Startseite Prediction of Strength of Remixed Concrete by Application of Orthogonal Decomposition, Neural Analysis and Regression Analysis
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Prediction of Strength of Remixed Concrete by Application of Orthogonal Decomposition, Neural Analysis and Regression Analysis

  • K.L. Bidkar und P.D. Jadhao
Veröffentlicht/Copyright: 31. August 2019
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Abstract

Compressive strength is the foremost property of concrete which is influenced by a number of parameters. These parameters plays important role for the characteristics achieved by concrete. Orthogonal decomposition, neural analysis and regression analysis tools can be utilized where the dependence and independence of these parameters to be considered. In this paper these analyses are considered for remix concrete, in which apart from the cement contents, w/c ratio, proportions of C.A., F.A., the other parameters like blend ratio (r=Qo/Qf, Qo=quantity of old partially set concrete, Qf =quantity of fresh concrete) time lag ( time between preparation and placing of concrete) also plays the important role.

Keywords: Remixed concrete; blend ratio; time lag; orthogonal decomposition; neural analysis; regression

1 Introduction

In the modern Civil Engineering construction work, concrete plays a vital role and is used very widely as a building material. It is composed of fine and coarse aggregates held together by a hardened paste of cement and water. It is generally considered that if this mixed mass is not immediately placed in the formwork and compacted without further loss in time, it starts to lose its strength. This partially set concrete if used in concreting reduces the strength of the structural elements. In construction partial setting of concrete occurs due to unforeseen circumstances like displacement of the formwork, power failure and breakdown of machinery, accidents and delay in casting due to time gaps, delay in transportation of concrete from RMC plant to project site location, due to extension of the incomplete construction on next day, due to shortage of constituents of concrete etc. A loss of strength is noticed if the concrete mass suffers setting due to considerable time lag between preparing the mix and it’s placing. It is relevant to mention here that the strength and workability characteristics may not be affected appreciably up to the initial setting time but as the final setting time is approached they are greatly affected [1, 2]. The algorithms optimizing hybrid performance measures that seek to balance quantification and classification performance. The algorithms present a significant advancement in the theory of multivariate optimization, via a rigorous theoretical analysis, that they exhibit optimal convergence [3]. The mathematical model development is also possible by orthogonal decomposition [4, 5], saving time and computational work. Proper orthogonal decomposition (POD) is an accepted dimensionality reduction technique, which helps in hierarchizing the various influencing variables based on their variability. Therefore, this technique is helpful in development of mathematical models that are operational and require less computational effort and time. For the estimation of the compressive strength of concrete specimens an artificial neural network (ANN) using the experimental laboratory strength, and the ingredient values, their ratios is utilized in the study. Prediction of concrete compressive strength is implemented using ANN models, consisting of eleven input layer, one hidden layer and one output layer, for each data set. The analysis is then conducted for cube specimens with different compressive strengths for wide variation in their constituent proportions.

As compressive strength is utmost important property judging the levels of performance, the constituents of concrete and their relative importance should be considered. The statistical approaches are useful in predicting the strength of concrete [6].

The purpose of regression techniques are used to take data and deduce a response (y) or responses in terms of input variables (x-values).

Regression analysis is utilized to predict a continuous dependent variable or response from a number of independent or input variables. If the dependent variable is dichotomous, then logistic regression should be used. The independent variables used in regression can be either continuous or dichotomous (i.e. take on a value of 0 or 1). Categorical independent variables with more than two values can also be used in regression analyses, but they first must be converted into variables that have only two levels. This is called dummy coding or indicator variables. Usually, regression analysis is used with naturally-occurring variables, as opposed to experimentally manipulated variables, although you can use regression with experimentally manipulated variables. There are also the state-of-the-art regression methods, namely projection pursuit regression, support vector machines (SVM) and random forests [7]. In statistics, projection pursuit regression is a statistical model developed by Jerome H. Friedman and Werner Stuetzle which is an extension of additive models. This model adapts the additive models in that it first projects the data matrix of explanatory variables in the optimal direction before applying smoothing functions to these explanatory variables [8]. Support vector machine (SVM) is firmly based on learning theory and uses regression technique by introducing accuracy insensitive loss function. SVM is one of the machine learning (ML) techniques derived from statistical learning theory by Vapnik and Chervonenkis [9]. The foundations of SVM were developed by Vapnik [10] at AT&T Bell Laboratories. Overall, SVMs have been applied in statistics, computer science, and other fields with great success. Random forest is a great algorithm to train early in the model development process, to see how it performs and it’s hard to build a “bad” Random Forest, because of its simplicity. This algorithm is also a great choice, if you need to develop a model in a short period of time. On top of that, it provides a pretty good indicator of the importance it assigns to your features. Random Forests are also very hard to beat in terms of performance. Of course you can probably always find a model that can perform better, like a neural network, but these usually take much more time in the development. And on top of that, they can handle a lot of different feature types, like binary, categorical and numerical. Random forests are a type of ensemble method whichmakes predictions by averaging over the predictions of several independent base models. Since its introduction by Breiman, the random forests framework has been extremely successful as a general purpose classification and regression method [11].

2 Related Work

The work is carried out for twelve strength values along with the variables which affect the strength i.e. cement, FA, CA, water, blend ratio r and time lag t as the primary variables along with the derived variables water cement ratio (W/C),fine aggregate to cement ratio (FA/C), coarse aggregate to cement ratio (CA/C), blend ratio to cement ratio (r/C) and time lag to cement ratio (t/C). For developing the proposed model three different methods namely orthogonal decomposition, neural analysis and regression analysis are utilized.

A) Orthogonal Decomposition

Algorithm for POD [5, 12] has the following sequential steps in reorganizing and rationalizing data for subsequent use.

  • Appropriate data attainment is the first step in orthogonal decomposition

  • Checking for size, completeness and outliers for the data collected

  • Creation of artificial variables from available data may be considered if established relationship exists, to reduce time and effort.

  • Z-score standardization is a popular normalization.

Zi = (xi − ẍk)/σk

Zi = Standardized variable

xi = Original variable

ẍk = Variable mean

σk = Standard deviation

= [1/NΣ(xi-ẍk)2]1/2

N = Total no. of observations

K = Total no. of variables

  • Assembling the correlation matrix for normalized. Checking for singularity, sample adequacy and sphericity of data.

  • Eigen values and eigenvectors extraction from the correlation matrix. Eigenvectors designate the direction in which the greatest variations are seen. Eigen values quantify the relative amount of variation explained by the components.

  • Correlation matrix is always a symmetric matrix, the eigen values are always real and eigenvectors are orthogonal to each other.

  • Data reduction and hierarchization is done, based on the end objective of the exercise. Scree plots, eigen values and eigenvectors help in decision making as to how many axes need to be considered.

A total data of 12 variables is considered, out of which 7 are primary quantities and 5 are derived quantities. These are examined by orthogonal decomposition [12]. The Dependent variable strength is tested by comparing it with other quantities. For the data under consideration the corelation matrix is formed as shown in Table 1.The data is checked for non-sphericity as well as adequacy.

Table 1

Co-relation Matrix

Co-relationStrengthCementFACAWaterrtW/CFA/CCA/Cr/Ct/C
Strength1.0001.0001.0000.9990.9990.9990.9990.9990.9960.99680.99670.9973
Cement1.0001.0001.0001.0000.9990.9990.9990.9990.99650.99650.99640.9970
FA1.0001.0001.0001.0000.9990.9990.9990.9990.9960.99610.99600.9967
CA0.991.00001.0001.0000.9980.9990.9990.9990.99600.99590.99580.9966
Water0.99930.99920.9990.9981.0001.0001.00000.9990.99880.99870.99860.9990
r0.99940.99930.9990.9991.0001.0001.00001.0000.99860.99860.99840.9989
t0.99950.99940.9990.9991.0001.0001.00001.0000.99850.99850.99840.9988
W/C0.99950.99940.9990.9990.9991.0001.00001.0000.99840.99830.99820.9987
FA/C0.99690.99650.9960.9960.9980.9980.99850.9981.00001.00000.99990.9999
CA/C0.99680.99650.9960.9950.9980.9980.99850.9981.00001.00001.00000.9999
r/C0.99670.99640.9960.9950.9980.9980.99840.9980.99991.00001.00000.9999
t/C0.99730.99700.9960.9960.9990.9980.99880.9980.99990.99990.99991.0000

Total variance is extracted from, each component by PCA. The components eigen values as a total, percentage of variance as well as cumulative percentage for all the components are tabulated in Table 2. The only first two components have the major influence.

Table 2

Total Variance using the Extraction Method, Principal Component Analysis

Initial Eigen valuesExtraction Sums of Squared Loadings
ComponentTotal% of VarianceCumulative %Total% of VarianceCumulative %
19.3177.58177.5819.3177.58177.581
22.68422.3799.9512.68422.3799.951
30.0030.02899.979
40.0030.021100
51.69E-050100
63.86E-083.22E-07100
72.65E-112.20E-10100
82.87E-122.39E-11100
98.60E-137.16E-12100
104.03E-143.36E-13100
111.45E-161.21E-15100
12-1.09E-16

For squared loading these two components plays important role.

The plot for variation of eigen values with respect to the components shown in Figure 1, which elaborates the significance of components in the analysis. Figure shows that the slope of trend line is a steep fall down up to component 2. Next to component 2, the slope is not significant. Thus 99.95% of changes occurred by first two parameters (Table 1).

Figure 1 Plot of Components Vs Eigen Value
Figure 1

Plot of Components Vs Eigen Value

The data decomposition (Table 3) is done and the plot of parameters Vs component presented in Figure 2. This figure shows the relationship of various variables with respect to the components is plotted. It is observed that the strength and W/C ratio are in different quadrants, which reveals that there is a reduction in strength with increase in water to cement ratio. This is consistent with the recognized Abram’s law which states the strength of a concrete mix is inversely related to the mass ratio of water to cement.As the water content increases the strength of concrete decreases.

Figure 2 Variation of Parameters Vs Component
Figure 2

Variation of Parameters Vs Component

Table 3

Component Matrix using the Extraction Method, Principal Component Analysis

ParametersComponent
12
Strength.999-.015
Cement.999-.034
FA.998-.055
CA.998-.065
Water.610.792
r.741.671
t.811.585
W/C.858.512
FA/C-.865.501
CA/C-.872.489
r/C-.881.473
t/C-.848.529

Graphical representation of each parameter Vs all other parameters is given in Figure 3.

Figure 3 Scatter Plot of Parameters
Figure 3

Scatter Plot of Parameters

Normal P-P Plot of Regression Standardized Residual for Dependent Variable Strength is plotted as shown in Figure 4 By regression analysis the dependent variable strength is represented against its predicted values (Figure 5).

Figure 4 Normal P-P Plot of Regression Standardized Residual for Dependent Variable Strength
Figure 4

Normal P-P Plot of Regression Standardized Residual for Dependent Variable Strength

B) Neural Network Analysis

The 12 specimens are considered as trial specimens, amongst which 75% (9 Nos) are considered for training and remaining 25% (3 Nos.) used for testing purpose. The case processing summary for these specimens including the testing and training is shown in Table 4

Table 4

Case Processing Summary

NPercent
SampleTraining975.00%
Testing325.00%
Valid12100.00%
Excluded0
Total12

The neural network [13] for input data and output data is represented in figure 6.The details of the network used is tabulated in Table 5

Table 5

Network Information

1Cement
2FA
3CA
4Water
5r
Covariates6t
Input Layer7W/C
8FA/C
9CA/C
10r/C
11t/C
Number of Units11
Rescaling Method for CovariatesStandardized
Hidden Layer(s)Number of Hidden Layers1
Number of Units in Hidden Layer 14
Activation FunctionHyperbolic tangent
Dependent Variables 1Strength
Output LayerNumber of Units1
Rescaling Method for Scale DependentsStandardized
Activation FunctionIdentity
Error FunctionSum of Squares

Multilayer Perceptron [14]

The Neural Analysis Model developed and it is summarized as shown in Table 6. It indicates Sum of squares error and relative error for testing and training data used. The various parameter estimates predicted are composed in Table 6, which indicates the values of component parameters for different layers under consideration.

Table 6

Model Summary

Sum of Squares Error0.003
TrainingRelative Error0.001
Stopping Rule1 consecutive step(s)
Usedwith no decrease in
error
Training Time00:00.0
TestingSum of Squares Error4.18E-05
Relative Error0.001
Dependent Variable: Strength

The relationship of laboratory strengths and predicted strengths are plotted in Figure 7, which gives a linear relationship between these two strengths.

Figure 5 Regression adjusted for Dependent Variable Strength
Figure 5

Regression adjusted for Dependent Variable Strength

Figure 6 Neural Network [14]
Figure 6

Neural Network [14]

Figure 8 illustrates the importance of independent variables which are responsible for influencing the dependent variable (strength of concrete).These results also holdgood with the results investigated from figure 2 of decomposition analysis.

C) Linear Regression

Linear regression analysis is carried out for the observed compressive lab strength. The model compressive strength and the observed values of compressive strength are summarized in Table 8

Table 7

Independent Variable Importance

ImportanceNormalized Importance
Cement0.1363.10%
FA0.1887.90%
CA0.205100.00%
Water0.09144.20%
r0.0419.40%
t0.09445.90%
W/C0.11254.50%
FA/C0.04923.80%
CA/C0.03215.60%
r/C0.03918.90%
t/C0.02813.70%
Table 8

Summary statistics

VariableObs.Obs. with missing dataObs. without missing dataMinimumMaximumMeanStd. deviation
Compressive Strength Model (MPa)1201220.51931.17326.623.289
Comp Lab(Strength MPa)1201221.00031.00026.403.508

3 Model Development

Regression of variable Compressive Strength Model (MPa))

The goodness of fit statistics for the model is noted in Table 9.

Table 9

Goodness of fit statistics (Compressive Strength Model (MPa)

Obs.Sum of weightsDFR2Adjusted R2MSERMSEMAPEDWCpAICSBCPC
1212100.9310.9240.8250.9081.9591.732.00-0.4960.4740.097

Table 10 shows the variance analysis for model strength with error involved

Table 10

Analysis of variance (Compressive Strength Model (Mpa))

SourceDFSum of squaresMean squaresFPr> F
Model1110.718110.718134.20< 0.0001
Error108.2500.825
Corrected Total11118.969

Computed against model Y = Mean(Y)

The parameters involved in the model developed from laboratory compressive strength is given in Table 11. From these observations the linear regression equation for model development can be derived.

Table 11

Model parameters (Compressive Strength Model (Mpa))

SourceValueStandard errortPr > |t|Lower boundUpper bound
(95%)(95%)
Intercept2.7392.0781.3180.217-1.8927.369
Comp. Strength Lab(MPa)0.9040.07811.58< 0.00010.7311.078

Equation of the model (Compressive Strength Model (MPa)):

Y Model(N/mm2) = 2.738678+0.904492*YLab (= Yp-N/mm2)

From standardized coefficients of compressive strength of tabular results of Table 12, the graphical results are plotted in Figure 9.

Figure 7 Laboratory Strength Vs Predicted Strength
Figure 7

Laboratory Strength Vs Predicted Strength

Figure 8 Importance of Independent Variables
Figure 8

Importance of Independent Variables

Table 12

Standardized coefficients (Compressive Strength Model (MPa))

SourceValueerror StandardtPr > |t|Lower bound (95%)Upper bound (95%)
Comp Strength Lab(MPa)0.9650.08311.58< 0.00010.7791.15

The lab strengths and predicted model strength prediction along with the residuals statistics is tabulated in Figure 9.

The regression analysis is applied for model strength and its lab strengths are plotted in Figure 10. The representation of lab strength Vs the standardized residuals is shown in Figure 11

Figure 9 Regression of YModel(N/mm2) by YLab (= Yp-N/mm2) (R2=0.931)
Figure 9

Regression of YModel(N/mm2) by YLab (= Yp-N/mm2) (R2=0.931)

Figure 10 Pred (YModel(N/m2)/YModel(N/m2)
Figure 10

Pred (YModel(N/m2)/YModel(N/m2)

Figure 11 Standardized residuals / YModelN/m2
Figure 11

Standardized residuals / YModelN/m2

The representation of predicted model strength Vs model strength shows a better linear relationship is as shown in Figure 10.

The standardized residuals for each model strengths are graphically represented in Figure 11.

4 Conclusions

The data used for various analyses gives the conclusions as follows:

From Orthogonal Decomposition

It is concluded that the compressive strength increases as cement content, CA and FA contents. It decreases with

Table 13

Predictions and residuals (Compressive Strength Model (MPa))

Obs.WeightComp Strength LabComp Strength ModelPred (Comp. Strength Model (MPa))ResidualStd. residualStd. dev. on pred.Lower bound 95%Upper bound 95%Std. dev. on pred.Lower Bound 95%Upper bound 95%
(MPa)(Mpa)(Mean)(Mean)(Mean)(Obs.)(Obs.)(Observation)
1131.00031.130.7780.3950.4350.44529.78731.7681.01128.5233.03
2130.70030.430.507-0.079-0.0870.42629.55831.4551.00328.2732.74
3129.80029.629.693-0.020-0.0220.37328.86130.5240.98227.5031.88
4129.40029.229.331-0.036-0.0400.35128.54830.1140.97427.1631.501
5128.33028.328.363-0.009-0.0100.30227.68929.0370.95726.2330.49
6127.20027.027.341-0.322-0.3550.26926.74027.9410.94725.2329.45
7125.50025.725.803-0.032-0.0350.27225.19826.4080.94823.6927.91
8125.10025.125.441-0.262-0.2880.28124.81526.0680.95123.3227.56
9124.30024.524.718-0.147-0.1620.30924.02925.4070.96022.5826.85
10122.60022.423.180-0.706-0.7770.39622.29824.0630.99120.9725.38
11121.00020.521.733-1.214-1.3370.49720.62622.8401.03519.4224.04
12121.9024.922.542.432.670.43921.5723.521.00920.3024.79

increase in water content, blend ratio, time lag and W/C ratio (Figure 2)

It also decreases with the parameter’s ratios like W/C, CA/C, r/C, t/c

From Neural Analysis

Figure 8 represents that the dependent variable strength is having the main influence of the parameters like C.A, FA, cement and W/C ratio, while the other parameters like t, r, water, the ratios like FA/C, r/C,CA/C, t/C are not so much significant.

From Linear Regression

From the lab compressive strength, the model strength can be predicted effectively (R2 = 0.931).

The equation of the model formalized as, Y Model (N/mm2) = 2.738678+0.904492*YLab (= Yp-N/mm2.

References

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Received: 2019-05-16
Accepted: 2019-06-02
Published Online: 2019-08-31

© 2019 K.L. Bidkar and Dr.P.D. Jadhao, published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  79. Monitoring bulk material pressure on bottom of storage using DEM
  80. Calibration of Transducers and of a Coil Compression Spring Constant on the Testing Equipment Simulating the Process of a Pallet Positioning in a Rack Cell
  81. Design of evaluation tool used to improve the production process
  82. Planning of Optimal Capacity for the Middle-Sized Storage Using a Mathematical Model
  83. Experimental assessment of the static stiffness of machine parts and structures by changing the magnitude of the hysteresis as a function of loading
  84. The evaluation of the production of the shaped part using the workshop programming method on the two-spindle multi-axis CTX alpha 500 lathe
  85. Numerical Modeling of p-v-T Rheological Equation Coefficients for Polypropylene with Variable Chalk Content
  86. Current options in the life cycle assessment of additive manufacturing products
  87. Ideal mathematical model of shock compression and shock expansion
  88. Use of simulation by modelling of conveyor belt contact forces
https://doi.org/10.1515/eng-2019-0053
Schlagwörter für diesen Artikel
Remixed concrete; blend ratio; time lag; orthogonal decomposition; neural analysis; regression
Creative Commons
BY 4.0

Artikel in diesem Heft

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  2. Exploring conditions and usefulness of UAVs in the BRAIN Massive Inspections Protocol
  3. A hybrid approach for solving multi-mode resource-constrained project scheduling problem in construction
  4. Identification of geodetic risk factors occurring at the construction project preparation stage
  5. Multicriteria comparative analysis of pillars strengthening of the historic building
  6. Methods of habitat reports’ evaluation
  7. Effect of material and technological factors on the properties of cement-lime mortars and mortars with plasticizing admixture
  8. Management of Innovation Ecosystems Based on Six Sigma Business Scorecard
  9. On a Stochastic Regularization Technique for Ill-Conditioned Linear Systems
  10. Dynamic safety system for collaboration of operators and industrial robots
  11. Assessment of Decentralized Electricity Production from Hybrid Renewable Energy Sources for Sustainable Energy Development in Nigeria
  12. Seasonal evaluation of surface water quality at the Tamanduá stream watershed (Aparecida de Goiânia, Goiás, Brazil) using the Water Quality Index
  13. EFQM model implementation in a Portuguese Higher Education Institution
  14. Assessment of direct and indirect effects of building developments on the environment
  15. Accelerated Aging of WPCs Based on Polypropylene and Plywood Production Residues
  16. Analysis of the Cost of a Building’s Life Cycle in a Probabilistic Approach
  17. Implementation of Web Services for Data Integration to Improve Performance in The Processing Loan Approval
  18. Rehabilitation of buildings as an alternative to sustainability in Brazilian constructions
  19. Synthesis Conditions for LPV Controller with Input Covariance Constraints
  20. Procurement management in construction: study of Czech municipalities
  21. Contractor’s bid pricing strategy: a model with correlation among competitors’ prices
  22. Control of construction projects using the Earned Value Method - case study
  23. Model supporting decisions on renovation and modernization of public utility buildings
  24. Cements with calcareous fly ash as component of low clinker eco-self compacting concrete
  25. Failure Analysis of Super Hard End Mill HSS-Co
  26. Simulation model for resource-constrained construction project
  27. Getting efficient choices in buildings by using Genetic Algorithms: Assessment & validation
  28. Analysis of renewable energy use in single-family housing
  29. Modeling of the harmonization method for executing a multi-unit construction project
  30. Effect of foam glass granules fillers modification of lime-sand products on their microstructure
  31. Volume Optimization of Solid Waste Landfill Using Voronoi Diagram Geometry
  32. Analysis of occupational accidents in the construction industry with regards to selected time parameters
  33. Bill of quantities and quantity survey of construction works of renovated buildings - case study
  34. Cooperation of the PTFE sealing ring with the steel ball of the valve subjected to durability test
  35. Analytical model assessing the effect of increased traffic flow intensities on the road administration, maintenance and lifetime
  36. Quartz bentonite sandmix in sand-lime products
  37. The Issue of a Transport Mode Choice from the Perspective of Enterprise Logistics
  38. Analysis of workplace injuries in Slovakian state forestry enterprises
  39. Research into Customer Preferences of Potential Buyers of Simple Wood-based Houses for the Purpose of Using the Target Costing
  40. Proposal of the Inventory Management Automatic Identification System in the Manufacturing Enterprise Applying the Multi-criteria Analysis Methods
  41. Hyperboloid offset surface in the architecture and construction industry
  42. Analysis of the preparatory phase of a construction investment in the area covered by revitalization
  43. The selection of sealing technologies of the subsoil and hydrotechnical structures and quality assurance
  44. Impact of high temperature drying process on beech wood containing tension wood
  45. Prediction of Strength of Remixed Concrete by Application of Orthogonal Decomposition, Neural Analysis and Regression Analysis
  46. Modelling a production process using a Sankey diagram and Computerized Relative Allocation of Facilities Technique (CRAFT)
  47. The feasibility of using a low-cost depth camera for 3D scanning in mass customization
  48. Urban Water Infrastructure Asset Management Plan: Case Study
  49. Evaluation the effect of lime on the plastic and hardened properties of cement mortar and quantified using Vipulanandan model
  50. Uplift and Settlement Prediction Model of Marine Clay Soil e Integrated with Polyurethane Foam
  51. IoT Applications in Wind Energy Conversion Systems
  52. A new method for graph stream summarization based on both the structure and concepts
  53. “Zhores” — Petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in Skolkovo Institute of Science and Technology
  54. Economic Disposal Quantity of Leftovers kept in storage: a Monte Carlo simulation method
  55. Computer technology of the thermal stress state and fatigue life analysis of turbine engine exhaust support frames
  56. Statistical model used to assessment the sulphate resistance of mortars with fly ashes
  57. Application of organization goal-oriented requirement engineering (OGORE) methods in erp-based company business processes
  58. Influence of Sand Size on Mechanical Properties of Fiber Reinforced Polymer Concrete
  59. Architecture For Automation System Metrics Collection, Visualization and Data Engineering – HAMK Sheet Metal Center Building Automation Case Study
  60. Optimization of shape memory alloy braces for concentrically braced steel braced frames
  61. Topical Issue Modern Manufacturing Technologies
  62. Feasibility Study of Microneedle Fabrication from a thin Nitinol Wire Using a CW Single-Mode Fiber Laser
  63. Topical Issue: Progress in area of the flow machines and devices
  64. Analysis of the influence of a stator type modification on the performance of a pump with a hole impeller
  65. Investigations of drilled and multi-piped impellers cavitation performance
  66. The novel solution of ball valve with replaceable orifice. Numerical and field tests
  67. The flow deteriorations in course of the partial load operation of the middle specific speed Francis turbine
  68. Numerical analysis of temperature distribution in a brush seal with thermo-regulating bimetal elements
  69. A new solution of the semi-metallic gasket increasing tightness level
  70. Design and analysis of the flange-bolted joint with respect to required tightness and strength
  71. Special Issue: Actual trends in logistics and industrial engineering
  72. Intelligent programming of robotic flange production by means of CAM programming
  73. Static testing evaluation of pipe conveyor belt for different tensioning forces
  74. Design of clamping structure for material flow monitor of pipe conveyors
  75. Risk Minimisation in Integrated Supply Chains
  76. Use of simulation model for measurement of MilkRun system performance
  77. A simulation model for the need for intra-plant transport operation planning by AGV
  78. Operative production planning utilising quantitative forecasting and Monte Carlo simulations
  79. Monitoring bulk material pressure on bottom of storage using DEM
  80. Calibration of Transducers and of a Coil Compression Spring Constant on the Testing Equipment Simulating the Process of a Pallet Positioning in a Rack Cell
  81. Design of evaluation tool used to improve the production process
  82. Planning of Optimal Capacity for the Middle-Sized Storage Using a Mathematical Model
  83. Experimental assessment of the static stiffness of machine parts and structures by changing the magnitude of the hysteresis as a function of loading
  84. The evaluation of the production of the shaped part using the workshop programming method on the two-spindle multi-axis CTX alpha 500 lathe
  85. Numerical Modeling of p-v-T Rheological Equation Coefficients for Polypropylene with Variable Chalk Content
  86. Current options in the life cycle assessment of additive manufacturing products
  87. Ideal mathematical model of shock compression and shock expansion
  88. Use of simulation by modelling of conveyor belt contact forces
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