• Computers and Concrete
• /
• v.31 no.2
• /
• pp.111-121
• /
• 2023

The influence of material composition such as aggregate types, addition of supplementary cementitious materials as well as exposed temperature levels have significant impacts on concrete residual mechanical strength properties when exposed to elevated temperature. This study is based on data obtained from literature for fly ash blended concrete produced with natural and recycled concrete aggregates to efficiently develop prediction models for estimating its residual compressive strength after exposure to high temperatures. To achieve this, an extensive database that contains different mix proportions of fly ash blended concrete was gathered from published articles. The specific design variables considered were percentage replacement level of Recycled Concrete Aggregate (RCA) in the mix, fly ash content (FA), Water to Binder Ratio (W/B), and exposed Temperature level. Thereafter, a simplified mathematical equation for the prediction of concrete's residual compressive strength using Gene Expression Programming (GEP) was developed. The relative importance of each variable on the model outputs was also determined through global sensitivity analysis. The GEP model performance was validated using different statistical fitness formulas including R², MSE, RMSE, RAE, and MAE in which high R² values above 0.9 are obtained in both the training and validation phase. The low measured errors (e.g., mean square error and mean absolute error are in the range of 0.0160 - 0.0327 and 0.0912 - 0.1281 MPa, respectively) in the developed model also indicate high efficiency and accuracy of the model in predicting the residual compressive strength of fly ash blended concrete exposed to elevated temperatures.

• Computers and Concrete
• /
• v.15 no.2
• /
• pp.167-181
• /
• 2015

"Mortar or concrete pneumatically projected at high velocity onto a surface" is called Shotcrete. Models that predict shotcrete design parameters (e.g. compressive strength, slump etc) from any mixing proportions of admixtures could save considerable experimentation time consumed during trial and error based procedures. Artificial Neural Network (ANN) has been widely used for similar purposes; however, such models have been rarely applied on shotcrete design. In this study 19 samples of shotcrete test panels with varying quantities of water, steel fibers and silica fume were used to determine their slump, cost and compressive strength at different ages. A number of 3-layer Back propagation Neural Network (BPNN) models of different network architectures were used to train the network using 15 samples, while 4 samples were randomly chosen to validate the model. The predicted compressive strength from linear regression lacked accuracy with $R^2$ value of 0.36. Whereas, outputs from 3-5-3 ANN architecture gave higher correlations of $R^2$ = 0.99, 0.95 and 0.98 for compressive strength, cost and slump parameters of the training data and corresponding $R^2$ values of 0.99, 0.99 and 0.90 for the validation dataset. Sensitivity analysis of output variables using ANN can unfold the nonlinear cause and effect relationship for otherwise obscure ANN model.

• Journal of Information Technology Applications and Management
• /
• v.11 no.4
• /
• pp.35-47
• /
• 2004

The nuclear design analysis requires time-consuming and erroneous model-input preparation. code run. output analysis and quality assurance process. To reduce human effort and improve design quality and productivity. Innovative Design Processor (IDP) is being developed. Two basic principles of IDP are the document-oriented desigll and the web-based design. The document-oriented design is that. if the designer writes a design document called active document and feeds it to a special program. the final document with complete analysis. table and plots is made automatically. The active documents can be written with Microsoft Word or created automatically on the web. which is another framework of IDP. Using the proper mix-up of server side and client side programming under the LAMP (Linux/Apache/MySQL/PHP) environment. it e design process on the web is modeled as a design wizard style so that even a novice designer makes the design document easily. This automation using the IDP is now being implemented for all the reload design of Korea Standard Nuclear Power Plant (KSNP) type PWRs. The introduction of this process will allow large reduction in all reload design efforts of KSNP and provide a platform for design and R&D tasks of KNFC.

• Proceedings of the Korea Concrete Institute Conference
• /
• 1999.04a
• /
• pp.123-128
• /
• 1999

Recently, as a performance based design concept is introduced, assurance of expected performances on serviceability and safety in the whole span of life is exactly requested. So, quantitative assessments about durability related properties of concrete in early-age long term are come to necessary, Especially in early age, deterioration which affects long-term durability performance can be occurred by hydration heat and shrinkage, so development of reasonable hydration heat model which can simulate early age behavior is necessary. The micor-pore structure formation property also affects shrinkage behavior in early age and carbonations and chloride ion penetration characteristic in long term, So, for the quantitative assessment on durability performance of concrete, modelings of early age concrete based on hydration process and micor-pore structure formation characteristics are important. In this paper, a micromechanics based hydration heat evolution model is adopted and a quantitative model which can simulate micro-pore structure development is also verified with experimental results. The models can be used effectively to simulate the early-age behavior of concrete composed of different mix proportions.

• Korean Journal of Construction Engineering and Management
• /
• v.15 no.2
• /
• pp.53-61
• /
• 2014

Previous studies reveal the need for a tool to cost estimation of building design in early design stages. This paper proposes an internal finishes cost model tool to address this need. The tool allows users to evaluate the functionality, economics and quality of finishes concurrently with high-rise building design. Lack of information in the early stages of the project enables a relatively accurate estimates of work to raise up. Measurements are automatically extracted from simple design information and profile driven estimates are revised in real-time. The data model uses a flexible unit rate system that can easily be extended to other estimate dimensions such as mix-use building surcharge rate estimation. The approach illustrated in this paper is applicable to BIM tool conceptual estimation that support for massing purposes other than the one chosen for this study.

• Computers and Concrete
• /
• v.22 no.4
• /
• pp.419-437
• /
• 2018

The compressive strength of self-compacting concrete (SCC) containing fly ash (FA) is highly related to its constituents. The principal purpose of this paper is to investigate the efficiency of hybrid fuzzy radial basis function neural network with biogeography-based optimization (FRBFNN-BBO) for predicting the compressive strength of SCC containing FA based on its mix design i.e., cement, fly ash, water, fine aggregate, coarse aggregate, superplasticizer, and age. In this regard, biogeography-based optimization (BBO) is applied for the optimal design of fuzzy radial basis function neural network (FRBFNN) and the proposed model, implemented in a MATLAB environment, is constructed, trained and tested using 338 available sets of data obtained from 24 different published literature sources. Moreover, the artificial neural network and three types of radial basis function neural network models are applied to compare the efficiency of the proposed model. The statistical analysis results strongly showed that the proposed FRBFNN-BBO model has good performance in desirable accuracy for predicting the compressive strength of SCC with fly ash.

• Journal of information and communication convergence engineering
• /
• v.7 no.2
• /
• pp.107-112
• /
• 2009

Renewable energy resources will be an increasingly important part of power generation in the new millennium. Besides assisting in the reduction of the emission of greenhouse gases, they add the much needed flexibility to the energy resource mix by decreasing the dependence on fossil fuels. Due to their modular characteristics, ease of installation and because they can be located closer to the user, PV system have great potential as distributed power source to the utilities. In this paper, a dc-de power converter scheme with the push-pull based technology is proposed to apply for solar power system which has many features such as high efficiency, stable output, and low acoustic noises, DC-DC converter is used in proposed topology has stable efficiency curve at all load range and very high efficiency characteristics. This paper presents the design of a single-phase photovoltaic inverter model and the simulation of its performance.

• Structural Engineering and Mechanics
• /
• v.90 no.4
• /
• pp.403-415
• /
• 2024

We looked at how the damping qualities of epoxy composites changed when different amounts of graphite nanoplatelets (GNP) were added, from 0% to 6% by weight. A mix of free and forced vibration tests helped us find the key GNP content that makes the damper ability better the most. We also created a Representative Volume Element (RVE) model to guess how the alloys would behave mechanically and checked these models against testing data. An Artificial Neural Network (ANN) was also used to guess how these compounds would react to motion. With proper hyperparameter tweaking, the ANN model showed good correlation (R²=0.98) with actual data, indicating its ability to predict complex material behavior. Combining these methods shows how GNPs impact epoxy composite mechanical properties and how machine learning might improve material design. We show how adding GNPs to epoxy composites may considerably reduce vibration. These materials may be used in industries that value vibration damping.

• Journal of Applied Reliability
• /
• v.17 no.4
• /
• pp.332-342
• /
• 2017

Purpose: In this study, the lifetime distribution of a k-out-of-n system with heterogeneous components is suggested as Markov model, and the time-to-failure (TTF) distribution of each component is considered as phase-type distribution (PHD). Furthermore, based on the model, a redundancy allocation problem with a mix of components (RAPMC) is proposed. Methods: The lifetime distribution model for the system is formulated by the structured Markov chain. From the model, the various information on the system lifetime can be ascertained by the matrix-analytic (or-geometric) method. Conclusion: By the generalization of TTF distribution (PHD) and the consideration of heterogeneous components, the lifetime distribution model can delineate many real systems and be exploited for developing system operation policies such as preventive maintenance, warranty. Moreover, the effectiveness of the proposed RAPMC is verified by numerical experiments. That is, under the equivalent design conditions, it presented a system with higher reliability than RAP without component mixing (RAPCM).

• Computers and Concrete
• /
• v.27 no.5
• /
• pp.489-512
• /
• 2021

In this study, two powerful techniques, namely particle swarm optimization (PSO) and imperialist competitive algorithm (ICA) were selected and combined with a pre-developed ANN model aiming at improving its performance prediction of the compressive strength of concrete modified with fly ash. To achieve this study's aims, a comprehensive database with 379 data samples was collected from the available literature. The output of the database is the compressive strength (CS) of concrete samples, which are influenced by 9 parameters as model inputs, namely those related to mix composition. The modeling steps related to ICA-ANN (or neuro-imperialism) and PSO-ANN (or neuro-swarm) were conducted through the use of several parametric studies to design the most influential parameters on these hybrid models. A comparison of the CS values predicted by hybrid intelligence techniques with the experimental CS values confirmed that the neuro-swarm model could provide a higher degree of accuracy than another proposed hybrid model (i.e., neuro-imperialism). The train and test correlation coefficient values of (0.9042 and 0.9137) and (0.8383 and 0.8777) for neuro-swarm and neuro-imperialism models, respectively revealed that although both techniques are capable enough in prediction tasks, the developed neuro-swarm model can be considered as a better alternative technique in mapping the concrete strength behavior.