The computer in the construction industry

Najafi, Fazil

doi:10.1016/0045-7949(91)90249-l

Cited by 4 publications

(1 citation statement)

References 4 publications

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“…Wire electrical discharge machining (WEDM) can be effectively used for the machining of difficult-to-cut materials and has the ability to produce accurate, precise, and complex surfaces with the help of very thin wires [3,4]. However, the selection of the proper machining parameters to achieve the desired machining performance has always been a challenging task for industry [5]. Ugrasen et al [24] BPNN with 1-hidden layer Levenberg-Marquardt -T ON , pulse-off-time, I P , bed speed VMRR, accuracy, R a ANN model with 70% training data gave best prediction as that of model with 50% or 60% training data.…”

Section: Introductionmentioning

confidence: 99%

Response Surface Methodology and Artificial Neural Network-Based Models for Predicting Performance of Wire Electrical Discharge Machining of Inconel 718 Alloy

Lalwani

Sharma

Pruncu

et al. 2020

JMMP

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This paper deals with the development and comparison of prediction models established using response surface methodology (RSM) and artificial neural network (ANN) for a wire electrical discharge machining (WEDM) process. The WEDM experiments were designed using central composite design (CCD) for machining of Inconel 718 superalloy. During experimentation, the pulse-on-time (TON), pulse-off-time (TOFF), servo-voltage (SV), peak current (IP), and wire tension (WT) were chosen as control factors, whereas, the kerf width (Kf), surface roughness (Ra), and materials removal rate (MRR) were selected as performance attributes. The analysis of variance tests was performed to identify the control factors that significantly affect the performance attributes. The double hidden layer ANN model was developed using a back-propagation ANN algorithm, trained by the experimental results. The prediction accuracy of the established ANN model was found to be superior to the RSM model. Finally, the Non-Dominated Sorting Genetic Algorithm-II (NSGA- II) was implemented to determine the optimum WEDM conditions from multiple objectives.

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Section: Introductionmentioning

confidence: 99%

Response Surface Methodology and Artificial Neural Network-Based Models for Predicting Performance of Wire Electrical Discharge Machining of Inconel 718 Alloy

Lalwani

Sharma

Pruncu

et al. 2020

JMMP

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Computing and the Construction Industry

Scott¹,

Ponniah²,

Saud³

1994

Structural Survey

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Reviews the general state of computer systems in the construction industry. Reports on a survey aimed at examining the attitudes of people in the construction industry regarding computer applications and reveals the importance of computers in areas such as engineering and management. Highlights the prospects of computer applications in the future and concludes that advances in technology are going to require a change in attitude between all parties in the industry.

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A Study of the Constraints Affecting the Proper Utilization of Computer Application Software in Resource Management in Chennai Construction Companies

.¹

2015

IJRET

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Construction is one of the area in which computer application software are highly required to perform different task at various stages of project construction. Computers have been used to enhance the effectiveness of construction management. Efficient utilization of computer application software is a key to enhancing the proper management of construction activities which will contribute to the successful implementation of construction project. Careful selection of computer application software is required by the construction companies for proper management of their resources. The difficulty about computerization especially with regard to the use of computer application software in resource management require the knowledge and expertise of those working in construction companies, and who are directly involved in the management of construction resources. The study was carried out to determine the obstacles confronting the proper utilization of computer application software in resource management. The result of the questionnaire survey used in this research work clearly indicated those constraints, which include, nonunderstanding about software potentials, lack of qualified personnel to use the software, unaware of most of the resource management software available in the market, communication gap between the vendors and users which contribute to nonunderstanding of the full scope of the software, greater-know-how required from staff, among numerous others. A total of 22 construction companies were selected for the study. An open ended questionnaire which consist of 22 questions with regard to the constraints affecting the proper utilization of computer application software in resource management was designed, validated, and distributed among Engineers, Project Managers, as well as executives who are directly involved in the activities of the construction site.

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The computer in the construction industry

Cited by 4 publications

References 4 publications

Response Surface Methodology and Artificial Neural Network-Based Models for Predicting Performance of Wire Electrical Discharge Machining of Inconel 718 Alloy

Response Surface Methodology and Artificial Neural Network-Based Models for Predicting Performance of Wire Electrical Discharge Machining of Inconel 718 Alloy

Computing and the Construction Industry

A Study of the Constraints Affecting the Proper Utilization of Computer Application Software in Resource Management in Chennai Construction Companies

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