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Construction Equipment Management for Engineers, Estimators, and Owners

Authors:
  • Gransberg & Associates Inc.

Abstract

Based on the authors' combined experience of seventy years working on projects around the globe, Construction Equipment Management for Engineers, Estimators, and Owners contains hands-on, how-to information that you can put to immediate use. Taking an approach that combines analytical and practical results, this is a valuable reference for a wide range of individuals and organizations within the architecture, engineering, and construction industry. The authors delineate the evolution of construction equipment, setting the stage for specific, up-to-date information on the state-of-the-art in the field. They cover estimating equipment ownership, operating cost, and how to determine economic life and replacement policy as well as how to schedule a production-driven, equipment-intensive project that achieves target production rates and meets target equipment-related unit costs and profits. The book includes a matrix for the selection of equipment and identifies common pitfalls of project equipment selection and how to avoid them. It describes how to develop an OSHA job safety analysis for an equipment-intensive project, making this sometimes onerous but always essential task easier. The authors' diverse and broad experience makes this a book that ranges from the rigorous mathematical analysis of equipment operations to the pragmatic discussion of the equipment maintenance programs needed to guarantee that the production predicted in a cost estimate occurs.
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Gransberg, D.D., C. M. Popescu and R.C. Ryan, Construction Equipment Management for Engineers,
Estimators, and Construction Managers, Taylor and Francis Books, Inc., ISBN 0-8493-4037-3; 2006, 544
pages.
https://www.crcpress.com/Construction-Equipment-Management-for-Engineers-Estimators-and-
Owners/Gransberg-Popescu-Ryan/p/book/9780849340376
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TABLE OF CONTENTS
Construction Equipment Management for Engineers, Estimators, and Construction Managers
by: Douglas D. Gransberg, P.E., Ph.D., Calin M. Popescu, P.E., Ph.D.
and Richard C. Ryan, C.P.C.
Chapter 1: The Evolution of Heavy Construction Equipment
1.0 The Role of Heavy Construction Equipment
1.2 Tools to Machines
1.3 Development of Earthmoving, Excavating and Lifting Machines
1.4 Heavy Construction Equipment Today
1.5 The Future of Heavy Construction Equipment
References
Chapter 2: Cost of Owning and Operating Construction Equipment
2.0 Introduction
2.1 Ownership Cost
2.1.1 Initial Cost
2. 1. 2 Depreciation
2.1.2.1 Straight-Line Depreciation
2.1.2.2 Sum-of-Years’-Digits Depreciation
2.1.2.3 Double-Declining Balance Depreciation
2. 1. 3 Investment (or Interest) Cost
2. 1. 4 Insurance Tax, and Storage Costs
2.2 Total Ownership Cost
2.3 Cost of Operating Construction Equipment
2.3.1 Repair and Maintenance cost
2.3.2 Tire cost
2.3.3 Consumable cost
2.3.3.1 Fuel Cost
2.3.3.2 Lubricating Oil Cost
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2.3.3 Mobilization and Demobilization cost
2.3.4 Equipment operator cost
2.3.5 Special Items Cost
2.4 Methods of Calculating Ownership and Maintenance cost
2.4.1 Caterpillar Method
2.4.1.1 Ownership Costs
2.4.1.2 Operating Costs
2.4.2 Corps of Engineers Method
2.4.2.1 Ownership Costs
2.4.2.2 Operating Costs
2.4.3 AGC Method
2.4.3.1 Ownership Costs
2.4.3.2 Operating Costs
2.4.4 Peurifoy/Schexnayder Method
2.4.4.1 Ownership Costs
2.4.4.2 Operating Costs
2.4.5 Comparison of costs calculated by different methods
2.5 Summary
References
LCCA
Sustainability- fuel use, emissions.
Intelligent compaction, intelligent
GPS, GIS, automated machine guidance.
Chapter 3: Equipment Life and Replacement Procedures
3.0 Introduction
3.1 Equipment life
3.1.1 Physical life
3.1.2 Profit life
3.1.3 Economic life
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3.1.3.1 Depreciation costs and replacement
3.1.3.2 Inflation
3.1.3.3 Investment costs
3.1.3.4 Maintenance and repair costs
3.1.3.5 Downtime
3.1.3.6 Obsolescence
3.1.3.7 Summary of costs
3.2 Replacement analysis
3.2.1 Theoretical methods
3.2.1.1 Intuitive method
3.2.1.2 Minimum cost method
3.2.1.3 Maximum profit method
3.2.1.4 The payback period method
3.2.1.5 Mathematical modeling method
3.2.2 Practical methods
3.2.2.1 Public agency methods
3.2.2.1.1 Texas Department of Transportation
3.2.2.1.2 Montana Department of Transportation
3.2.2.1.3 Louisiana Department of Transportation and Development
3.2.3 Sensitivity analysis on theoretical methods
3.2.3.1 Sensitivity analysis on minimum cost method
3.2.3.2 Sensitivity analysis on maximum profit method
3.2.4 Comparison and discussion of sensitivity analysis results
3.3 Replacement equipment selection
3.3.1 Replacement decision-making
3.3.1.1 Decision-making foundations
3.3.1.2 Examination of alternatives
3.3.1.3 Decision to invest
3.3.2 General factors
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3.3.2.1 Machine productivity
3.3.2.2 Product features and attachments
3.3.2.3 Dealer support
3.3.2.4 Price
3.4 Summary
References
Chapter 4: Earthmoving, Excavating and Lifting Equipment Selection
4.0 Introduction
4.1 Basic Considerations for Equipment Selection
4.2 Earthmoving and Excavating Considerations
4.2.1 Tires and Tracks
4.2.2 Blades and Buckets
4.2.3 Accessories and Attachments
4.2.4 Earthmoving and Excavating Work
4.2.4.1 Earthmoving and Excavating Work Activities
4.3 Earthmoving Equipment Selection
4.3.1 Bulldozer
4.3.1.1 Bulldozer Production
4.3.2 Front-end Loaders
4.3.2.1 Loader Production
4.3.3 Motor Graders
4.3.3.1 Motor Grader Production
4.3.3.2 Box Blades
4.3.4 Scrapers
4.3.4.1 Scraper Production
4.3.5 Trucks
4.3.5.1 Truck Production
4.4 Excavating Equipment Selection
4.4.1 Excavators
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4.4.1.1 Excavator Production
4.4.2 Backhoes
4.4.2.1 Backhoe Production
4.4.3 Front Shovels
4.5 Lifting Considerations
4.5.1 Placing a Load
4.5.2 The Operator
4.5.3 Mobilization and Set-up
4.5.4 Booms
4.5.5 Forks
4.5.6 Rigging
4.5.7 Jibs
4.5.8 Hoist Speed
4.6 Lifting Equipment Selection
4.6.1 Cranes
4.6.1.1 Telescoping Boom Mobile Cranes
4.6.1.2 Lattice Boom Crawler Cranes
4.6.1.3 Tower Cranes
4.6.2 Forklifts
4.6.3 Personnel Lifts
References
Chapter 5: Advanced Methods in Estimating and Optimizing Construction Equipment
System Productivity
5.0 Introduction
5.1 Background
5.2 Peurifoy's Method of Optimizing Productivity
5.2.1 Rimpull
5.2.2 Cycle Time and Optimum Number of Units
vii
5.3 Phelps' Method
5.3.1 Fixed Time
5.3.2 Variable Time
5.3.3 Instantaneous and Sustained Cycle Time
5.4 Optimizing the Hauling System Based on Loading Facility Characteristics
5.4.1 Load Growth Curve Construction
5.4.2 Belt Conveyor Load Growth Curve
5.4.3 Rounding Based On Productivity
5.4.4 Rounding Based on Profit Differential
5.4.5 Optimizing with Cost Index Number
5.4.6 Selecting Optimum Size Haul Unit
5.4.7 Optimizing the System with a Belt Conveyor
5.4.8 Selecting Optimum Size Loading Facility
5.5 Comments on Optimizing Equipment Fleets
References
Chapter 6: Stochastic Methods for Estimating Productivity
6.0 Introduction
6.1 Background
6.2 Developing mathematical models.
6.2.1 Probability theory
6.2.2 Statistical analysis
6.2.3 Historical data
6.2.3.1 Cost performance data
6.2.3.2 Production performance data
6.2.3.4 Maintenance failure data
6.3 Simulations
6.3.1 Monte Carlo simulation theory
6.3.1.1 Developing Monte Carlo simulation input
viii
6.3.1.2 Analyzing Monte Carlo simulation output
6.3.2 Other simulations
6.3.2.1 Developing input
6.3.2.2 Analyzing output
6.4 Expected production
6.4.1 Cost estimating factors
6.4.2 Production management factors
6.5 Validating simulation models
6.5.1 Verifying assumptions and inputs
6.5.2 Sensitivity analysis
References
Chapter 7: Scheduling Equipment-Intensive Horizontal Construction Projects
7.0 Introduction
7.1 Background
7.2 Precedence Diagramming Method
7.2.1 Determining the Critical Path
7.2.1.1 Forward Pass Calculations
7.2.1.2 Backward Pass Calculations
7.2.1.3 Calculating Float
7.2.2 Critical resource identification
7.2.3 Resource loading the schedule
7.2.4 Cost loading the schedule
7.3 Linear Scheduling Method
7.3.1 Identifying production-driven activities
7.3.2 Establishing Production Rates
7.3.3 Lines, bars, and blocks
7.3.4 Converting to PDM
7.4 Developing equipment resource packages
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7.4.1 Rules for developing crew sizes
7.4.2 Developing crew costs
7.5 Establishing project management assessment parameters
7.5.1 Minimum required daily production
7.5.2 Expected daily production
7.5.3 Allowable cycle time variation
7.5.4 Cost and unit targets
7.6 Summary
References
Chapter 8: Scheduling Lifting Equipment Vertical Construction Projects
8.0 Introduction
8.1 Lifting and Vertical Construction
8.2 Lifting Productivity
8.3 Scheduling Lifting for High-Rise Work
8.3.1 The Lifting Strategy
8.3.2 Typical Lifting Activities for High-Rise Construction
8.4 Concrete Placing Cranes
8.4.1 Bucket Pouring
8.4.2 Pumping
8.4.3 Scheduling and Ordering Concrete
8.5 Tower Crane Erection and Dismantle
References
Chapter 9: Rent, Lease, or Buy Decision
9.0 Introduction
9.1 Acquiring Heavy Equipment
9.2 Financing Methods
9.2.1 Outright Cash Purchase
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9.2.2 Conventional Financing Purchase
9.2.3 Leasing
9.2.4 Renting
9.2.5 Rent to Own (Rental Purchase)
9.2.5 Rental and Lease Contract Considerations
9.3 Equipment Financing Comparison
9.3.1 Acquisition Comparison
9.4 Rental and Lease Contract Considerations
9.5 The Buy, Lease or Rent Decision
References
Chapter 10: Construction Equipment Maintenance
10.0 Introduction
10.1 Need for a Maintenance Program
10.1.1 Types of Maintenance Programs
10.2 Designing the maintenance program
10.2.1 Define objectives and goals
10.2.2 Establish responsibility and authority
10.2.3 Actions and Controls
10.3 Preventive Maintenance and Predictive Maintenance Management
10.3.1 Preventive Maintenance
10.3.2 Availability and Reliability
10.3.2.1 Availability
10.3.2.1.1 Downtime
10.3.2.1.2 Uptime
10.3.2.1.3 Availability calculation
10.3.2.2 Reliability
10.3.3 Oil sample analysis
10.3.4 Preventive Maintenance Reporting Systems
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10.4 Maintenance Performance Control
10.4.1 Maintenance Labor Productivity Control
10.4.2 Training
10.4.3 Practical maintenance guidelines
10.5 Preventive Maintenance Programs
10.5.1 Operator Training
10.5.2 Maintenance Guidelines
10.5.3 Maintenance Cost Control Metrics
10.6 Field Maintenance
10.6.1 Field maintenance personnel and support facilities
10.6.2 Design features for field maintenance facilities
10.6.3 Specialized Maintenance Tools
References
Chapter 11: Construction Equipment Site Safety
11.0 Introduction
11.1 Safety is a Profit Center
11.2 The Job Safety Plan
11.3 Heavy Construction Equipment Site Safety Considerations
11.4 Job Safety Analysis for Earthmoving
11.5 Lifting Safety
11.5.1 Safety Considerations
11.5.2 Pre-Lift Meetings
11.6 OSHA Accident Reporting and Record Keeping
11.6.1 Reporting
11.6.2 Record Keeping
11.7 Safety Requirements for Construction Equipment
References
xii
Chapter 12: Construction Equipment Security
12.0 Introduction
12.1 Security issues
12.2 Theft and vandalism
12.3 Security Programs
12.3.1 Security Planning
12.2.2 Security inventories and markings
12.3.3 Job Site Security
12.3.4 Heavy Equipment Protection
12.4 Insurance
12.4.1 Policy Information
12.4.2 Types of Policies
12.4.3 Rates and Deductibles
12.5 Summary
References
Chapter 13: Inventory Procedures and Practices
13.0 Introduction
13.1 Objectives of inventory control
13.2 Equipment and parts identification
13.2.1 Equipment identification
13.2.2 Parts identification
13.3 Inventory Record Keeping and Management Systems
13.3.1 Paper Based Record Keeping
13.3.2 Electronic Record Keeping
13.4 Equipment Location And Utilization
13.4.1 Geographic Information System applications
13.4.2 Global Positioning System equipment fleet management systems
13.4.3 Comparing GPS systems
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13.5 summary
References
Appendices
Appendix A: Corps of Engineers Equipment Ownership Cost Tables for Chapter 2
Appendix B: Lifting tables and other support material for Chapter 4
... The paper's structure comprises the subsequent sections: an overview of prior research, a comprehensive explanation of the methodology employed, the research outcomes, specifically, the results of ANN training and Pareto analysis, and a final analysis of the obtained results along with a suggestion for future research. According to Gransberg et al. (2006), for proper maintenance of heavy machinery, the data from its history card has to be collected. Previous studies reveal that early detection of equipment faults is a very beneficial way to increase the reliability of equipment such as excavators availability (Rosaler et al., 2007). ...
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This book contains the proceedings of the 21st International Conference on Smart Business Technologies (ICSBT 2024). This year, ICSBT is held in collaboration with the ESEO, which hosts this event in Dijon, France, on July 9-11, 2024. It was sponsored by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC). ICSBT 2024 was also organized in cooperation with the ACM Special Interest Group on Management Information Systems. The International Conference on Smart Business Technologies (formerly known as ICE-B - International Conference on e-Business), aims at bringing together researchers and practitioners who work on e-Business technology and its applications. The scope of the conference covers low-level technological issues, such as technology platforms, internet of things, artificial intelligence, data science and web services, but also higher-level issues, such as business processes, business intelligence, digital twins, value setting and business strategy. Furthermore, it covers different research approaches (like qualitative cases, experiments, forecasts, and simulations) to address these issues and different possible application domains (like manufacturing, service management and trade systems) with their own specific needs and requirements. We invite both more academic and practical oriented submissions, but we are especially interested in academic research with a potential practical impact and practical research papers with theoretical implications. ICSBT 2024 received 27 paper submissions from 13 countries of which 14.8% were accepted and published as full papers. A double-blind paper review was performed for each submission by at least 2 but usually 3 or more members of the International Program Committee, which is composed of established researchers and domain experts. The high quality of the ICSBT 2024 program is enhanced by the keynote lecture delivered by distinguished speakers who are renowned experts in their fields: Samuel Fosso Wamba (Toulouse Business School, France) and Sukhpal Singh Gill (Queen Many University of London, United Kingdom). All presented papers will be available at the SCITEPRESS Digital Library and will be submitted for evaluation for indexing by SCOPUS, Google Scholar, The DBLP Computer Science Bibliography, Semantic Scholar, Engineering Index and Web of Science / Conference Proceedings Citation Index. As recognition for the best contributions, several awards based on the combined marks of paper reviewing, as assessed by the Program Committee, and the quality of the presentation, as assessed by session chairs at the conference venue, are conferred at the closing session of the conference. Authors of selected papers will be invited to submit extended versions for inclusion in a forthcoming book of ICSBT Selected Papers to be published by Springer, as part of the CCIS Series. Some papers will also be selected for publication of extended and revised versions in the special issue of the Socio-Economic Planning Sciences and IMA Journal of Management Mathematics. The program for this conference required the dedicated effort of many people. Firstly, we must thank the authors, whose research efforts are herewith recorded. Next, we thank the members of the Program Committee and the auxiliary reviewers for their diligent and professional reviewing. We would also like to deeply thank the invited speakers for their invaluable contribution and for taking the time to prepare their talks. Finally, a word of appreciation for the hard work of the INSTICC team; organizing a conference of this level is a task that can only be achieved by the collaborative effort of a dedicated and highly competent team. We wish you all an exciting and inspiring conference. We hope to have contributed to the development of our research community, and we look forward to having additional research results presented at the next edition of ICSBT, details of which are available at https://icsbt.scitevents.org.
... Selecting the appropriate machine type for a task requires understanding field operation costs. Failing to accurately estimate equipment costs can lead to hardship [6]. ...
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... The basis for the successful completion of earthworks is the assessment of the productivity of construction machinery (Mohamed, 2017). It is important to choose the machine that will achieve the best productivity for a given job (Gransberg et al., 2006). The lower the idling of machines, the higher the productivity of machines (Zou and Kim H., 2007). ...
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Construction generally takes place in very variable and dynamic conditions due to the presence of machinery, transport of materials, movement of workers, and the progress of construction. In order to solve the problem of site variable and dynamic conditions, two major research topics may stand out, more precisely, off-site performance planning and on-site performance tracking and monitoring. Earthworks commonly involve extensive utilization of various construction machines, mainly excavators and tipper (or dump) trucks. Tracking and monitoring of earthworks, based on collected quality data from the construction site, are necessary to detect discrepancies between the planned and actual work efficiency of construction machinery. The main topic of this paper is the time study analysis of one of the standard technological processes in earthworks – loading and transporting materials by tipper trucks. On-site data were obtained using the stopwatch method with the purpose of determining the actual work efficiency of the tipper truck. This paper aims to propose a research framework for clear insight into the progress effectiveness of earthworks and the actual work efficiency of the tipper truck. The purpose of applying the research framework is to form a base for tracking and monitoring the work of tipper trucks so the right decisions can be made in a timely manner. Such can enhance earthworks progress by using the appropriate number of tipper trucks and their better utilization.
... The basis for the successful completion of earthworks is the assessment of the productivity of construction machinery (Mohamed, 2017). It is important to choose the machine that will achieve the best productivity for a given job (Gransberg et al., 2006). The lower the idling of machines, the higher the productivity of machines (Zou and Kim H., 2007). ...
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Construction generally takes place in very variable and dynamic conditions due to the presence of machinery, transport of materials, movement of workers, and the progress of construction. In order to solve the problem of site variable and dynamic conditions, two major research topics may stand out, more precisely, off-site performance planning and on-site performance tracking and monitoring. Earthworks commonly involve extensive utilization of various construction machines, mainly excavators and tipper (or dump) trucks. Tracking and monitoring of earthworks, based on collected quality data from the construction site, are necessary to detect discrepancies between the planned and actual work efficiency of construction machinery. The main topic of this paper is the time study analysis of one of the standard technological processes in earthworks – loading and transporting materials by tipper trucks. On-site data were obtained using the stopwatch method with the purpose of determining the actual work efficiency of the tipper truck. This paper aims to propose a research framework for clear insight into the progress effectiveness of earthworks and the actual work efficiency of the tipper truck. The purpose of applying the research framework is to form a base for tracking and monitoring the work of tipper trucks so the right decisions can be made in a timely manner. Such can enhance earthworks progress by using the appropriate number of tipper trucks and their better utilization.
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Purpose The equipment resale price plays an important role in calculating the optimum time for equipment replacement. Some of the existing models that predict the equipment resale price do not take many of the influencing factors on the resale price into account. Other models consider more factors that influence equipment resale price, but they still with low accuracy because of the modeling techniques that were used. An easy tool is required to help in forecasting the resale price and support efficient decisions for equipment replacement. This research presents a machine learning (ML) computer model helping in forecasting accurately the equipment resale price. Design/methodology/approach A measuring method for the influencing factors that have impacts on the equipment resale price was determined. The values of those factors were measured for 1,700 pieces of equipment and their corresponding resale price. The data were used to develop a ML model that covers three types of equipment (loaders, excavators and bulldozers). The methodology used to develop the model applied three ML algorithms: the random forest regressor, extra trees regressor and decision tree regressor, to find an accurate model for the equipment resale price. The three algorithms were verified and tested with data of 340 pieces of equipment. Findings Using a large number of data to train the ML model resulted in a high-accuracy predicting model. The accuracy of the extra trees regressor algorithm was the highest among the three used algorithms to develop the ML model. The accuracy of the model is 98%. A computer interface is designed to make the use of the model easier. Originality/value The proposed model is accurate and makes it easy to predict the equipment resale price. The predicted resale price can be used to calculate equipment elements that are essential for developing a dependable equipment replacement plan. The proposed model was developed based on the most influencing factors on the equipment resale price and evaluation of those factors was done using reliable methods. The technique used to develop the model is the ML that proved its accuracy in modeling. The accuracy of the model, which is 98%, enhances the value of the model.
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The preliminary design phase transforms the investor’s ideas and information into general plans, drawings and specifications and helps the project managers continue the next steps in the projects. Material selection is an essential task to incorporate sustainable content into the construction industry. This paper reviewed materials selection studies and proposed a comprehensive method to compare the economic performance of construction material alternatives in this phase. The article constructed a life cycle costing equation used in the preliminary design stage that compared the total expected cost of the alternatives throughout the road construction project life cycle. The proposed life cycle cost method was divided into two scenarios according to the available information in the preliminary design phase and the material-dependent costs. A case study in Vietnam about the selection between baked bricks and concrete bricks was presented to demonstrate the proposed equations and models. The result showed that the baked bricks took the top priority due to the differences in cost items (e.g., transportation costs). Furthermore, to validate the models, they were applied to compare the expected and actual cost of 18 road construction projects in Vietnam – the differences are all below 10%. Further research may focus on building a database of models by applying the Building Information Model (BIM).
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