Reliability assessment of concrete bridges subject to corrosion-induced cracks during life cycle using artificial neural networks

2013 ◽  
Vol 12 (1) ◽  
pp. 91-107 ◽  
Author(s):  
Afshin Firouzi ◽  
Alireza Rahai
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Life Cycle ◽  
Reliability Assessment ◽  
Concrete Bridges ◽  
Artificial Neural

scholarly journals Artificial neural networks incorporating cost significant Items towards enhancing estimation for (life-cycle) costing of construction projects

2013 ◽  
Vol 13 (3) ◽  
pp. 51-64 ◽  
Author(s):  
Ayedh Alqahtani ◽  
Andrew Whyte
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Life Cycle ◽  
Life Cycle Cost ◽  
Construction Projects ◽  
Back Propagation ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Excel Solver

Industrial application of life-cycle cost analysis (LCCA) is somewhat limited, with techniques deemed overly theoretical, resulting in a reluctance to realise (and pass onto the client) the advantages to be gained from objective (LCCA) comparison of (sub)component material specifications. To address the need for a user-friendly structured approach to facilitate complex processing, the work described here develops a new, accessible framework for LCCA of construction projects; it acknowledges Artificial Neural Networks (ANNs) to compute the whole-cost(s) of construction and uses the concept of cost significant items (CSI) to identify the main cost factors affecting the accuracy of estimation. ANNs is a powerful means to handle non-linear problems and subsequently map between complex input/output data, address uncertainties. A case study documenting 20 building projects was used to test the framework and estimate total running costs accurately. Two methods were used to develop a neural network model; firstly a back-propagation method was adopted (using MATLAB SOFTWARE); and secondly, spread-sheet optimisation was conducted (using Microsoft Excel Solver). The best network was established as consisting of 19 hidden nodes, with the tangent sigmoid used as a transfer function of NNs model for both methods. The results find that in both neural network models, the accuracy of the developed NNs model is 1% (via Excel-solver) and 2% (via back-propagation) respectively.

Composite Reliability Assessment Based on Monte Carlo Simulation and Artificial Neural Networks

2007 ◽  
Vol 22 (3) ◽  
pp. 1202-1209 ◽  
Author(s):  
Armando M. Leite da Silva ◽  
Leonidas Chaves de Resende ◽  
Luiz AntÔnio da Fonseca Manso ◽  
Vladimiro Miranda
Keyword(s):  
Neural Networks ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Artificial Neural Networks ◽  
Reliability Assessment ◽  
Artificial Neural ◽  
Composite Reliability

Reliability-based life-cycle cost design of asphalt pavement using artificial neural networks

2020 ◽  
pp. 1-15
Author(s):  
Jiyu Xin ◽  
Mitsuyoshi Akiyama ◽  
Dan M. Frangopol ◽  
Mingyang Zhang ◽  
Jianzhong Pei ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Life Cycle ◽  
Life Cycle Cost ◽  
Asphalt Pavement ◽  
Artificial Neural

scholarly journals Rapid Life-Cycle Impact Screening Using Artificial Neural Networks

2017 ◽  
Vol 51 (18) ◽  
pp. 10777-10785 ◽  
Author(s):  
Runsheng Song ◽  
Arturo A. Keller ◽  
Sangwon Suh
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Life Cycle ◽  
Artificial Neural ◽  
Life Cycle Impact
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