Estimating capital and operational costs of backhoe shovels

Material loading is one of the most critical operations in earthmoving projects. A number of different equipment is available for loading operations. Project managers should consider different technical and economic issues at the feasibility study stage and try to select the optimum type and size of equipment fleet, regarding the production needs and project specifications. The backhoe shovel is very popular for digging, loading and flattening tasks. Adequate cost estimation is one of the most critical tasks in feasibility studies of equipment fleet selection. This paper presents two different cost models for the preliminary and detailed feasibility study stages. These models estimate the capital and operating cost of backhoe shovels using uni-variable exponential regression (UVER) as well as multi-variable linear regression (MVLR), based on principal component analysis. The UVER cost model is suitable for quick cost estimation at the early stages of project evaluation, while the MVLR cost function, which is more detailed, can be useful for the feasibility study stage. Independent variables of MVLR include bucket size, digging depth, dump height, weight and power. Model evaluations show that these functions could be a credible tool for cost estimations in prefeasibility and feasibility studies of mining and construction projects

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

Stochastic-optimization of equipment productivity in multi-seam formations

Short and long range planning and execution for multi-seam coal formations (MSFs) are challenging with complex extraction mechanisms. Stripping equipment selection and scheduling are functions of the physical dynamics of the mine and the operational mechanisms of its components, thus its productivity is dependent on these parameters. Previous research studies did not incorporate quantitative relationships between equipment productivities and extraction dynamics in MSFs. The intrinsic variability of excavation and spoiling dynamics must also form part of existing models. This research formulates quantitative relationships of equipment productivities using Branch-and-Bound algorithms and Lagrange Parameterization approaches. The stochastic processes are resolved via Monte Carlo/Latin Hypercube simulation techniques within @RISK framework. The model was presented with a bituminous coal mining case in the Appalachian field. The simulated results showed a 3.51% improvement in mining cost and 0.19% increment in net present value. A 76.95yd³ drop in productivity per unit change in cycle time was recorded for sub-optimal equipment schedules. The geologic variability and equipment operational parameters restricted any possible change in the cost function. A 50.3% chance of the mining cost increasing above its current value was driven by the volume of material re-handled with 0.52 regression coefficient. The study advances the optimization process in mine planning and scheduling algorithms, to efficiently capture future uncertainties surrounding multivariate random functions. The main novelty includes the application of stochastic-optimization procedures to improve equipment productivity in MSFs --Abstract, page iii

Forecasting of sports fields construction costs aided by ensembles of neural networks

The paper presents an original approach to construction cost analysis and development of predictive models based on ensembles of artificial neural networks. The research was focused on the application of two alternative approaches of ensemble averaging that allow for combining a number of multilayer perceptron neural networks and developing effective models for cost predictions. The models have been developed for the purpose of forecasting construction costs of sports fields as a specific type of construction objects. The research included simulation and selection of numerous neural networks that became the members of the ensembles. The ensembles included either the networks of different types in terms of their structure and activation functions or the networks of the same type. The research also included practical implementation of the developed models for cost analysis based on a sports field BIM model. This case study examined and confirmed all of the four models’ predictive capabilities and superiority over models based on single networks for the particular problem. Verification including testing and the case study enabled selection of the best ensemble-based model that combined ten networks of different types. The proposed approach is prospective for fast cost analyses and conceptual estimates in construction projects

Neuro-fuzzy inference systems approach to decision support system for economic order quantity

Supply chain management (SCM) has a dynamic structure involving the constant flow of information, product, and funds among different participants. SCM is a complex process and most often characterized by uncertainty. Many values are stochastic and cannot be precisely determined and described by classical mathematical methods. Therefore, in solving real and complex problems individual methods of artificial intelligence are increasingly used, or their combination in the form of hybrid methods. This paper has proposed the decision support system for determining economic order quantity and order implementation based on Adaptive neuro-fuzzy inference systems - ANFIS. A combination of two concepts of artificial intelligence in the form of hybrid neuro-fuzzy method has been applied into the decision support system in order to exploit the individual advantages of both methods. This method can deal with complexity and uncertainty in SCM better than classical methods because they it stems from experts’ opinions. The proposed decision support system showed good results for determining the amount of economic order and it is presented as a successful tool for planning in SCM. Sensitivity analysis has been applied, which indicates that the decision sup- port system gives valid results. The proposed system is flexible and can be applied to various types of goods in SC

Development of Unit Price Indices and Estimating Inflation for Potable Water and Wastewater Pipeline Capital Works Construction

The importance of sustainable financial management of water and wastewater pipeline infrastructure has grown in recent years due to the increasing backlog of maintenance, renewal and replacement of aging water and wastewater infrastructure. As the water and wastewater infrastructure age, the condition of the water and wastewater infrastructure will continue to deteriorate increasing the cost for renewal and replacement. In response to the aging and deteriorating potable water and wastewater infrastructure Public Sector Accounting Board PS3150 and Regulation 453/07 under the Ontario Safe Drinking Water Act were established. PS3150 requires local governments to report their tangible capital assets along with their depreciation on financial statements. One key component of this reporting is determining the need for and cost of the replacement of these assets. Ontario Regulation 453/07 requires public utilities to prepare and submit long term financial plans for water systems. One key principle of the financial plans is that the expenses of operating water systems should be paid by revenues generated from providing the water systems. A crucial aspect of PS3150, Ontario regulation 453/07 and the financial management of water and wastewater infrastructure are accurate estimates of future capital works construction prices. Historically, construction indices are used to forecast construction prices. Engineering New Record (ENR) Construction Cost Index (CCI), Federal Highway Administration (FWHA) composite National Highway Construction Cost Index (NHCCI) and Consumer Price Index (CPI) have been used to estimate future construction prices of water and wastewater infrastructure in Canada. However, these indices do not accurately represent the circumstance of the water and wastewater infrastructure construction sector, which can lead to errors and inaccuracies in construction price forecasts. It is recommended sector specific construction indices be used to forecast construction prices. However, there are few construction indices available for the water and wastewater infrastructure sector and available indices are not based on actual construction data. This thesis presents a methodology to accurately estimate future construction prices for water and wastewater pipeline capital works based on actual construction price data. The methodology contains three components: construction data processing, development of unit price indices for watermain and sanitary sewer construction, and estimation of inflation in watermain and sanitary sewer construction. The data processing component cleans and transforms actual construction price data from the City of Niagara Falls from 1981 to 2014 into a centralized, organized and auditable construction price dataset. Based on the construction price dataset, unit price indices specific to the watermain and sanitary sewer construction sector were developed. Unit price indices were developed and calculated for watermain projects, pipes, valves, and hydrants, and sanitary sewer projects, pipes, and maintenance holes. Geometric Brownian Motion was used to estimate inflation in and forecast future construction prices for watermain and sanitary sewer capital works construction based on the developed unit price indices. A Microsoft Access relational database containing the data processing function, calculation of watermain and sanitary sewer unit price indices, and estimation of inflation was developed to improve the accuracy, efficiency and consistency of the methodology. Additionally, the methodology allows contractor markup in watermain and sanitary sewer construction and factors influencing watermain and sanitary sewer unit price indices to be examined. The inflation of watermain reference project construction is 5.79% per annum from 1982-2014, while the inflation of sanitary sewer reference project capital works construction is 4.66% per annum from 1981-2014. The inflation rates of watermain pipe, valve and hydrant construction from 1982-2014 are 6.36%, 5.09%, and 2.81% per annum, respectively. The inflation rates of sanitary sewer pipe and maintenance hole construction from 1981-2014 are 7.41% and 5.25% per annum, respectively. Inflation of watermain and sanitary sewer reference projects is above inflation of CPI, NRBCPI and LDCCT at 2.25%, 3.17% and 3.77% per annum, respectively, but below inflation of S&P/TSX composite index at 6.90% per annum. This indicates when forecasting future prices within a construction sector, the use of a proxy index will result in inaccurate estimates of future construction prices. In the water and wastewater pipeline construction sector the use of CPI, NRBCPI or LDCCT will result in significant underestimation of future construction prices. To obtain accurate estimates of future construction prices it is important to use sector specific indices which the developed unit price indices represent for the water and wastewater pipeline construction sector. In this thesis contractor markup is defined as a financial premium in excess of market inflation in the form of a per annum interest rate surcharge. Contractor markup includes risk premiums, overhead and profit. The contractor markups for watermain and sanitary sewer projects are 3.54% and 2.41%, respectively. As the number of tender bids submitted for a project increase, the unit price of reference projects generally decreases. This is caused by an increase in the competition among contractors resulting in a decrease in the unit prices of the reference projects as bidders attempt to win the project. The Infrastructure Stimulus Fund increased the total number of projects and the total value of projects in 2009 and 2010 but did not significantly alter the watermain and sanitary sewer unit price indices

AI and IoT Meet Mobile Machines: Towards a Smart Working Site

Infrastructure construction is society's cornerstone and economics' catalyst. Therefore, improving mobile machinery's efficiency and reducing their cost of use have enormous economic benefits in the vast and growing construction market. In this thesis, I envision a novel concept smart working site to increase productivity through fleet management from multiple aspects and with Artificial Intelligence (AI) and Internet of Things (IoT)

AI and IoT Meet Mobile Machines

Infrastructure construction is society's cornerstone and economics' catalyst. Therefore, improving mobile machinery's efficiency and reducing their cost of use have enormous economic benefits in the vast and growing construction market. In this thesis, I envision a novel concept smart working site to increase productivity through fleet management from multiple aspects and with Artificial Intelligence (AI) and Internet of Things (IoT)