• Interaction and multiscale mechanics
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• v.3 no.1
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• pp.23-37
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• 2010

Concrete is a heterogeneous material consisting of coarse aggregate, mortar matrix and interfacial zones at the meso level. Though studies have been done to interpret the fracture process in concrete using meso level models, not much work has been done for simulating the macroscopic behaviour of reinforced concrete structures using the meso level models. This paper presents a procedure for the mesoscopic analysis of reinforced concrete beams using a modified micro truss model. The micro truss model is derived based on the framework method and uses the lattice meshes for representing the coarse aggregate (CA), mortar matrix, interfacial zones and reinforcement bars. A simple procedure for generating a random aggregate structure is developed using the constitutive model at meso level. The study reveals the potential of the mesoscopic numerical simulation using a modified micro truss model to predict the nonlinear response of reinforced concrete structures. The modified micro truss model correctly predicts the load-deflection behaviour, crack pattern and ultimate load of reinforced concrete beams failing under different failure modes.

• Advances in nano research
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• v.13 no.5
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• pp.499-512
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• 2022

This study investigates the efficiency of ensemble machine learning for predicting the lightweight-aggregate concrete (LWC) characteristics. A stacking ensemble (STEN) approach was proposed to estimate the dry density (DD) and 28 days compressive strength (Fc-28) of LWC using two meta-models called random forest regressor (RFR) and extra tree regressor (ETR), and two novel ensemble models called STEN-RFR and STEN-ETR, were constructed. Four standalone machine learning models including artificial neural network, gradient boosting regression, K neighbor regression, and support vector regression were used to compare the performance of the proposed models. For this purpose, a sum of 140 LWC mixtures with 21 influencing parameters for producing LWC with a density less than 1000 kg/m³, were used. Based on the experimental results with multiple performance criteria, it can be concluded that the proposed STEN-ETR model can be used to estimate the DD and Fc-28 of LWC. Moreover, the STEN-ETR approach was found to be a significant technique in prediction DD and Fc-28 of LWC with minimal prediction error. In the validation phase, the accuracy of the proposed STEN-ETR model in predicting DD and Fc-28 was found to be 96.79% and 81.50%, respectively. In addition, the significance of cement, water-cement ratio, silica fume, and aggregate with expanded glass variables is efficient in modeling DD and Fc-28 of LWC.

• Computers and Concrete
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• v.32 no.4
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• pp.373-381
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• 2023

Fly ash, granulated blast furnace slag, marble waste powder, etc. are just some of the by-products of other sectors that the construction industry is looking to include into the many types of concrete they produce. This research seeks to use surrogate machine learning methods to forecast the compressive strength of self-compacting concrete. The surrogate models were developed using Gradient Boosting Machine (GBM), Support Vector Machine (SVM), Random Forest (RF), and Gaussian Process Regression (GPR) techniques. Compressive strength is used as the output variable, with nano silica content, cement content, coarse aggregate content, fine aggregate content, superplasticizer, curing duration, and water-binder ratio as input variables. Of the four models, GBM had the highest accuracy in determining the compressive strength of SCC. The concrete's compressive strength is worst predicted by GPR. Compressive strength of SCC with nano silica is found to be most affected by curing time and least by fine aggregate.

• Computers and Concrete
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• v.3 no.5
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• pp.301-312
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• 2006

Experimental research revealed that the spatial dispersion of aggregate grains exerts pronounced influences on the mechanical and durability properties of concrete. Therefore, insight into this phenomenon is of paramount importance. Experimental approaches do not provide direct access to three-dimensional spacing information in concrete, however. Contrarily, simulation approaches are mostly deficient in generating packing systems of aggregate grains with sufficient density. This paper therefore employs a dynamic simulation system (with the acronym SPACE), allowing the generation of dense random packing of grains, representative for concrete aggregates. This paper studies by means of SPACE packing structures of aggregates with a Fuller type of size distribution, generally accepted as a suitable approximation for actual aggregate systems. Mean free spacing $\bar{\lambda}$, mean nearest neighbour distance (NND) between grain centres $\bar{\Delta}_3$, and the probability density function of ${\Delta}_3$ are used to characterize the spatial dispersion of aggregate grains in model concretes. Influences on these spacing parameters are studied of volume fraction and the size range of aggregate grains. The values of these descriptors are estimated by means of stereological tools, whereupon the calculation results are compared with measurements. The simulation results indicate that the size range of aggregate grains has a more pronounced influence on the spacing parameters than exerted by the volume fraction of aggregate. At relatively high volume density of aggregates, as met in the present cases, theoretical and experimental values are found quite similar. The mean free spacing is known to be independent of the actual dispersion characteristics (Underwood 1968); it is a structural parameter governed by material composition. Moreover, scatter of the mean free spacing among the serial sections of the model concrete in the simulation study is relatively small, demonstrating the sample size to be representative for composition homogeneity of aggregate grains. The distribution of ${\Delta}_3$ observed in this study is markedly skew, indicating a concentration of relatively small values of ${\Delta}_3$. The estimate of the size of the representative volume element (RVE) for configuration homogeneity based on NND exceeds by one order of magnitude the estimate for structure-insensitive properties. This is in accordance with predictions of Brown (1965) for composition and configuration homogeneity (corresponding to structure-insensitive and structure-sensitive properties) of conglomerates.

• Management Science and Financial Engineering
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• v.20 no.1
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• pp.1-9
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• 2014

In multi-objective scheduling problems, the objectives are usually in conflict. To obtain a satisfactory compromise and resolve the issue of NP-hardness, most existing works have suggested employing meta-heuristic methods, such as genetic algorithms. In this research, we propose a novel data-driven approach for generating a single solution that compromises multiple rules pursuing different objectives. The proposed method uses a data mining technique, namely, random forests, in order to extract the logics of several historic schedules and aggregate those. Since it involves learning predictive models, future schedules with the same previous objectives can be easily and quickly obtained by applying new production data into the models. The proposed approach is illustrated with a simulation study, where it appears to successfully produce a new solution showing balanced scheduling performances.

• Computers and Concrete
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• v.7 no.6
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• pp.497-510
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• 2010

For better estimation of elastic property of concrete composites, the effect of Interfacial Transition Zone (ITZ) has been found to be significant. Numerical concrete composites models have been introduced using Finite Element Method (FEM), where ITZ is modeled as a thin shell surrounding aggregate. Therefore, difficulties arise from the mesh generation. In this study, a numerical concrete composites model in 3D based on FEM and random unit cell method is proposed to calculate elastic modulus of concrete composites with ITZ. The validity of the model has been verified by comparing the calculated elastic modulus with those obtained from other analytical and numerical models.

• Computers and Concrete
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• v.32 no.3
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• pp.327-337
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• 2023

Concrete is the most widely used building material, with various types including high- and ultra-high-strength, reinforced, normal, and lightweight concretes. However, accurately predicting concrete properties is challenging due to the geotechnical design code's requirement for specific characteristics. To overcome this issue, researchers have turned to new technologies like machine learning to develop proper methodologies for concrete specification. In this study, we propose a highly accurate deep learning-based predictive model to investigate the compressive strength (UCS) of lightweight concrete with natural aggregates (pumice). Our model was implemented on a database containing 249 experimental records and revealed that water, cement, water-cement ratio, fine-coarse aggregate, aggregate substitution rate, fine aggregate replacement, and superplasticizer are the most influential covariates on UCS. To validate our model, we trained and tested it on random subsets of the database, and its performance was evaluated using a confusion matrix and receiver operating characteristic (ROC) overall accuracy. The proposed model was compared with widely known machine learning methods such as MLP, SVM, and DT classifiers to assess its capability. In addition, the model was tested on 25 laboratory UCS tests to evaluate its predictability. Our findings showed that the proposed model achieved the highest accuracy (accuracy=0.97, precision=0.97) and the lowest error rate with a high learning rate (R2=0.914), as confirmed by ROC (AUC=0.971), which is higher than other classifiers. Therefore, the proposed method demonstrates a high level of performance and capability for UCS predictions.

• Journal of the Korean Regional Science Association
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• v.11 no.1
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• pp.31-60
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• 1995

The distribution of economic activity over a mutually exclusive and exhaustive categorical industry-region matrix is modeled as a composition of two random components: the probability-like share distribution of jobs and the dynamic evolution of absolute aggregates. The former describes the individual activity location choice by comparing the predicted profitability of the current industry-region pair against that of all other alternatives based on the available information on industry-specific, region specific, or activity specific attributes. The latter describes the time evolution of macro-level aggregates using a dynamic reduced from model. With the seperation of micro choice behavior and macro dynamic aggregate constraint, the usual independence and identicality assumptions become consistent with the activity share distribution, hence multi-regional industrial migration can be represented by a set of probability evolution equations in a conservative Markovian from. We call this a Micro-Macro Composition Approach since the product of the aggregate prediction and the predicted activity share distribution gives the predicted activity distribution gives the predicted activity distribution which explicitly considers the underlying individual choice behavior. The model can be applied to interesting practical problems such as the plant location choice of multinational enterprise, the government industrial ploicy to attract international firms, and the optimal tax-transfer mix to influence activity location choice. We consider the latter as an example.

• Journal of the Korean Ceramic Society
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• v.42 no.4
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• pp.245-250
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• 2005

ZnO was deposited on sapphire single crystal substrate by an ultrasonic pyrolysis of Zinc Acetate Dehydrate (ZAH) with carrying Ar gas. Through Thermogravimetry-Differential Scanning Calorimetry(TG-DSC), zinc acetate dihydrate was identified to be dissolved into ZnO above $380^{\circ}C$. ZnO deposited at $380-700^{\circ}C$ showed polycrystalline structures with ZnO (101) and ZnO (002) diffraction peaks like bulk ZnO in XRD, and from which c-axis strain ${\Sigma}Z=0.2\%$ and compressive biaxial stress$\sigma=-0.907\;GPa$ was obtained for the ZnO deposited $400^{\circ}C$. Scanning electron microscope revealed that microstructures of the ZnO were dependent on the deposition temperature. ZnO grown below temperature $600^{\circ}C$ were aggregate consisting of zinc acetate and ZnO particles shaped with nanoblades. On the other hand the grain of the ZnO deposited at $700^{\circ}C$ showed a distorted hexagonal shape and was composed of many ultrafine ZnO powers of 10-25 nm in size. The formation of these ulrafine nm scale ZnO powers was explained by the model of random nucleation mechanism. The optical property of the ZnO was analyzed by the photoluminescence (PL) measurement.

• Journal of the Korean Operations Research and Management Science Society
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• v.21 no.3
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• pp.159-174
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• 1996

Facing decision-making tasks, managers frequently make judgments, However, since managers are human beings, the fficiency of their judgments is limited. Two major sources of inefficiency in their judgments have been recognized : one is systematic deviations from normatively preferred decisions, so called bias or incorrect intuition, and the other is inconsistency in their judgments, i. e. erratic decision making variance. Rather than bias, variance is really expensive or damaging. Thus, if the inconsistency inmanagers judgments is removed, performance could be by far improved by virtue of the reduced random variance. One of the approaches to improve managerial judgment is to simply bring managers together by effectively moderating the random variance due to inconsistency. Focusing on combining judgments, this paper addresses many relevant issues such as why combining and how to combine judgments, and suggests methods and models to effectively aggregate subjective judgments, We conduct an experiment to validata the effectiveness of combining jugements over individual judgments. Various combining schemes are also evaluated in terms of their prective accuracy. Among them, mean bias based wighting scheme turns out the best. However, when available information is not enough to estimate the expertise of judges, simple and robust equal weighting might be more efficient and productive. This urges an imperative future research on the issue of how many and which ones to combine from a large set of experts.