Traditional Life Cycle Cost Research Articles

An improved stochastic life-cycle cost analysis model for examining the impact of environmental policy instruments on construction equipment replacement

Both contractors and governments are eager for a model that can assist them to examine the impact of environmental policy instruments (EPIs) on construction equipment replacement from a stochastic perspective. Therefore, this study introduces an improved stochastic life cycle cost (LCC) analysis model and designs eight scenarios in which different EPIs are considered for such examination. The limitation of the traditional LCC analysis model has been examined. The effectiveness of the improved LCC analysis model is demonstrated with its application in a 2002 Sterling LT9500 dump truck in the US market. The results show that: (1) The traditional LCC analysis model is not robust due to failing to consider costs incurred by EPIs and a lack of stochastic perspective. (2) Mandatory administration policy instruments (EPIA) can promote earlier replacement of construction equipment, but EPIA can put a heavy financial burden on contractors. (3) When economic incentive policy instruments such as grants or subsidies programs (EPIBGS) and tax credits programs (EPIBT) are not lucrative enough, it is very hard to involve contractors in these programs, which will hardly motivate them to replace their construction equipment earlier. (4) A combination of EPIA, EPIBGS and EPIBT can work better, which can motive contractors to earlier replace existing equipment, and reduce the financial burden of contractors to some extent.

Life Cycle Cost versus Life Cycle Investment – A new Approach

The paper proposes a model for the life cycle of physical assets that includes the maintenance policy, because it has direct implications on the equipment’s Return On Investment (ROI) and Life Cycle Cost; the developed model can be applied to any type of physical asset. The model is called Life Cycle Investment (LCI) instead of the traditional Life Cycle Cost (LCC). The paper proposes a new methodology based on the modified economic life cycle and lifespan methods by including the maintenance policy using maintenance Key Performance Indicators (KPI), namely Availability, based on the Mean Time Between Failures (MTBF) and the Mean Time To Repair (MTTR). The benefits (profits) that result from the asset’s Availability must be balanced with the initial investment and the variable maintenance investment along its life, which has relation with the maintenance policy and the ROI.

Decision-making for Civil Infrastructures Incorporating the time-Varying Effect of Risk Preference

Life cycle cost (LCC) analysis offered a useful framework to think about the efficiency of infrastructures investment. However, the widely accepted algorithm of the LCC doesn’t account for the decision maker's attitude toward the risk of future consequences. The cumulative prospect theory (CPT) can be used to reveal the decision maker's preference under different risk and uncertainty conditions, and has been applied in literature to some design optimization issues. Nevertheless, previous research didn’t consider the fact that people's risk preference may vary with time, i.e., there is a reasonable tendency that people will be more risk-averse to the near future losses than to the far future losses. Recognizing this, this paper adopts the CPT model in the minimum expected cost analysis, and moreover incorporates the time-varying weighting function included in the CPT model to reflect the possible changing of risk preference toward future events. A seismic retrofit optimization of a dam built in strong earthquake-prone area is used to demonstrate the application of the method, and it is found that considering the time-varying of risk preference leads to a low design level compared with that by the CPT model, but a high design level compared with the traditional LCC.

A model for earthquake risk management based on the life-cycle performance of structures

Over 50 years of design life, buildings are exposed to different magnitudes and frequencies of earthquakes that require consideration of life-cycle cost (LCC). The LCC entails quantifying the building performance under seismic hazard and investments throughout the life of the structures. Traditional LCC utilises probabilities of being in different damage states. However, for buildings with inherent irregularities (e.g. vertical irregularity and plan irregularity), these probabilities are not readily available. In this paper, a system-based approach, utilising fuzzy set theory, is used to quantify the possibility of being in different damage states. The analysis is limited to study the effect of seismic exposure on the building LCC. The proposed method is illustrated with two case studies, a six-storey reinforced concrete (RC) building located in Vancouver, Canada, and vulnerability of an urban centre with 1000 RC buildings. Furthermore, sensitivity analysis is carried out to highlight the impact of different building performance modifiers on the LCC.

Approximate Estimation of the Product Life Cycle Cost Using Artificial Neural Networks in Conceptual Design

In order to improve the design of products and reduce design changes, cost, and time to market, life cycle engineering has emerged as an effective approach to address these issues in today's competitive global market. As over 70% of the total life cycle cost of a product is committed at the early design stage, designers can substantially reduce the life cycle cost of products by giving due consideration to the life cycle implications of their design decisions. During the early design stages there may be competing requirements. In addition, detailed information is scarce and decisions must be made quickly. Thus, both the overhead in developing parametric life cycle cost (LCC) models for a wide range of concepts or requirements, and the lack of detailed information make the application of traditional LCC models impractical. A different approach is required because a traditional LCC method should be incorporated in the very early design stages. This paper explores an approximate method for providing the preliminary life cycle cost. Learning algorithms trained to use the known characteristics of existing products can perhaps allow the life cycle cost of new products to be approximated quickly during the conceptual design phase without the overhead of defining new LCC models. Artificial neural networks are trained to generalise product attributes and life cycle cost data from pre-existing LCC studies. Then, the product designers query the trained artificial model with new high-level product attribute data to obtain an LCC for a new product concept quickly. Foundations for the learning LCC approach are established, and then an application is provided. This paper has been developed to provide designers with LCC information to guide them in conceptual design.

Prediction of the life cycle cost using statistical and artificial neural network methods in conceptual product design

During the early design stages, over 70% of the total life cycle cost (LCC) of a product is committed and there may be competing concepts with dramatic differences. Additionally, both the lack of detailed information, and the overhead in developing parametric LCC models for a range of concepts make the application of traditional LCC models impractical. This paper describes the development of predictive models for the product LCC during conceptual design. An artificial neural network (ANN) model to predict the product LCC is developed and compared with a conventional statistical model -- a regression model. The results show that the ANN model outperforms the traditional regression model used for predicting the product LCC.