Open Access Open Access  Restricted Access Subscription or Fee Access

Selection of Tower Cranes through ANN (Artificial Neural Network) Model

Vidhi Munshaw, Jyoti Trivedi

Abstract


In India, the construction sector has been increased due to large spending on the infrastructures in last few years. By such growth in infrastructure, rising trend of precast technology for real estate construction and high rise construction, there is a drive growth of the Indian tower crane industry. With the increasing demand of tower cranes, the selection of the suitable tower crane for a particular site also becomes important; as selection of appropriate tower crane can have a significant influence on time, cost and safety of construction operation and it also will govern the selection of other equipment’s. And also, there is an address of improvement for deployment of equipment in the five year plan. Many models are developed for selection but none has quantified them. Fuzzy logic and expert system based methods for mobile crane type selection has also been suggested. But no comprehensive and integrated systems are available, which can even select a given model for a given type of mobile crane and tower cranes. Further, these models have considered the productivity and cost factor, but not the safety and environmental factors for the selection of the equipment. This paper presents the factors to be considered while selection of a tower crane for a particular construction site; and the significance of each factor were found out using regression analysis through SPSS software and Matlab by ANN (Artificial Neural Network) tool. Factors such as project duration, project cost, site area, maximum height, number of towers, accessibility, availability of the space on site, terrain type of the site, availability of power lines, soil bearing capacity, maximum load to be carried on site, type of material to be lifted on site, storage yard location, relocation on site, safety and quality conditions specified by the client, cost of the tower crane are taken into consideration during preparation of the model. Also, an integrated tower crane selection model is prepared which helps in selection of; the type and model selection of tower crane, using historical data, case studies and pattern recognition tool of ANN model in Matlab. 

Cite this Article Vidhi Munshaw, Jyoti Trivedi. Selection of Tower Cranes through ANN (Artificial Neural Network) Model. Journal of Image Processing & Pattern Recognition Progress. 2016; 3(2): 67–79p. 


Keywords


Tower cranes, ANN (Artificial Neural Network), selection factors, pattern recognition tool

Full Text:

PDF

References


Abiola OS, Kupolati WK. Modelling Present Serviceability Rating of Highway Using Artificial Neural Network. International Journal of Sustainable Development (IJSD). 2014.

Al-Hussein M, Alkass S, Moselhi O. An Algorithm for Mobile Crane Selection and Location on Construction Sites. Construction Innovation. 2001; 91–105p.

Al-Hussein M, Alkass S, Moselhi O. Optimization Algorithm for Selection and on Site Location of Mobile Crane. J Constr Eng Manag. 2005.

Anil S, Mund A. Adaptive Probabilistic Neural Network-based Crane Type Selection System. J Constr Eng Manag. 2002.

Tushar Mehendale, MD. (E. I. Magazine, Interviewer). 2014.

Ramdev PV, MD. (E. India, Interviewer). 2014.

Ramdev PV. Equipment India. 2014.

Peurifoy, Schexnayder, Shapira. Construction Planning, Equipment and Methods. Tata McGraw-Hill; 2012.

Shapira AS, Lyachin B. Identification and Analysis of Factors Affecting Safety on Construction Sites with Tower Cranes. J Constr Eng Manag. 2009.

Shapira A, Glascock JD. Culture of Using Mobile Cranes for Building Construction. J Constr Eng Manag. 1996.

Shapira A, Goldenberg M. Soft Considerations in Equipment Selection for Building Construction Projects. J Constr Eng Manag. 2007.

Shapira A, Goldenberg M. AHP-Based Equipment Selection Model for Construction Projects. J Constr Eng Manag. 2011.

Shapira A, Simch M. AHP-Based Weighting of Factors Affecting Safety on Construction Sites with Tower Cranes. J Constr Eng Manag. 2009.

Shapira A, Simcha M. Measurement and Risk Scales of Crane-Related Safety Factors on Construction Sites. J Constr Eng Manag. 2009.

Shapira A, Filin S, Wicnudel A. Quantitative Analysis of Blind TowerCrane Lifts Using Laser-Scanning

Information. Construction Innovation. 2014.

Shapira A, Lucko G, Schexnayder CJ. Cranes For Building Construction Projects. J Constr Eng Manag. 2011.

Shapirao A, Schexnayder CJ. Selection of Mobile Cranes for Building Connstruction Projects. Constr Manage Econ. 1999.

Jha N. Construction Project Management- Theory and Practice. Dorling Kindersley (India) Pvt. Ltd.; 2011.

BM, York P, Brown M, et al. Optimization of the Predictive Ability of Artificial Neural Network (ANN) Models: A Comparison of Three ANN Programs and Four Classes of Training Algorithm. Eur J Pharm Sci. 2005.

Bernold LE. Spatial Integration in Construction. J Constr Eng Manag. 2002.

Bhavsar P. Neural Network for Strength Prediction of Cement. CEPT University; 2003.

Casals M, Forcada N, Roca X. (n.d.).

Chao LC, Skibniewski MJ. Neural Network Method of Estimating Construction Technology Acceptiability. J Constr Eng Manag. 1995.

Chitkara KK. Construction Project Management-Planning, Scheduling and Controlling. Tata McGraw-Hill; 2004.

Edwards DJ, Holt GD. Mini-Excavator Safety: Toward Innovative Stability Testing, Procurement, and Manufacture. J Constr Eng Manag. 2011.

Everett JG, Slocum AH. Cranium: Device for Improving Crane Productivity and Safety. J Constr Eng Manage. 1993.

Gao T, Ergan S, Akinci B, et al. Proactive Productivity Management at Job Sites: Understanding Characteristics of Assumptions Made for Construction Processes during Planning Based on Case Studies and Interviews. J Constr Eng Manag. 2014.

General Development Control Regulations, Ahmedabad.

Goodrum PM, Haas CT. Long-Term Impact of Equipment Technology on Labor Productivity in the US Construction Industry at the Activity Level. J Constr Eng Manage. 2004.

Hannon JJ. National Cooperative Highway Research Program. Washington, DC: Transportation Research Board; 2007.

Harris F. Modern Construction and Ground Engineering Equipment and Methods. Longman Singapore Publishers Ltd.; 1994.

Hasan S, Al-Hussein M, Gillis P. Advanced Simulation of Tower Crane Operation Utilizing System Dynamics Modeling and Lean Principles. Proceedings of the 2010 Winter Simulation Conference. 2010.

Hassan S, Al-Hussein M, Hermann UH, et al. Interactive and Dynamic Integrated Module for Mobile Cranes Supporting System Design. J Constr Eng Manage. 2010.

Jin Lin SC. (n.d.). Selection of Procurement Method for Building Maintenance Management: A Decision Making Model.

Al-Subhi KM, Al-Harbi. Application of the AHP in Project Management. Int J Proj Manage. 2001.

Lei Z, Behzadipour S, Hermann U. (n.d.). Application of Robotic Obstacle Avoidance in Crane Lift Path Planning.

Li H, Chan NK. The Use of Virtual Prototyping to Rehearse the Sequence of Construction Work Involving Mobile Cranes. Construction innovation. 2012.

Lu W, Liu AM, Rowlinson S, et al. Sharpening Competitive Edge through Procurement Innovation: Perspectives from Chinese International Construction Companies. J Constr Eng Manage. 2013.

MZA, HH, NQS, et al. Regression and ANN Models for Estimating Minimum Value of Machining Performance. Application of Mathematical Modelling. 2011.

Milier G, Furneaux C, Davis P, et al. Built Environment Procurement Practice: Impediments to Innovation and Opportunities for Changes. Built Environment Industry Innovation Council; 2009.

Monnot JM, Williams RC. Construction Equipment Telematics. J Constr Eng Manage. 2011.

Negnevitsky M. Artificial Intelligence: A Guide to Intelligence Systems. 2002.

Parsertrungruang T, Hadikusumo B. System Dynamics Modelling of Machine Downtime for Small to Medium Highway Contractors. Engineering, Construction and Architectural Management. 2007.

Phogat VS, Singh AP. Selection of Equipment for Construction of a Hilly Road using Multi Criteria Approach. Procedia Soc Behav Sci. 2013.

Prasertrungruang T, Hadikusumo B. Heavy Equipment Management Practices and Problems in Thai Highway Contractors. Engineering, Construction and Artichectural Management. 2007.

Prasertrungruang T, Hadikusumo HW. Modeling the Dynamics of Heavy Equipment Management Practices and Downtime in Large Highway Contractors. J Constr Eng Manag. 2000.

SR, BI, SA. Feasibility of Automated Monitoring of Lifting Equipment in Support of Project Control. J Constr Eng Manag. 2005.

Schexnavder CJ, David SA. Past and Future of Construction Equipment-Part IV. J Constr Eng Manage. 2002.

Seokho Chi CH. Image-Based Safety Assessment: Automated Spatial Safety Risk Identification of Earthmoving and Surface Mining Activities. J Constr Eng Manage © ASCE. 2011.

Shah S. Evaluation of Productivity, Safety and Type Selection for Tower Cranes. 2012.

Stemp BA, Walewski J. Lampson TransiLift Mobile Crane: Concept, Design and Use. J Constr Eng Manage. 2011.

TSA, TG, KM, et al. Artificial Neural Network (ANN) Prediction of Compressive Strength of VARTM Processed Polymer Composites. Comput Mater Sci. 2004.

Taghaddos H, Abou Rizk S, Mohamed Y, et al. Simulation Based Auction Protocol for Resource Scheduling Problems. J Constr Eng Manag. 2012.

Tam CM, Tong TK, Chan WK. Genetic Algorithm for Optimizing Supply Locations Around Tower Crane. J Constr Eng Manag. 2001.

Trivedi JS, Kumar R. Optimization of Construction Equipment Utility Using Neural Network. NICMAR: Jurnal of Construction Management. 2013.

Waris M, Liew SM, Khamidi MF, et al. Criteria for the Selection Of Sustainable Onsite Construction Equipment. International Journal of Sustainable Built Environment. 2014.

Zhang P, Harris FC, Olomolaiy PO, et al. Location Optimization for a Group of Tower Cranes. J Constr Eng Manag. 1999.

Zhang P, Harris FC, Olomolaiye PO, et al. Location Optimization for a Group of Tower Cranes. J Constr Eng Manage. 1999.


Refbacks

  • There are currently no refbacks.


This site has been shifted to https://stmcomputers.stmjournals.com/