2018
2
3
5
116
Refined Simplified Neutrosophic Similarity Measures Based on Trigonometric Function and Their Application in Construction Project DecisionMaking
2
2
Refined simplified neutrosophic sets (RSNSs) are appropriately used in decisionmaking problems with subattributes considering their truth components, indeterminacy components and falsity components independently. This paper presents the similarity measures of RSNSs based on tangent and cotangent functions. When the weights of each element/attribute and each subelement/subattribute in RSNSs are considered according to their importance, we propose the weighted similarity measures of RSNSs and their multiple attribute decisionmaking (MADM) method with RSNS information. In the MADM process, the developed method gives the ranking order and the best selection of alternatives by getting the weighted similarity measure values between alternatives and the ideal solution according to the given attribute weights and subattribute weights. Then, an illustrative MADM example in a construction project with RSNS information is presented to show the effectiveness and feasibility of the proposed MADM method under RSNS environments. This study extends existing methods and provides a new way for the refined simplified neutrosophic MADM problems containing both the attribute weight and the subattribute weights.
1

1
12


Uwera
Solang
Department of Civil Engineering, Shaoxing University, 508 Huancheng West Road, Shaoxing, Zhejiang Province 312000, P.R. China
Department of Civil Engineering, Shaoxing
China
usolange0@gmail.com


Jun
Ye
508 Huancheng West Road
508 Huancheng West Road
China
yehjun@aliyun.com
Refined simplified neutrosophic set
tangent function
cotangent function
similarity measure
construction project
multiple attribute decisionmaking
Connectivity and Flowrate Estimation of Discrete Fracture Network Using Artificial Neural Network
2
2
Hydraulic parameters of rock mass are the most effective factors that affect rock mass behavioral and mechanical analysis. Aforementioned parameters include intensity and density of fracture intersections, percolation frequency, conductance parameter and mean outflow flowrate which flowing perpendicular to the hydraulic gradient direction. In order to obtain hydraulic parameters, threedimensional discrete fracture network generator, 3DFAM, was developed. But unfortunately, hydraulic parameters obtaining process using conventional discrete fracture network calculation is either time consuming and tedious. For this reason, in this paper using Artificial Neural Network, a tool is designed which precisely and accurately estimate hydraulic parameters of discrete fracture network. Performance of designed optimum artificial neural network is evaluated from mean Squared error, errors histogram, and correlation between artificial neural network predicted value and with discrete fracture network conventionally calculated value. Results indicate that there is acceptable value of mean squared error and also major part of estimated values deviation from actual value placed in acceptable error interval of (1.17, 0.85). In the other hand, excellent correlation of 0.98 exist between predicted and actual value that prove reliability of designed artificial neural network.
1

13
26


Akbar
Esmailzadeh
Mining and metallurgical department, Urmia university of Technology.
Mining and metallurgical department, Urmia
Iran
esmailzade.ak@aut.ac.ir


Abbas
Kamali
Amirkabir university of technology
Amirkabir university of technology
Iran
bbskml@yahoo.com


Kurosh
Shahriar
Amirkabir university of technology
Amirkabir university of technology
Iran
k.shahriar@aut.ac.ir


Reza
Mikaeil
Urmia university of Technology
Urmia university of Technology
Iran
reza.mikaeil@uut.ac.ir
hydraulic parameters
Rock mass
discrete fracture network(DFN)
Artificial Neural Network
connectivity
Prediction of Concrete Properties Using Multiple Linear Regression and Artificial Neural Network
2
2
The selection of appropriate type and grade of concrete for a particular application is the critical step in any construction project. Workability & compressive strength are the two significant parameters that need special attention. The aim of this study is to predict the slump along with 7days & 28days compressive strength based on the data collected from various RMC plants. There are many studies reported in general to address this issue time to time over a long period. However, considering the worldwide use of a huge quantity of concrete for various infrastructure projects, there is a scope for the study that leads to most accurate estimate. Here, data from various concrete mixing plants and ongoing construction sites was collected for M20, M25, M30, M35, M40, M45, M50, M55, M60 and M70 grade of concrete. Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models were built to predict slump as well as 7days and 28days compressive strength. A variety of experiments was carried out that suggests ANN performs better and yields more accurate prediction compared to MLR model for both slump & compressive strength.
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27
38


Shrikant
Charhate
Professor and Dean
Department of Civil Engineering
Pillai HOC College of Engineering and Technology, Rasayani
Professor and Dean
Department of Civil Engineering
India
sbcharate@yahoo.co.in


Mansi
Subhedar
Faculty of Technology
Faculty of Technology
India
msubhedar@mes.ac.in


Nilam
Adsul
Department of Civil Engineering
Pillai HOC College of Engineering & Technology
Department of Civil Engineering
Pillai HOC
India
nilam@mes.ac.in
Slump
Compressive strength
Multiple linear regression
Artificial Neural Network
Application of ANN in Estimating Discharge Coefficient of Circular Piano Key Spillways
2
2
Among all solutions for disrupted vortex formation in shaft spillways, an innovative one called Circular Piano Key Spillway, based upon piano key weir principles, has been experimented less. In this study, the potential of Artificial Neural Networks (ANN) in estimating the amounts of discharge coefficient of Circular Piano Key Spillway has been evaluated. In order to pursue this purpose, the results of some physical experiments were used. These experiments have been conducted in the hydraulic laboratory using different physical models of Circular Piano Key Spillway including three models with different angles of 45, 60 and 90 degrees. Data from those experiments were used in training and test steps of ANN models. Multilayer Perceptron (MLP) network with LevenbergMarquardt backpropagation algorithm was used. The performance of artificial neural network was measured by these statistical indicators: coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE) and optimum quantities of statistical indicators for test step were assessed 0.9999, 0.4988, 0.5963 and 0.9999 respectively, for Circular Piano Key Spillway with an angle of 90 degree and for training step were assessed 0.9999, 0.5479, 0.6305 and 0.9999 respectively, for Circular Piano Key Spillway with an angle of 90 degree. In other words, Circular Piano Key Spillway with an angle of 90 degrees has the optimum performance, both in training and test steps. Artificial Neural Network model can successfully estimate the amounts of discharge coefficient of Circular Piano Key Spillway.
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49


Zahra
Kashkaki
Water Engineering Department, Faculty of Agriculture, BuAli Sina University, Hamadan, Iran
Water Engineering Department, Faculty of
Iran
zkashkaki@gmail.com


Hossein
Banejad
Department of Water Engineering, College of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran
Department of Water Engineering, College
Iran
hossein_banejad@yahoo.com


Majid
Heydari
Water Engineering Department, Faculty of Agriculture, BuAli Sina University, Hamadan, Iran
Water Engineering Department, Faculty of
Iran
mheydari_ir@yahoo.com
Circular Piano Key Spillway
piano key weir
Papaya Spillway
Discharge coefficient
ANN
[[1] Lempérière F, Ouamane A. The Piano Keys weir: A new costeffective solution for spillways. Int J Hydropower Dams 2003;10:144–9.##[2] Lempérière F, Vigny J., Ouamane A. General comments on Labyrinths and Piano KeyWeirs: The past and present. Proc Int Conf Labyrinth Piano Key Weirs  PKW2011, London: Taylor & Francis; 2011, p. 17–24.##[3] Ouamane A. Nine years of study of the Piano KeyWeir in the university laboratory of Biskra “lessons and reflections.” Proc Int Conf Labyrinth Piano Key Weirs  PKW2011, London: Taylor & Francis; 2011, p. 51–8.##[4] Noui A, Ouamane A. Study of optimization of the Piano KeyWeir. Proc Int Conf Labyrinth Piano Key Weirs  PKW2011, London: Taylor & Francis; 2011, p. 175–82.##[5] Lempérière F, Vigny J. General comments on Labyrinth and Piano Key Weirs: The future. Proc Int Conf Labyrinth Piano Key Weirs  PKW2011, London: Taylor & Francis; 2011, p. 289–94.##[6] Barcouda M, Cazaillet O, Cochet P, Jones BA, Lacroix S, Laugier F, et al. Cost effective increase in storage and safety of most dams using fusegates or PK Weirs. 22nd ICOLD Congr, vol. 22, 2006, p. 1289–326.##[7] Cicero GM, Barcouda M, Luck M. Study of a piano key morning glory to increase the spillway capacity of the Bage dam. Proc Int Conf Labyrinth Piano Key Weirs  PKW2011, London: Taylor & Francis; 2011, p. 81–6.##[8] Ackers JC, Bennett FCJ, Scott TA, Karunaratne G. Raising the bellmouth spillway at Black Esk reservoir using Piano Key Weirs. Proc 2nd Int Work Labyrinth Piano Key Weirs  PKW2013, 2013, p. 235–42.##[9] Ancell W. Black Esk Reservoir Dam Raising. UK Water Proj 2013, Water Treat Supply 2013:295–7.##[10] Shemshi R, KabiriSamani A. Swirling flow at vertical shaft spillways with circular pianokey inlets. J Hydraul Res 2017;55:248–58. doi:10.1080/00221686.2016.1238015.##[11] Heydari M, Olyaie E, Mohebzadeh H. Development of a Neural Network Technique for Prediction of Water Quality Parameters in the Delaware River , Pennsylvania. MiddleEast J Sci Res 2013;13:1367–76. doi:10.5829/idosi.mejsr.2013.13.10.1238.##[12] Harandizadeh H, Toufigh MM, Toufigh V. DIFFERENT NEURAL NETWORKS AND MODAL TREE METHOD FOR PREDICTING ULTIMATE BEARING CAPACITY. Int J Optim Civ Eng 2018;8:311–28.##[13] Khademi F, Behfarnia K. Evaluation of Concrete Compressive Strength Using Artificial Neural Network and Multiple Linear Regression Models. Int J Optim Civ Eng 2016;6:423–32.##[14] Keshavarz Z, Torkian H. Application of ANN and ANFIS Models in Determining Compressive Strength of Concrete. J Soft Comput Civ Eng 2018;2:62–70. doi:10.22115/SCCE.2018.51114.##[15] Naderpour H, Rafiean AH, Fakharian P. Compressive strength prediction of environmentally friendly concrete using artificial neural networks. J Build Eng 2018;16:213–9. doi:10.1016/j.jobe.2018.01.007.##[16] Kamanbedast AA. The investigation of discharge coefficient for the morning glory spillway using artificial neural network. World Appl Sci J 2012;17:913–8.##[17] Bashiri Atrabi H, Dewals B, Pirotton M, Archambeau P, Erpicum S. Towards a New Design Equation for Piano Key Weirs Discharge Capacity. Proc 6th Int Symp Hydraul Struct 2016;3310628160:40–9. doi:10.15142/T3310628160853.##[18] Khademi F, Akbari M, Jamal SMM. Prediction of Compressive Strength of Concrete by DataDriven Models. IManager’s J Civ Eng 2015;5:16.##[19] Haghiabi AH, Parsaie A, Ememgholizadeh S. Prediction of discharge coefficient of triangular labyrinth weirs using Adaptive Neuro Fuzzy Inference System. Alexandria Eng J 2016. doi:10.1016/j.aej.2017.05.005.##[20] Salmasi F, Yildirim G, Masoodi A, Parsamehr P. Predicting discharge coefficient of compound broadcrested weir by using genetic programming (GP) and artificial neural network (ANN) techniques. Arab J Geosci 2013;6:2709–17. doi:10.1007/s1251701205407.##[21] Behfarnia K, Khademi F. A comprehensive study on the concrete compressive strength estimation using artificial neural network and adaptive neurofuzzy inference system. Int J Optim Civ Eng 2017;7:71–80.##[22] Honar T, Tarazkar M, Tarazkar M. Estimating discharge coefficient of side weirs using ANFIS. J Water Soil Conserv 2010;17:169–76.##]
Artificial neural networks prediction of compaction characteristics of black cotton soil stabilized with cement kiln dust
2
2
Artificial neural networks (ANNs) that has been successfully applied to structural and most other disciplines of civil engineering is yet to be extended to soil stabilization aspect of geotechnical engineering. As such, this study aimed at applying the ANNs as a soft computing approach that were trained with the feed forward backpropagation algorithm, for the simulation of optimum moisture content (OMC) and maximum dry density (MDD) of cement kiln duststabilized black cotton soil. Ten input and two output data set were used for the ANN model development. The mean squared error (MSE) and Rvalue were used as yardstick and criterions for acceptability of performance. In the neural network development, NN 1051 and NN 1071 respectively for OMC and MDD that gave the lowest MSE value and the highest Rvalue were used in the hidden layer of the networks architecture and performed satisfactorily. For the normalized data used in training, testing and validating the neural network, the performance of the simulated network was satisfactory having R values of 0.983 and 0.9884 for the OMC and MDD, respectively. These values met the minimum criteria of 0.8 conventionally recommended for strong correlation condition. All the obtained simulation results are satisfactory and a strong correlation was observed between the experimental OMC and MDD values as obtained by laboratory tests and the predicted values using ANN.
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50
71


Salahudeen
Bunyamin
Samaru College of Agriculture, Division of Agricultural Colleges, Ahmadu Bello University, Zaria, Nigeria.
Samaru College of Agriculture, Division of
Nigeria
basalahudeen@gmail.com


Thomas
Ijimdiya
Department of Civil Engineering, Ahmadu Bello University, Zaria, Nigeria
Department of Civil Engineering, Ahmadu Bello
Nigeria
ijmstp@hotmail.com


Adrian
Eberemu
Department of Civil Engineering, Ahmadu Bello University, Zaria, Nigeria
Department of Civil Engineering, Ahmadu Bello
Nigeria
eberadr@hotmail.com


Kolawole
Osinubi
Department of Civil Engineering, Ahmadu Bello University, Zaria, Nigeria
Department of Civil Engineering, Ahmadu Bello
Nigeria
muazbj@yahoo.com
Artificial Neural Networks
Black cotton soil
Cement kiln dust
Maximum dry density
Optimum moisture content
Soil stabilization
[[1] Kolay P. K., Rosmina A. B., and Ling, N. W. (2008). “Settlement prediction of soft tropical soil by Artificial Neural Network (ANN)”. The 12th International Conference of International Association for Computer Methods and Advances in Geomechanics (IACMAG) pp.18431848.##[2] Shahin M. A., Jaksa M. B., Maier H. R. (2001). “Artificial neural network applications in geotechnical engineering.” Australian Geomechanics, Vol. 36, No. 1, pp. 4962.##[3] Maizir, H. and Kassim, K. A. (2013). “Neural network application in prediction of axial bearing capacity of driven piles.” Proceedings of the International MultiConference of Engineers and Computer Scientists Vol I.##[4] Eidgahee, D. R., Fasihi, F. and Naderpour, H. (2015). Optimized artificial neural network for amazing soilwaste rubber shred mixture (in Persian). Sharif Journal of Civil Engineering, Vol. 31.2, No. 1.1, pp. 105 – 111.##[5] Fakharian, P., Naderpour, H., Haddad, A., Rafiean, A. H. and Eidgahee, D. R. (2018). A proposed model for compressive strength prediction of FRPconfined rectangular column in terms of Genetic expression Programming (GEP) (in Persian). Concrete Research.##[6] Shahin M.A., Maier H.R. and Jaksa M.B. (2002). “Predicting settlement of shallow foundations using Neural Networks.” Journalof Geotechnical and Geoenvironmental Engineering, ASCE, Vol. 128 No. 9, pp. 785793.##[7] Benali, A. Nechnech, A. and Ammar B. D. (2013). “Principal component analysis and Neural Networks for predicting the pile capacity using SPT.” International Journal of Engineering and Technology, Vol. 5, No. 1, February 2013.##[8] Van, D. B., Onyelowe, K. C., and VanNguyen, M. (2018). Capillary rise, suction (absorption) and the strength development of HBM treated with QD base Geopolymer. International Journal of Pavement Research and Technology.##[9] Phetchuay, C., Horpibulsuk, S., Arulrajah, A., Suksiripattanapong, C., and Udomchai, A. (2016). Strength development in soft marine clay stabilized by fly ash and calcium carbide residue based geopolymer. Applied Clay Science, Issue 127, pp. 134142.##[10] Bui, V. D. and Onyolowe, K.C. (2018). Adsorbed complex and laboratory geotechnics of Quarry Dust (QD) stabilized lateritic soils. Environmental Technology and Innovation., Issue 10, 3pp. 55363.##[11] Salahudeen, A. B., Eberemu O. A. and Osinubi, K. J. (2014). Assessment of cement kiln dusttreated expansive soil for the construction of flexible pavements. Geotechnical and Geological Engineering, Springer, Vol. 32, No. 4, PP. 923931.##[12] Rahman M.K., Rehman S. and AlAmoudi S.B. (2011). “Literature review on cement kiln dust usage in soil and waste stabilization and experimental investigation” IJRRAS 7 pp. 7778.##[13] Warren, K. W. and Kirby, T. M. (2004). “Expansive clay soil: A widespread and costly geohazard.” Geostrata, GeoInstitute of the American Society Civil Engineers, Jan pp. 2428.## [14] Balogun, L. A. (1991). “Effect of sand and salt additives on some geotechnical properties of lime stabilized black cotton soil.” The Nigeria Engineer, Vol 26, No 2, pp. 1524.##[15] Adeniji, F. A. (1991). “Recharge function of vertisolic Vadose zone in subsahelian Chad Basin.” Proceedings 1st International Conference on Arid Zone Hydrology and Water Resource, Maiduguri, pp. 331348.##[16] Salahudeen, A. B. and Akiije, I. (2014). Stabilization of highway expansive soils with high loss on ignition content kiln dust. Nigerian journal of technology (NIJOTECH), Vol. 33. No. 2, pp. 141 – 148.##[17] BS 1377 (1990). Methods of Testing Soil for Civil Engineering Purposes. British Standards Institute, London.##[18] BS 1924 (1990). Methods of Tests for Stabilized Soils. British Standards Institute, London.##[19] Mikaeil, R., ShaffieeHaghshenas, S., Ozcelik, Y., and ShaffieeHaghshenas, S. (2017). Development of Intelligent Systems to Predict Diamond Wire Saw Performance. Soft Computing in Civil Engineering, Vol. 1, No. 2, pp. 5269.##[20] Aryafar, A., Mikaeil, R., Doulati Ardejani, F., ShaffieeHaghshenas, S., and Jafarpour, A. (2018). Application of nonlinear regression and soft computing techniques for modeling process of pollutant adsorption from industrial wastewaters. Journal of Mining and Environment.##[21] Shahin, M. A. (2013). Artificial intelligence in geotechnical engineering: Applications, modelling aspects and future directions. Metaheuristics in Water, Geotechnical and Transportation Engineering, Elsevier, pp. 169 – 2014.##[22] Rezazadeh Eidgahee D, Haddad A, Naderpour H (2018) Evaluation of shear strength parameters of granulated waste rubber using artificial neural networks and group method of data handling. Sci Iran. https://doi.org/10.24200/sci.2018.5663.1408.##[23] Naderpour, H. Kheyroddin, A and GhodratiAmiri, G. (2010). “Prediction of FRPconfined compressive strength of concrete using artificial neural networks.” Composite Structures, Issue 92, pp. 2817–2829.##[24] Alavi, A. H., Ameri, M., Gandomi, A. H. and Mirzahosseini, M. R. (2011). Formulation of flow number of asphalt mixes using a hybrid computational method, Construction Building Materials, Vol. 25, No. 3, pp. 1338 – 1355.##[25] Golbraikh, A. and Tropsha, A. (2002). Beware of q2, J. Mol. Graph. Model., Vol. 20, No. 4, pp. 269–276.##[26] Smith, G. N. (1986). Probability and statistics in civil engineering. An Introduction, Collins, London.##[27] Das, S. K. and Sivakugan, N. (2010). Discussion of intelligent computing for modelling axial capacity of pile foundations. Canadian Geotechnical Journal, Vol. 47, pp. 928 – 930.##[28] Ahmadi, M., Naderpour, H, and Kheyroddin, A. (2014). “Utilization of artificial neural networks to prediction of the capacity of CCFT short columns subject to short term axial load.” Archives of civil and mechanical engineering, issue 14, pp. 510 – 517.##[29] Naderpour, H., Rafiean, A.H. and Fakharian, P., (2018). Compressive strength prediction of environmentally friendly concrete using artificial neural networks. Journal of Building Engineering, Issue 16, pp. 213219.##]
Process Parameter Optimization for minimizing Springback in Cold Drawing Process of Seamless Tubes using Advanced Optimization Algorithms
2
2
In tube drawing process, a tube is pulled out through a die and a plug to reduce its diameter and thickness as per the requirement. Dimensional accuracy of cold drawn tubes plays a vital role in further quality of end products and controlling rejection in manufacturing processes of these end products. Springback phenomenon is the elastic strain recovery after removal of forming loads, causes geometrical inaccuracies in drawn tubes. Further this leads to difficulty in achieving close dimensional tolerances. In the present work springback of EN 8 D tube material is studied for various cold drawing parameters. The process parameters in this work include die semi angle, land width and drawing speed. The experimentation is done using Taguchi’s L36 orthogonal array and then optimization is done in data analysis software Minitab 17.The results of ANOVA shows that 15 degree die semi angle,5 mm land width and 6 m/min drawing speed yields least springback. Furthermore, optimization algorithms named Particle Swarm Optimization (PSO),Simulated Annealing (SA) and Genetic Algorithm (GA) are applied which shows that 15 degree die semi angle, 10 mm land width and 8 m/min drawing speed results in minimal springback with almost 10.5 % improvement. Finally the results of experimentation are validated with Finite Element Analysis technique using ANSYS.
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72
90


Dadabhau
Karanjule
Sinhgad College of Engineering,Vadgaon,Pune,M.S.,India
Sinhgad College of Engineering,Vadgaon,Pune,M.S.,I
India
dadakaranjule1234@gmail.com


S.
Bhamare
Registrar,Dr.Babasaheb Ambedkar Technological University,Lonere, M.S., India, 402103
Registrar,Dr.Babasaheb Ambedkar Technological
India
bsunilsbhamare@gmail.com


Tianrong
Rao
Former Director,R and D Department, I.S.M.T. Limited, Ahmednagar, M.S.,India,414003
Former Director,R and D Department, I.S.M.T.
India
chrthota@yahoo.co.in
Cold drawing
Springback
Taguchi
Particle Swarm Optimization
Genetic Algorithm
Simulated Annealing
The Gaussian process modelling module in UQLab
2
2
We introduce the Gaussian process (GP) modelling module developed within the UQLab software framework. The novel design of the GPmodule aims at providing seamless integration of GP modelling into any uncertainty quantification workflow, as well as a standalone surrogate modelling tool. We first briefly present the key mathematical tools at the basis of GP modelling (a.k.a. Kriging), as well as the associated theoretical and computational framework. We then provide an extensive overview of the available features of the software and demonstrate its flexibility and userfriendliness. Finally, we showcase the usage and the performance of the software on several applications borrowed from different fields of engineering. These include a basic surrogate of a wellknown analytical benchmark function, a hierarchical Kriging example applied to wind turbine aeroservoelastic simulations and a more complex geotechnical example that requires a nonstationary, userdefined correlation function. The GPmodule, like the rest of the scientific code that is shipped with UQLab, is open source (BSD license).
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91
116


Christos
Lataniotis
ETH Zurich (Switzerland)
ETH Zurich (Switzerland)
Switzerland
latanioc@ethz.ch


Stefano
Marelli
ETH Zurich (Switzerland)
ETH Zurich (Switzerland)
Switzerland
marelli@ibk.baug.ethz.ch


Bruno
Sudret
ETH Zurich (Switzerland)
ETH Zurich (Switzerland)
Switzerland
sudret@ibk.baug.ethz.ch
UQLab
Gaussian process modelling
Kriging
MATLAB
Uncertainty Quantification