Bin Queues as defined in Stroboscope. 3.2.3. Simulation module The model utilizes Stroboscope as a simulation engine. After the completion of the input modeling and scenario generation phases, the Stroboscope code of the defined models is generated and the simulation is run for the selected number of replications. The generated Stroboscope code is accessible to the user in .txt format for validation and verification purposes. Additional code written in C# language has been added, in order to provide a more detailed control of the simulation. 3.2.4. Reporting module The reporting module is launched when the simulation has been completed. CaissonSim generates three types of reports: (i) Standard report: the standard report contains all necessary statistical information yielded by the simulation experiment. It estimates summary statistics on selected output parameters such as average values, min/max values, standard deviations and confidence intervals (see Fig. 7). An important aspect of CaissonSim's reporting capabilities is denoted by the hyperlinked number of replications in the first column of the standard report: Every replication's statistical output is accessible for all combinations that have been simulated. That is, if the user would like to

explore how a specific replication was executed for validation or verification purposes, it is possible to do so, because the system collects and provides all statistical data for every global variable and modeling element of Stroboscope. (ii) Raw data report: the raw data report contains the same information as the standard report, but they are rearranged in such a way, so as to be easily elaborated for statistical output analysis in a spreadsheet program. (iii) Confidence level report: for the three main output parameters of cost, duration and productivity rate, CaissonSim creates two types of reports, one pessimistic and one optimistic based on a confidence level variation ranging from 10% to 90%. The pessimistic report provides the lower values of the estimated average for the selected output parameters, whereas the optimistic report contains the upper values respectively. In addition, the results can be sorted in ascending or descending order. The rationale is for the pessimistic report to reflect a conservative analysis, which can be utilized in the pre-planning phase of the project in order to determine the worst-case scenarios. Equivalently, the optimistic report can serve as a yardstick for improvement margins, once the operations have started or as a baseline metric for evaluations between the optimum system output capabilities and the actual on-site performance. A case study is presented in the next section demonstrating the applicability of the developed simulation tool within the framework of an actual construction project.

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Fig. 7. CaissonSim standard report format.

4. Framework implementation: case study 4.1. Case description and data collection The project which served as the case study regards the construction of 34 floating caissons by the use of the slipform technique (see Fig. 8). The study covered a period of eight months (January2012–August 2012) and the operations were monitored by the use of direct observation augmented by time-lapse video and expert consultation. In addition, ancillary documentation was utilized to enhance the input modeling process, such as project drawings and contractual documents, daily productivity reports and construction protocols. Productivity field data expressed in workhours/activity's output (e.g. whs/m3 or m etc.) have been collected resulting in a dataset containing 1771 data points from all activities. The next section gives more concise information about the case modeling process. 4.2. Case modeling From the method statement submitted by the contractor it was concluded that the actual operation could be modeled by using CaissonSim's template “5.0.2”. The respective simulation model is illustrated in Fig. 9 and a list of the modeling elements used to create the graphical format of the model together with a brief description is provided in Table A.1, Appendix A.

The floating caissons are constructed in pairs. Due to weight capacity restrictions of the floating barge, when the caissons reach +9.00 m height slipforming stops (Phase A), the barge sinks, the two caissons are towed in the water by a tug boat and the construction continues afloat until it reaches the final elevation (+19.70 m) (Phase B). After the dismantling, the slipform equipment will be available for assembly on the base of the next two caissons. It should be noted that after the completion of each cycle the constructed caissons are tugged into their final position and sunk along the new quay wall, however, this activity is not critical for the model under study and hence was excluded from the analysis. 5. Results and sensitivity analysis As illustrated in Fig. 10, the actual amount of labor hours for slipforming the caissons decreased gradually as the construction process progressed. This variability in the actual system output stems from two reasons: (a) there is a strong learning behavior in view of the repetitive nature of the works and (b) the sliding rate increases incrementally as the temperature rises and experience is gained among the deployed crews. As such, the collected datasets reflect this variability and, hence, cannot be incorporated in the simulation analysis without checking their homogeneity, as explained in Section 3.1. The main drivers of this variation are the slipforming activities and the results of the Kruskal–Wallis tests undertaken for both slipforming phases

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Fig. 8. Caissons fabrication on floating dry dock with slipform equipment.

denoted that merging of datasets would be statistically acceptable only for the following caisson pairs: 1–2, 3–4, 7–8, 9–10, 13–14, 15–16, 17– 18, 21–22, 23–24, 27–28 and 29–30. As such, 23 separate models have been created using CaissonSim's “5.0.2” template and the results of the analysis are presented in the following paragraphs. The developed simulation tool yields valid results as illustrated in Fig. 10. The simulation model tends to slightly overestimate the slipforming activity duration. For the first five caissons, model accuracy

lies in the range of 73-89%, whereas for the rest 25 caissons the average accuracy ranges between 85% and 90% with its maximum value being 97%. Although not reported for brevity reasons, the overall model accuracy in productivity estimation for the whole caisson fabrication process was found to be more than 90%. Indicatively, for the construction of caissons 15–16, template “5.0.2” is modified by specifying the input parameters associated with the construction process. The activity distributions have been defined by statistically fitting actual data to predefined

Fig. 9. Graphical model of “5.0.2” simulation template.

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Slipforming (whs)

3000 2500 2000 1500 1000 500 1

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Number of caissons Actual slipforming workhours

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Fig. 10. Actual caisson slipforming workhours and CaissonSim simulated results.

probability distributions (see Table A.2 in Appendix A) by using the BestFit software package [30] and one crew is utilized in every trade. The average productivity generated by the system after 30 replications for the production of 34 caissons was 1700.80 whs/caisson, as compared to the actual 1676 whs and 1701 whs for caissons 15 and 16 respectively, thus yielding a difference of less than 1.50%. After its validation the developed model was used to examine the effect of the selected variables on the caisson construction operations.

5.1. Workflow sequence effect 5.1.1. Crews deployment effect This section investigates the effect of utilizing the same concrete crews and rebar crews not only for the construction of the caisson core, but also for the construction of the foundation slab on the floating dock, which is represented by template “5.0.1”. Thus, the objective is to quantify the effect of sharing resources between two production cycles: the cycle of the caisson under completion and the cycle of the next caissons commencing on the floating dock. An average productivity rate of 1752.51 whs/caisson is yielded by model “5.0.1”, hence denoting an increase in the required workhours of about 3%. In order to ensure company confidentiality, direct cost data are not going to be reported, however, the results denote that the utilization of separate crews induces an increase of ~11% in the fabrication cost. Despite these facts though, the deployment of separate crews was the preferred solution, because it provided planning flexibility, since the two production cycles could be scheduled independently. In addition, the effect of varying the number of concrete and rebar crews (using one, two or three crews of each trade) was also examined. The scenario generator run all nine combinations for each template and it was found that no gains in productivity would be achieved for model “5.0.2”. Model “5.0.1” became equivalent to “5.0.2”, from a productivity standpoint, when two concrete and two rebar crews were deployed, which is logical given that the extra crew in each trade could be assigned to the foundation slab construction,

whenever the floating dock became available, just like in model “5.0.2”. The increase in costs due to the additional workforce utilization in both models ranged from 6% to 27%. In view of these findings, it was decided to complete the actual caisson construction process with the deployment of one crew per trade. 5.1.2. Parallel activities effect This section examines the effect of task execution sequence by analyzing the system output in the case where the washing and removing activities take place concurrently. Thus, model “5.1.2” is run in CaissonSim and the average calculated production rate is 1686.51 whs/ caisson, which represents a marginal improvement of less than 1%. Given these findings and also taking into account critical health and safety issues raised by the fact that crews would be working on a sinking floating barge, this operational setting, although initially suggested as a candidate alternative by construction operatives, was ultimately not implemented on the project in hand. 5.2. Sliding rate effect The sliding rate is a physical factor and its effect on the caisson fabrication process is reflected through the respective variations of the slipforming activity duration. Regardless if slipform acceleration stems from the learning effect, the improvement of the weather conditions, the decrease of the concrete setting time and the improvement of managerial capabilities, sliding rate is the main operational parameter that quantifies the change in the working process. The sensitivity analysis has been extended to all 34 caisson production cycles and the results are presented in Fig. 11. The chart gives an indication of the quantitative impact of sliding rate on slipforming duration, which implies that slipforming activities for lower sliding rates may take more than 2.5 times longer than the respective duration for sliding rates in the magnitude of 0.28 m/h. This finding gives a clear notion of the estimating risk's magnitude in case this variability is ignored in the planning

Slipforming (whs)

3000 2500 2000 1500 1000 500 0 10

15

20

25

30

Sliding rate (cm/h) Fig. 11. Sliding rate versus caisson slipforming workhours.

Please cite this article as: J.-P. Pantouvakis, A. Panas, Computer simulation and analysis framework for floating caisson construction operations, Automation in Construction (2013), http://dx.doi.org/10.1016/j.autcon.2013.04.003

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process. The derived empirical relationship can be applied for estimating purposes in future operations, provided that the overall contextual framework (type of project, applied construction method) remains unchanged. 5.3. Floating dock availability and capacity effect The floating dock's availability effect is quantified by comparing templates “5.0.2” and “5.2.2”. In the case of caisson 15–16 fabrication, the merit of constructing the next caissons' pair base slab concurrently with the construction finalization of the other two results in an improvement of productivity in the range of 11%. This finding evidently shows the positive effect of the resource utilization strategy on the system's output, since the slipform duration remains the same in both alternatives. In addition, simulation model “5.3.1” demonstrates the system response in case the floating dock has the capacity of bearing both caissons for the whole construction process. In a similar fashion, simulation analysis for caissons 15–16 showed that in this case productivity improves by 3%. Therefore, it seems that the avoidance of work interruption and the removal out of the tank is not as critical to system performance as being able to merge the production cycle of more than two pairs of caissons. 6. Discussion The main research contribution stemming from this study is assessed along three pillars: 1. System modeling: the study has focused on a construction activity that has rarely been investigated in published literature despite their popularity in modern marine infrastructure projects. In fact, the entire caisson fabrication cycle has never been scrutinized in such detail. In addition, the chosen level of abstraction for the system under study is generic enough, so as to allow the utilization of the developed simulation models in different operational settings and for varying production processes, since the main construction methodology principles remain the same. In this manner, the modeling effort undertaken in this research can be extended and applied, after appropriate adjustments, not only in other caisson fabrication operations, but other similar activities in general, such as construction of silos, chimneys, bridge and oil platform pillars and high rise buildings where the slipforming technique is often utilized. 2. Computer platform development: the special purpose simulation tool that has been developed provides a user-friendly environment for automating the simulation analysis mentioned above. The built-in library of simulation models reduces the modeling effort on behalf of the user. Although it could be argued that the tool limits the modeling flexibility since the contained templates are fixed, however the library contains models which represent fundamental managerial actions, such as the change in work sequence, the resource deployment strategy and different construction alternatives that are commonly examined in practice. Furthermore, the combinations and scenario generation module allows the proactive definition of the operational settings to be examined and allows the evaluation of all possible combinations in a statistically explicit and physically understandable way, so as not to confuse the user with overcomplicated analyses. 3. Research methodology: the clear definition of the experimental framework represented by every combination produced by the scenario generator, enables CaissonSim to yield simulation results with an accurate scope of application. Instead of just taking advantage of the large collected databank of measurements, the study followed a strict protocol for investigating the statistical properties of each collected datapoint, so as to ensure that its incorporation in the analysis is not to the detriment of the research validity. Although it is not suggested that this is the only simulation study

to apply such an approach, however the implementation of the reported methodological framework was the main contributor to the valid elaboration of the highly variable collected dataset, as the case study showed. Besides, it should be stressed that applying a deterministic estimation approach on the total dataset, without taking into account the contextual framework as expressed by the intensely varying sliding rate, would lead to elusive estimates, since the large variance of the calculated data would limit both their accuracy as well as their applicability. It is the implementation of the simulation modeling technique combined with the explicit statistical elaboration of the collected datasets that enables the modeling of the stochastic nature of the main operations' driver, namely the slipforming activity.

7. Conclusions and future work This paper presented a simulation analysis framework based on a developed simulation tool focusing on caisson construction operations. The Stroboscope simulation engine was used and seven modeling templates were incorporated in the system's built in library representing different versions of the production process. The model development process was presented and a case study was used to validate its applicability for analyzing real-life, large-scale operations. The system response was studied under different conditions and variables, such as crews, resources and work sequence. The analysis demonstrated that increasing the number of deployed crews does not substantially improve the achieved production rates. In addition, the parallel execution of washing–removing activities within either the two-stage or one-stage construction methods does not bring significant improvements on system performance. Finally, it was shown that from a productivity standpoint it is more efficient for the floating dock to be available for the next caisson slab foundation construction as soon as possible than bearing the capacity to support the one-stage caisson fabrication process, since the former allows the commencement of the next caisson production cycle concurrently with the one already being concluded. On any case, the sliding rate is the main determinant of the slipforming activity and an empirical chart has been derived, that can be useful in estimating slipforming activities durations for future projects, in the absence of past or historical data. The applied methodological framework demonstrated the importance of valid simulation analysis and the magnitude of the risk of generating misleading results which negatively affect the decision making process, as a result of the failure to understand and model dynamically changing operational parameters. Key problems in applying simulation modeling and analysis for construction operations may arise in relation to (1) the proper definition of input data, (2) the correct interpretation of the simulation results and (3) the comparative evaluation of construction alternatives. This research addresses the first aspect by defining strict statistical procedures for analyzing historical data of completed activities and assessing their homogeneity. The second aspect is served by evaluating the system outputs under the prism of varying production parameters which have not been explored before within the context of the caisson fabrication process. The third aspect is realized by the automated generation of alternative scenarios combined with the provision of dynamic reports which contain all statistical data for every model variable and each replication, both of which constitute novel characteristics of the CaissonSim simulation platform. In addition, the study highlighted the importance of automating the planning and analysis of construction processes, through the use of simulation modeling. The research demonstrated that CaissonSim essentially represents an engineering decision making tool which enables the elaboration of automated solutions. Furthermore, the feature of automation is present in all aspects of the developed system, starting with the selection of the appropriate models from the pre-defined library, going over to the automated generation of the implementation scenarios

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J.-P. Pantouvakis, A. Panas / Automation in Construction xxx (2013) xxx–xxx

and concluding with the reporting and the rank-ordering of the analysis' results. The emerging inferences from the case study implicate that the implementation of CaissonSim at a major infrastructure project provided consistency in the productivity analysis and accuracy in the assessment of the key production drivers. In this manner, a valuable historical record of the operations was created, which could lead to performance improvement incentives, if similar activities are to be executed in the future. In total, the developed simulation system is believed to be a practical tool for both academia and practitioners for studying caisson fabrication operations within the context reflected in the model library. The extension of the current system's scope by adding more projects, in order to expand the historical data repository and, hence, be able to improve the accuracy of the estimated results could be potential topics for further elaboration in the future.

Appendix A

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Table A.1 (continued) Element name

Element type

Description

FlDockAvl FormCrew RebarCrew RmvlCrew SCncrCrew SftCrew SRebarCrew WashCrew nAbl nCaisson nCncr nDock nDsmntl nEq nForm nRebar nRmvl nSCncr nSft nSRebar nWash

Queue Queue Queue Queue Queue Queue Queue Queue Input parameter Input parameter Input parameter Input parameter Input parameter Input parameter Input parameter Input parameter Input parameter Input parameter Input parameter Input parameter Input parameter

Floating dock available Formwork crew Reinforcement steel installation crew Removal crew Slipform concrete crew Safety crew Slipform rebar crew Wash crew Number of assembly crews Number of caissons to be constructed Number of concrete crews Number of floating docks Number of dismantling crews Nunber of slipform equipment sets Number of formwork crews Number of reinforcement steel crews Number of removal crews Number of slipform concrete crews Number of safety crews Number of slipform rebar crews Number of washing crews

Table A.1 Modeling elements and their description for simulation template “5.0.2”. Element name

Element type

Description

Act1 (Act1Dur) Act2 (Act2Dur)

Combi (Input) Combi (Input)

Act3 (Act3Dur) Act4 (Act4Dur)

Combi (Input) Combi (Input)

Act5 (Act5Dur) Act6 (Act6Dur)

Combi (Input) Combi (Input)

Act7 (Act7Dur)

Combi (Input)

Act8 (Act8Dur) Act9 (Act9Dur) Act10 (Act10Dur) Act11 (Act11Dur) Act12 (Act12Dur) Act13 (Act13Dur) Act14 (Act14Dur)

Combi (Input) Normal (Input) Combi (Input) Combi (Input) Combi (Input) Normal (Input) Combi (Input)

Act15 (Act15Dur)

Combi (Input)

Act16 (Act16Dur) Act17 (Act17Dur) Act18 (Act18Dur) Act19 (Act19Dur) q1 q2 q3

Combi (Input) Normal (Input) Combi (Input) Combi (Input) Queue Queue Queue

q4 q5

Queue Queue

q6

Queue

q7 q8 q9 q10 q11

Queue Queue Queue Queue Queue

q12

Queue

q13 q14 q15 AblCrew BeginSim CncrCrew DsmntlCrew EqAvl

Queue Queue Queue Queue Queue Queue Queue Queue

Base formwork construction (duration) Base reinforcement bars installation (duration) Concrete base pouring (duration) Secondary reinforcement steel bars installation (duration) Assembly (duration) Prepare for initial concreting — Phase A (duration) Safety check before slipform — Phase A (duration) Initial concreting Phase A (duration) Slipform Phase A (duration) Freelifting Phase A (duration) Washing (duration) Removal (duration) Night delay (duration) Prepare for initial concreting — Phase B (duration) Safety check before slipform — Phase B (duration) Initial concreting Phase B (duration) Slipform Phase B (duration) Freelifting Phase B (duration) Dismantling (duration) Base reinforcement bars ready Concrete base pouring ready Secondary reinforcement steel bars ready for slipform Phase A Assembly ready Ready for initial concreting preparation — Phase A Ready for safety check before slipform — Phase A Initial concreting Phase A ready Freelifting Phase A ready Washing ready Removal ready Ready for initial concreting preparation — Phase B Ready for safety check before slipform — Phase B Initial concreting Phase B ready Freelifting Phase B ready Dismantling ready Assembly crew Start simulation Concrete crew Dismantling crew Slipform equipment available

Table A.2 Input distributions for the simulation of the construction process for caissons 15 and 16. Activity name

Input duration

Act1 (Act1Dur) Act2 (Act2Dur) Act3 (Act3Dur) Act4 (Act4Dur) Act5 (Act5Dur) Act6 (Act6Dur) Act7 (Act7Dur) Act8 (Act8Dur) Act9 (Act9Dur) Act10 (Act10Dur) Act11 (Act11Dur) Act12 (Act12Dur) Act13 (Act13Dur) Act14 (Act14Dur) Act15 (Act15Dur) Act16 (Act16Dur) Act17 (Act17Dur) Act18 (Act18Dur) Act19 (Act19Dur)

Uniform [40,50] Uniform [0.96,1.00]*81 Uniform[0.18,0.22]*241,80 Triangular[48,50,52] Uniform[80,140] Uniform[2.17, 24.50] 1 Uniform[25, 52] Triangular[41.708, 52, 82.009]*9.00 Uniform[42, 55]*0.50 Uniform[12,15] Uniform[35,45] Uniform[8,10] Uniform[2,4] 1 Uniform[170, 385] ScaledBeta[51.333, 167.08, 0.68838, 4.119]*10.70 Uniform[27, 35]*0.50 Uniform[80,133]

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Please cite this article as: J.-P. Pantouvakis, A. Panas, Computer simulation and analysis framework for floating caisson construction operations, Automation in Construction (2013), http://dx.doi.org/10.1016/j.autcon.2013.04.003