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Frontiers of Structural and Civil Engineering

Front. Struct. Civ. Eng.    2016, Vol. 10 Issue (4) : 462-471
Reliability analysis on civil engineering project based on integrated adaptive simulation annealing and gray correlation method
Xiao-ping BAI(),Ya-nan LIU
System Engineering Research Institute, School of Management, Xi’an University of Architecture & Technology, Xi’an 710055, China
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Dynamic reliability is a very important issue in reliability research. The dynamic reliability analysis for the project is still in search of domestic and international research in the exploration stage. By now, dynamic reliability research mainly concentrates on the reliability assessment; the methods mainly include dynamic fault tree, extension of event sequence diagram and Monte Carlo simulation, and et al. The paper aims to research the dynamic reliability optimization. On the basis of analysis of the four quality influence factors in the construction engineering, a method based on gray correlation degree is employed to calculate the weights of factors affecting construction process quality. Then the weights are added into the reliability improvement feasible index (RIFI). Furthermore, a novel nonlinear programming mathematic optimization model is established. In the Insight software environment, the Adaptive Simulated Annealing (ASA) algorithm is used to get a more accurate construction subsystem optimal reliability under different RIFI conditions. In addition, the relationship between construction quality and construction system reliability is analyzed, the proposed methods and detailed processing can offer a useful reference for improving the construction system quality level.

Keywords civil engineering      dynamic reliability      grey relational degree      adaptive simulated annealing algorithm     
Corresponding Authors: Xiao-ping BAI   
Online First Date: 16 November 2016    Issue Date: 29 November 2016
 Cite this article:   
Xiao-ping BAI,Ya-nan LIU. Reliability analysis on civil engineering project based on integrated adaptive simulation annealing and gray correlation method[J]. Front. Struct. Civ. Eng., 2016, 10(4): 462-471.
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Xiao-ping BAI
Ya-nan LIU
first class indicators second class indicators
human factors constructors’ technical management
the supervisions’ work depth and breadth
suppliers’ quality
supervisory personnel’s power
material factors building material quality
equipment materials quality
method factors construction technology and construction operation level
construction management level
engineering contracting way
mechanical factors mechanical quality level
machinery advanced degree
Tab.1  Quality affecting factor indicators
factors expert1 expert2 expert3 expert4
human 5 6 7 7
material 7 8 6 6
method 8 8 5 7
mechanical 9 8 7 8
Tab.2  Experts’ evaluation of quality affecting factors
factor expert1 expert2 expert3 expert4
Δ41 human 0.14 0.08 0 0.04
Δ42 material 0.07 0 0.04 0.08
Δ43 method 0.03 0 0.08 0.04
Δ44 mechanical 0 0 0 0
Tab.3  Sequence difference
factor expert1 expert2 expert3 expert4
human 0.33 0.47 1 0.64
material 0.5 1 0.64 0.47
method 0.7 1 0.47 0.64
mechanical 1 1 1 1
Tab.4  Grey correlation degree
the range of RIFI evaluation relations
RIFI≥0.8 excellent
0.7≤RIFI≤0.79 Very good
0.5≤RIFI≤0.69 good
0.2≤RIFI≤0.49 general
RIFI<0.2 bad
Tab.5  Evaluations of RIFI relations
Fig.1  Construction subsystem reliability relationship
No. work package basic cost index T(i) (CNY)
1 steel bar tying 700
2 concrete structure form 870
3 concrete ratio test 900
4 concrete manufacturing 850
5 concrete casting 720
6 concrete curing 730
Tab.6  Work packages and basic cost indexes of the case study
input variable distribution
human factors weight [0.1–0.3] uniform distribution
material factor weight [0.1–0.3] uniform distribution
mechanical factor weight [0.2–0.4] uniform distribution
method factor weight [0.1–0.3] uniform distribution
environmental factors weight [0.1–0.3] uniform distribution
Tab.7  Input variable distribution
Input variable main effect index sequence total effect index sequence
human factors weight 0.2462 5 0.3778 5
material factor weight 0.1781 4 0.2596 4
mechanical factor weight 0.0665 3 0.0467 3
method factor weight 0.0449 2 0.0230 1
environmental factors weight 0.0357 1 0.0336 2
Tab.8  Sensitivity sequence
parameter distribution
the biggest cost constraints [4000-6000] Uniform distribution
every job cost under the worst conditions [700-1000] Uniform distribution
Tab.9  Parameter distribution
input variable main effect index sequence total effect index sequence
human factors weight 0.2908 7 0.2793 6
material factor weight 0.1123 6 0.2834 7
mechanical factor weight 0.0390 5 0.0338 3
method factor weight 0.0388 4 0.0450 4
environmental factors weight 0.0269 3 0.0470 5
the biggest cost constraints 0.0055 1 0.0021 2
every job cost under the worst conditions 0.0089 2 0.0019 1
Tab.10  Main effect and total effect index and sequence results
algorithm type simulated annealing algorithm genetic algorithm artificial neural network algorithm ant colony algorithm
characteristic good robustness less constraints steady-state good parallelism
wide range of adaptability large convergence probability simple construction good robustness
less constraints high adaptive ability quick convergence self-organizing system
applicable to nonlinear programming problem strong single point search ability low climbing capacity good positive feedback ability
Tab.11  Algorithm compares
Fig.2  improved ASA algorithm steps
Fig.3  optimization schematic
R(i)/RIFI 0.2 0.5 0.7 0.8
R (1) 0.90529 0.94778 0.92794 0.93740
R (2) 0.92031 0.90952 0.89977 0.82656
R (3) 0.87854 0.94371 0.94937 0.94604
R (4) 0.87245 0.88420 0.91044 0.90110
R (5) 0.92540 0.94479 0.94762 0.94521
R (6) 0.93468 0.93280 0.94613 0.93480
F (R) Max 0.84513 0.87147 0.88606 0.89320
Tab.12  The reliability results in the different RIFI condition
Fig.4  work reliability
Fig.5  system reliability
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