• Computers and Concrete
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• v.11 no.4
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• pp.337-350
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• 2013

The sensitivity of compressive strength of no-slump concrete to its ingredient materials and proportions, necessitate the use of robust models to guarantee both estimation and generalization features. It was known that the problem of compressive strength prediction owes high degree of complexity and uncertainty due to the variable nature of materials, workmanship quality, etc. Moreover, using the chemical and mineral additives, superimposes the problem's complexity. Traditionally this property of concrete is predicted by conventional linear or nonlinear regression models. In general, these models comprise lower accuracy and in most cases they fail to meet the extrapolation accuracy and generalization requirements. Recently, artificial intelligence-based robust systems have been successfully implemented in this area. In this regard, this paper aims to investigate the use of optimized support vector machine (SVM) to predict the compressive strength of no-slump concrete and compare with optimized neural network (ANN). The results showed that after optimization process, both models are applicable for prediction purposes with similar high-qualities of estimation and generalization norms; however, it was indicated that optimization and modeling with SVM is very rapid than ANN models.

• Geomechanics and Engineering
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• v.34 no.3
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• pp.285-301
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• 2023

During the construction of E75 highway through Grdelica gorge in Serbia, a major failure occurred in the zone of reinforced rock slope. Excavation was performed in highly anisotropic Paleozoic schist rock formation. The reinforcement consisted of the two rows of micropile wall with pre-stressed anchors. Forces in anchors were monitored with load cells while benchmarks were installed for superficial displacement measurements. The aim of the study is to investigate possible causes of instability considering different probability distributions of the strength of discontinuities and anchor bond strength by applying different optimization techniques for finding the critical failure surface. Even though the deterministic safety factor value is close to unity, the probability of failure is governed by variability of shear strength of anisotropic planes and optimization method used for locating the critical sliding surface. The Cuckoo search technique produces higher failure probabilities compared to the others. Depending on the assigned statistical distribution of input parameters, various performance functions of the factor of safety are obtained. The probability of failure is insensitive to the variation of bond strength. Different sampling techniques should yield similar results considering that the sufficient number of safety factor evaluations is chosen to achieve converged solution.

• Computers and Concrete
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• v.32 no.6
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• pp.577-594
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• 2023

Engineered cementitious composites with calcined clay limestone cement (LC³-ECC) as a kind of green, low-carbon and high toughness concrete, has recently received significant investigation. However, the complicated relationship between potential influential factors and LC³-ECC compressive strength makes the prediction of LC³-ECC compressive strength difficult. Regarding this, the machine learning-based prediction models for the compressive strength of LC³-ECC concrete is firstly proposed and developed. Models combine three novel meta-heuristic algorithms (golden jackal optimization algorithm, butterfly optimization algorithm and whale optimization algorithm) with support vector regression (SVR) to improve the accuracy of prediction. A new dataset about LC³-ECC compressive strength was integrated based on 156 data from previous studies and used to develop the SVR-based models. Thirteen potential factors affecting the compressive strength of LC³-ECC were comprehensively considered in the model. The results show all hybrid SVR prediction models can reach the Coefficient of determination (R²) above 0.95 for the testing set and 0.97 for the training set. Radar and Taylor plots also show better overall prediction performance of the hybrid SVR models than several traditional machine learning techniques, which confirms the superiority of the three proposed methods. The successful development of this predictive model can provide scientific guidance for LC³-ECC materials and further apply to such low-carbon, sustainable cement-based materials.

• Steel and Composite Structures
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• v.19 no.4
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• pp.995-1009
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• 2015

This study presents design optimization of spot welded structures to attain maximum strength by using the Nelder-Mead (Simplex) method. It is the main idea of the algorithm that the simulation run is executed several times to satisfy predefined convergence criteria and every run uses the starting points of the previous configurations. The material and size of the sheet plates are the pre-assigned parameters which do not change in the optimization cycle. Locations of the spot welds, on the other hand, are chosen to be design variables. In order to calculate the objective function, which is the maximum equivalent stress, ANSYS, general purpose finite element analysis software, is used. To obtain global optimum locations of spot welds a methodology is proposed by modifying the Nelder-Mead (Simplex) method. The procedure is applied to a number of representative problems to demonstrate the validity and effectiveness of the proposed method. It is shown that it is possible to obtain the global optimum values without stacking local minimum ones by using proposed methodology.

• Journal of Ocean Engineering and Technology
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• v.13 no.2 s.32
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• pp.83-89
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• 1999

In this paper an experimental study on the development of the shielded metal are welding(SMAW) optimization technique for the dissimilar materials SS41 and STS304 of Auto-Lifting Magnet core plate was carried out. It was confirmed that the optimum welding heat input range was 37.5 to 45 kj/cm by considering on the strength and fatigue life of the welded joints more than 100% joint efficiency. And the quantitative relationship empirical wquation between the strength toughness adn fatigue life and the weld heat input was obtained.

• Structural Engineering and Mechanics
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• v.78 no.2
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• pp.145-161
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• 2021

Dynamic compaction of Aluminum powder using gas detonation forming technique was investigated. The experiments were carried out on four different conditions of total pre-detonation pressure. The effects of the initial powder mass and grain particle size on the green density and strength of compacted specimens were investigated. The relationships between the mentioned powder design parameters and the final features of specimens were characterized using Response Surface Methodology (RSM). Artificial Neural Network (ANN) models using the Group Method of Data Handling (GMDH) algorithm were also developed to predict the green density and green strength of compacted specimens. Furthermore, the desirability function was employed for multi-objective optimization purposes. The obtained optimal solutions were verified with three new experiments and ANN models. The obtained experimental results corresponding to the best optimal setting with the desirability of 1 are 2714 kg·m-3 and 21.5 MPa for the green density and green strength, respectively, which are very close to the predicted values.

• Steel and Composite Structures
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• v.45 no.6
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• pp.877-894
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• 2022

Geopolymer concrete (GPC) has emerged as a feasible choice for construction materials as a result of the environmental issues associated with the production of cement. The findings of this study contribute to the development of machine learning methods for estimating the properties of eco-friendly concrete to help reduce CO₂ emissions in the construction industry. The compressive strength (f_c) of GPC is predicted using artificial intelligence approaches in the present study when ground granulated blast-furnace slag (GGBS) is substituted with natural zeolite (NZ), silica fume (SF), and varying NaOH concentrations. For this purpose, two machine learning methods multi-layer perceptron (MLP) and radial basis function (RBF) were considered and hybridized with arithmetic optimization algorithm (AOA), and grey wolf optimization algorithm (GWO). According to the results, all methods performed very well in predicting the f_c of GPC. The proposed AOA - MLP might be identified as the outperformed framework, although other methodologies (AOA - RBF, GWO - RBF, and GWO - MLP) were also reliable in the fc of GPC forecasting process.

• Structural Engineering and Mechanics
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• v.71 no.4
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• pp.417-432
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• 2019

Seismic performance analysis of steel-brace reinforced concrete (RC) frame using topology optimization in highly seismic region was discussed in this research. Topology optimization based on truss-like material model was used, which was to minimum volume in full-stress method. Optimized bracing systems of low-rise, mid-rise and high-rise RC frames were established, and optimized bracing systems of substructure were also gained under different constraint conditions. Thereafter, different structure models based on optimized bracing systems were proposed and applied. Last, structural strength, structural stiffness, structural ductility, collapse resistant capacity, collapse probability and demolition probability were studied. Moreover, the brace buckling was discussed. The results show that bracing system of RC frame could be derived using topology optimization, and bracing system based on truss-like model could help to resolve numerical instabilities. Bracing system of topology optimization was more effective to enhance structural stiffness and strength, especially in mid-rise and high-rise frames. Moreover, bracing system of topology optimization contributes to increase collapse resistant capacity, as well as reduces collapse probability and accumulated demolition probability. However, brace buckling might weaken beneficial effects.

• Computers and Concrete
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• v.26 no.4
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• pp.309-316
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• 2020

The application of multi-variable adaptive regression spline (MARS) in predicting he long-term compressive strength of a concrete with various admixtures has been investigated in this study. The compressive strength of concrete specimens, which were made based on 24 different mix designs using various mineral and chemical admixtures in different curing ages have been obtained. First, The values of fly ash (FA), micro-silica (MS), water-reducing admixture (WRA), coarse and fine aggregates, cement, water, age of samples and compressive strength were defined as inputs to the model, and MARS analysis was used to model the compressive strength of concrete and to evaluate the most important parameters affecting the estimation of compressive strength of the concrete. Next, the proposed equation by the MARS method using particle swarm optimization (PSO) algorithm has been optimized to have more efficient equation from the economical point of view. The proposed model in this study predicted the compressive strength of the concrete with various admixtures with a correlation coefficient of R=0.958 rather than the measured compressive strengths within the laboratory. The final model reduced the production cost and provided compressive strength by reducing the WRA and increasing the FA and curing days, simultaneously. It was also found that due to the use of the liquid membrane-forming compounds (LMFC) for its lower cost than water spraying method (SWM) and also for the longer operating time of the LMFC having positive mechanical effects on the final concrete, the final product had lower cost and better mechanical properties.

• Transactions of the Korean Society of Automotive Engineers
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• v.14 no.5
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• pp.155-163
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• 2006

Car seat is one the most important element to make comfortable drivability. It can absorb the impact or vibration during driving state. In addition to those factors, it is needed to have enough strength for passenger safety. From energy efficiency and environmental point of view lighter passenger car seat frame becomes hot issue in the auto industry. In this paper, weight optimization methodology is investigated for commercial car seat frame using CAE. Optimized designs for seat frame are developed using commercially available finite element code(ANSYS) and design of experiment method. At first, car seat frame is modelled using 3-D computer aided design tool(CATIA) and simplified for finite element modelling. Finite element analysis is carried out for the case of FMVSS 202 Head Restraint test to check the strength of the original seat frame. Two base brackets are selected as optimized elements that are the heaviest parts in the seat frame. After finite element analysis for the brackets with similar load condition to the previous test optimization technique is applied for 10% to 50% weight reduction. Design of experiment is utilized to obtain optimization design for the bracket based on the modified 50% weight reduction model in which outer shape of the bracket is conserved. Weight optimization models result in the decrease of the strength in spite of weight reduction. The more design points should be considered to get better optimized model. The more advanced optimization technique may be utilized for more parts of the seat frame to increase whole seat frame characteristics in the future.