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Frontiers of Engineering Management

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Front. Eng    2021, Vol. 8 Issue (1) : 109-121    https://doi.org/10.1007/s42524-019-0084-6
RESEARCH ARTICLE
Modeling and simulating the impact of forgetting and communication errors on delays in civil infrastructure shutdowns
Zhe SUN1, Cheng ZHANG3, Pingbo TANG2()
1. School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ 85281, USA
2. Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburg, PA 15213, USA
3. The Zachry Department of Civil Engineering, Texas A&M University, College Station, TX 77843, USA
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Abstract

Handoff processes during civil infrastructure operations are transitions between sequential tasks. Typical handoffs constantly involve cognitive and communication activities among operations personnel, as well as traveling activities. Large civil infrastructures, such as nuclear power plants (NPPs), provide critical services to modern cities but require regular or unexpected shutdowns (i.e., outage) for maintenance. Handoffs during such an outage contain interwoven workflows and communication activities that pose challenges to the cognitive and communication skills of handoff participants and constantly result in delays. Traveling time and changing field conditions bring additional challenges to effective coordination among multiple groups of people. Historical NPP records studied in this research indicate that even meticulous planning that takes six months before each outage could hardly guarantee sufficient back-up plans for handling various unexpected events. Consequently, delays frequently occur in NPP outages and bring significant socioeconomic losses. A synthesis of previous studies on the delay analysis of accelerated maintenance schedules revealed the importance and challenges of handoff modeling. However, existing schedule representation methods could hardly represent the interwoven communication, cognitive, traveling, and working processes of multiple participants collaborating on completing scheduled tasks. Moreover, the lack of formal models that capture how cognitive, waiting, traveling, and communication issues affect outage workflows force managers to rely on personal experiences in diagnosing delays and coordinating multiple teams involved in outages. This study aims to establish formal models through agent-based simulation to support the analytical assessment of outage schedules with full consideration of cognitive and communication factors involved in handoffs within the NPP outage workflows. Simulation results indicate that the proposed handoff modeling can help predict the impact of cognitive and communication issues on delays propagating throughout outage schedules. Moreover, various activities are fully considered, including traveling between workspaces and waiting. Such delay prediction capability paves the path toward predictive and resilience outage control of NPPs.
Keywords NPP outage      human error      team cognition      handoff modeling     
Corresponding Author(s): Pingbo TANG   
Just Accepted Date: 19 December 2019   Online First Date: 11 March 2020    Issue Date: 15 January 2021
 Cite this article:   
Zhe SUN,Cheng ZHANG,Pingbo TANG. Modeling and simulating the impact of forgetting and communication errors on delays in civil infrastructure shutdowns[J]. Front. Eng, 2021, 8(1): 109-121.
 URL:  
https://academic.hep.com.cn/fem/EN/10.1007/s42524-019-0084-6
https://academic.hep.com.cn/fem/EN/Y2021/V8/I1/109
Fig.1  Framework for assessing the impact of cognitive and communication factor on workflow delays.
Level of human factors Definition Examples References
Individual level Individual cognition reflects the mental action or process of acquiring knowledge and understanding, which includes such aspects as attention, memory, judgment, and evaluation (Von Eckardt, 1995; 2001) Forgetting Love and Li (2000); Nembhard (2000); Nembhard and Bentefouet (2012)
Fatigue Lee and Pena-Mora (2005); Seo et al. (2016); Techera et al. (2018)
Experience level Lloyd (2003); Sambasivan and Soon (2007); Sacks et al. (2013); Scott et al. (2015)
Team level Team cognition reflects the team capabilities when teams are performing cognitive activities as a unit (Cooke, 2015; Cooke et al., 2008) Communication Sambasivan and Soon (2007); Abdel-Monem and Hegazy (2013); Liao et al. (2014)
Team situation awareness Gorman et al. (2006); Demir et al. (2017)
Culture and leadership Fang et al. (2006); Liao et al. (2014); Zhang et al. (2019)
Tab.1  Human factors at the individual/team level during construction workflows
Fig.2  Overall methodology.
Fig.3  Status transition of the worker agent.
Fig.4  Status transition of the supervisor agent.
Fig.5  Overall workflow for Scenario 1.
Fig.6  Pump maintenance workflow in cooling systems.
Task ID As-planned task Locations Resources As-planned durations (min)
Task 1 Visual inspection Site #1 to #9 Inspector 20–40
Task 2 Maintenance Site #1 to #9 Mechanic 30–60
Task 3 Functional surveillance testing Site #1 to #9 Tester 30–60
Tab.2  Detailed task information in the pump maintenance workflow
Forgetting curve Memory decay speed (β) References
P = αe−βt 0.01 Loftus (1985)
0.05 Loftus (1985)
0.1 Anderson and Tweney (1997)
0.2 Anderson and Tweney (1997)
0.5 Anderson and Tweney (1997)
1.0 Anderson and Tweney (1997)
Tab.3  Forgetting curves from previous studies (α = 1.0)
Fig.7  Overall workflow for Scenario 2.
Fig.8  Valve maintenance workflow.
Task ID Task name Location Resource Avg. task duration (min)
Task 1 Remove the insulation from the valve Site A/B Insulators 30
Task 2 De-term the motor operator Site A/B Electricians 45
Task 3 Maintain the valve Site A/B Mechanics 60
Task 4 Re-term the motor operator Site A/B Electricians 45
Task 5 Re-install the insulation Site A/B Insulators 30
Tab.4  Detailed task information in a typical valve maintenance workflow
Fig.9  Indoor workspace setup for handoff processes.
Worker Enter/Exist containment Sequences of station visiting
Insulator Enter 4 → 1 → 2 → 3
Exit 1 → 2 → 4
Electrician Enter 4 → 2 → 1 → 3
Exit 1 → 4
Mechanic Enter 4 → 2 → 3
Exit 1 → 2
Tab.5  Handoff processes in the indoor work environment
Task name Station Resource Avg. task duration: Enter (min) Avg. task duration: Exit (min)
Dosimetry checking Station 1 Insulator/Electrician/Mechanic 5/5/NA 5/5/5
Pick-up/Drop-off tools Station 2 Insulator/Electrician/Mechanic 5/10/15 3/NA/5
Technical debrief Station 3 Insulator/Electrician/Mechanic 5/10/15 NA
Check work packages Station 4 Insulator/Electrician/Mechanic 5/5/5 3/3/NA
Tab.6  Tasks during handoff for valve maintenance
Forgetting curve Memory decay speed (β) Probability of workflow failure Delays (%)
P = αe−βt
(α = 1.0)		 0.01 0.14 6.72
0.05 0.51 12.77
0.1 0.71 17.14
0.2 0.80 26.22
0.5 0.96 54.12
1.0 0.98 68.24
Tab.7  Impact of forgetting errors on the delays of pump maintenance workflow
Task Worker Late report (Delayed time: min) Trial
Task 1 (A) Insulator 2.6 2
Task 3 (A) Mechanic 1.5 2
Task 4 (A) Electrician 1.8 7
Task 1 (A) Insulator 0.5 12
Task 3 (B) Mechanic 3.0 14
Task 4 (A) Electrician 1.2 18
Task 1 (B) Insulator 0.8 18
Tab.8  Late-report captured during the laboratory experiments
Site Task Worker As-planed task duration (min) Late report (min) Workflow duration (min) Workflow delays (min) Probability
A Task 1 Insulator 30 60 597 29 5.10%
Task 2 Electrician 45 75 584 16 2.82%
Task 3 Mechanic 60 90 577 9 1.58%
Task 4 Electrician 45 75 585 17 2.99%
Task 5 Insulator 30 60 568 0 0%
B Task 1 Insulator 30 60 577 9 1.58%
Task 2 Electrician 45 75 570 2 0.35%
Task 3 Mechanic 60 90 586 18 3.17%
Task 4 Electrician 45 75 598 30 5.28%
Task 5 Insulator 30 60 568 0 0%
Tab.9  The sensitivity of the 30-min late-report of each task
1 M Abdel-Monem, T Hegazy (2013). Enhancing construction as-built documentation using interactive voice response. Journal of Construction Engineering and Management, 139(7): 895–898
https://doi.org/10.1061/(ASCE)CO.1943-7862.0000648
2 S AbouRizk (2010). Role of simulation in construction engineering and management. Journal of Construction Engineering and Management, 136(10): 1140–1153
https://doi.org/10.1061/(ASCE)CO.1943-7862.0000220
3 S M Abourizk, S A Hague, R Ekyalimpa (2016). Construction Simulation: An Introduction Using Simphony. Edmonton, Canada: University of Alberta
4 H Alzraiee, T Zayed, O Moselhi (2013). Assessment of construction operations productivity rate as computed by simulation models. In: Proceedings of the 2013 Winter Simulation Conference: Simulation—Making Decisions in A Complex World. IEEE Press, 3225–3236
5 H Alzraiee, T Zayed, O Moselhi (2015). Dynamic planning of construction activities using hybrid simulation. Automation in Construction, 49: 176–192
6 R B Anderson, R D Tweney (1997). Artifactual power curves in forgetting. Memory & Cognition, 25(5): 724–730
https://doi.org/10.3758/BF03211315 pmid: 9337590
7 I A Chaudhry, A A Khan (2016). A research survey: Review of flexible job shop scheduling techniques. International Transactions in Operational Research, 23(3): 551–591
https://doi.org/10.1111/itor.12199
8 N J Cooke (2015). Team cognition as interaction. Current Directions in Psychological Science, 24(6): 415–419
9 N J Cooke, J C Gorman, C W Myers, J L Duran (2013). Interactive team cognition. Cognitive Science, 37(2): 255–285
10 N J Cooke, J C Gorman, J L Winner (2008). Team cognition. In: Durso F T, Nickerson R S, Dumais S T, Lewandowsky S, Perfect T J, eds. Handbook of Applied Cognition, 2nd ed. West Sussex: John Wiley & Sons
11 N J Cooke, E E Salas, P A Kiekel, B Bell (2005). Advances in measuring team cognition. In: Salas E, Fiore S M, eds. Team Cognition: Understanding the Factors that Drive Process and Performance, 83–106
12 M Demir, N J McNeese, N J Cooke (2017). Team situation awareness within the context of human-autonomy teaming. Cognitive Systems Research, 46: 3–12
13 D Fang, Y Chen, L Wong (2006). Safety climate in construction industry: A case study in Hong Kong. Journal of Construction Engineering and Management, 132(6): 573–584
14 S Germain (2015). Use of collaborative software to improve nuclear power plant outage management. In: International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human Machine Interface Technologies. Charlotte, NC, US: Department of Energy Office of Nuclear Energy, 608–615
15 S Germain, R K Farris, A M Whaley, H D Medema, D Gertman I (2014). Guidelines for implementation of an advanced outage control center to improve outage coordination, problem resolution, and outage risk management. Technical Report. Idaho National Laboratory
16 J C Gorman, N J Cooke, J L Winner (2006). Measuring team situation awareness in decentralized command and control environments. Ergonomics, 49(12–13): 1312–1325
https://doi.org/10.1080/00140130600612788 pmid: 17008258
17 J C Gorman, E E Hessler, P G Amazeen, N J Cooke, S M Shope (2012). Dynamical analysis in real time: Detecting perturbations to team communication. Ergonomics, 55(8): 825–839
https://doi.org/10.1080/00140139.2012.679317 pmid: 22533819
18 S M H Hadavi (2008). Risk-based, genetic algorithm approach to optimize outage maintenance schedule. Annals of Nuclear Energy, 35(4): 601–609
https://doi.org/10.1016/j.anucene.2007.08.011
19 G Heo, J Park (2010). A framework for evaluating the effects of maintenance-related human errors in nuclear power plants. Reliability Engineering and System Safety, 95(7): 797–805
https://doi.org/10.1016/j.ress.2010.03.001
20 D Hu, Y Mohamed (2010). State-based simulation mechanism for facilitating project schedule updating. In: Construction Research Congress 2010: Innovation for Reshaping Construction Practice, 369–378
21 S H Jan, H P Tserng, S P Ho (2013). Enhance construction visual as-built schedule management using BIM technology. International Journal of Civil Science and Engineering, 7(11): 812–817
22 I Jang, A Ryum, M Ali, S Al, S Jun, H Gook, P Hyun (2013). An empirical study on the basic human error probabilities for NPP advanced main control room operation using soft control. Nuclear Engineering and Design, 257: 79–87
23 J Kim, J Park (2012). Reduction of test and maintenance human errors by analyzing task characteristics and work conditions. Progress in Nuclear Energy, 58: 89–99
https://doi.org/10.1016/j.pnucene.2012.02.002
24 N Kundakcı, O Kulak (2016). Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem. Computers & Industrial Engineering, 96: 31–51
https://doi.org/10.1016/j.cie.2016.03.011
25 S H Lee, F Pena-Mora (2005). System dynamics approach for error and change management in concurrent design and construction. In: Proceedings of the 37th Winter Simulation Conference. IEEE, 1508–1514
26 P C Liao, G Lei, D Fang, W Liu (2014). The relationship between communication and construction safety climate in China. KSCE Journal of Civil Engineering, 18(4): 887–897
https://doi.org/10.1007/s12205-014-0492-4
27 R L Lloyd (2003). A survey of crane operating experience at US nuclear power plants from 1968 through 2002. Division of Systems Analysis and Regulatory Effectiveness, Office of Nuclear Regulatory Research, US Nuclear Regulatory Commission
28 G R Loftus (1985). Evaluating forgetting curves. Journal of Experimental Psychology: Learning, Memory, and Cognition, 11(2): 397–406
29 P E D Love, H Li (2000). Quantifying the causes and costs of rework in construction. Construction Management and Economics, 18(4): 479–490
https://doi.org/10.1080/01446190050024897
30 M Lu (2003). Simplified discrete-event simulation approach for construction simulation. Journal of Construction Engineering and Management, 129(5): 537–546
https://doi.org/10.1061/(ASCE)0733-9364(2003)129:5(537)
31 A R McKendall, J S Noble, C M Klein (2008). Scheduling maintenance activities during planned outages at nuclear power plants. International Journal of Industrial Engineering: Theory, Applications and Practice, 15(1): 53–61
32 V Neboyan, L Lederman (1987). Human factors in the operation of nuclear power plants improving the way man and machines work together. International Atomic Energy Agency Bulletin, 29(4): 27–30
33 D A Nembhard (2000). The effects of task complexity and experience on learning and forgetting: A field study. Human Factors: The Journal of the Human Factors and Ergonomics Society, 42(2): 272–286
34 D A Nembhard, F Bentefouet (2012). Parallel system scheduling with general worker learning and forgetting. International Journal of Production Economics, 139(2): 533–542
https://doi.org/10.1016/j.ijpe.2012.05.024
35 P Promsorn, P Soponsakulrat, C Adulyanukosol, P Kaiyarit, T Chinda (2015). Identifying root causes of construction accidents: Non-human error factors. International Journal of Computing, Communications & Instrumentation Engineering, 2(1): 1–5
36 P Pyy (2001). An analysis of maintenance failures at a nuclear power plant. Reliability Engineering & System Safety, 72(3): 293–302
https://doi.org/10.1016/S0951-8320(01)00026-6
37 A Rozinat, M T Wynn, W M P van der Aalst, A H M ter Hofstede, C J Fidge (2009). Workflow simulation for operational decision support. Data and Knowledge Engineering, 68(9): 834–850
38 R Sacks, A Perlman, R Barak (2013). Construction safety training using immersive virtual reality. Construction Management and Economics, 31(9): 1005–1017
https://doi.org/10.1080/01446193.2013.828844
39 M Sambasivan, Y W Soon (2007). Causes and effects of delays in Malaysian construction industry. International Journal of Project Management, 25(5): 517–526
https://doi.org/10.1016/j.ijproman.2006.11.007
40 M J Scott, S C Guntuku, Y Huan, W Lin, G Ghinea (2015). Modelling human factors in perceptual multimedia quality: On the role of personality and culture. In: Proceedings of the 23rd ACM International Conference on Multimedia, 481–490
41 J O Seo, S H Lee, J Seo (2016). Simulation-based assessment of workers’ muscle fatigue and its impact on construction operations. Journal of Construction Engineering and Management, 142(11): 04016012–04016063
https://doi.org/10.1061/(ASCE)CO.1943-7862.0001182
42 P R Smart, N R Shadbolt (2012). Modelling the dynamics of team sensemaking: A constraint satisfaction approach. Knowledge Systems for Coalition Operations, 1–10
43 Z Sun, C Zhang, P Tang (2018a). Simulation-based optimization of communication protocols for reducing delays during nuclear power plant outages. In: Construction Research Congress 2018: Infrastructure and Facility Management, 455–464
44 Z Sun, C Zhang, P Tang, Y Wang, Y Liu (2018b). Bayesian network modeling of airport runway incursion occurring processes for predictive accident control. In: Advances in Informatics and Computing in Civil and Construction Engineering. Cham: Springer, 669–676
45 U Techera, M Hallowell, R Littlejohn, S Rajendran (2018). Measuring and predicting fatigue in construction: Empirical field study. Journal of Construction Engineering and Management, 144(8): 04018062
https://doi.org/10.1061/(ASCE)CO.1943-7862.0001513
46 B Von Eckardt (1995). What Is Cognitive Science? Massachusetts: MIT Press
47 B Von Eckardt (2001). Multidisciplinarity and cognitive science. Cognitive Science, 25(3): 453–470
https://doi.org/10.1207/s15516709cog2503_5
48 P H Wakker, F C M Verhagen, J T van Bloois, III W R Sutton (2003). Reducing refueling outage duration by optimizing core design and shuffling sequence. In: Advances in Nuclear Fuel Management III (ANFM 2003). Hilton Head Island, South Carolina, 1–16
49 Y Wang, Y Liu, Z Sun, P Tang (2018). A Bayesian-entropy network for information fusion and reliability assessment of national airspace systems. In: Proceeding of the 10th Annual Conference of the Prognostics and Health Management Society, 10(1)
50 C Zhang, P Tang, N Cooke, V Buchanan, A Yilmaz, S Germain, R Boring, S Akca-Hobbins, A Gupta (2017). Human-centered automation for resilient nuclear power plant outage control. Automation in Construction, 82: 179–192
51 C Zhang, P Tang, A Yilmaz, N Cooke, A Chasey, S Jones (2016). Automatic crane-related workflow control for nuclear plant outages through computer vision and simulation. In: Proceedings of the International Symposium on Automation and Robotics in Construction (ISARC). Auburn, AL: Department of Construction Economics & Property, Vilnius Gediminas Technical University
52 H Zhang, G M Tam, J J Shi (2002). Simulation-based methodology for project scheduling. Construction Management and Economics, 20(8): 667–678
https://doi.org/10.1080/0144619022000014088
53 P Zhang, N Li, Z Jiang, D Fang, C J Anumba (2019). An agent-based modeling approach for understanding the effect of worker-management interactions on construction workers’ safety-related behaviors. Automation in Construction, 97: 29–43
https://doi.org/10.1016/j.autcon.2018.10.015
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