APPLICATION OF COST SIGNIFICANT ITEMS TOWARDS IMPROVING ESTIMATION FOR LIFE CYCLE COSTING OF BUILDING PROJECTS

2015 ◽  
Vol 2 (1) ◽  
Author(s):  
Ayedh Alqahtani ◽  
Andrew Whyte
Keyword(s):  
Life Cycle ◽  
Life Cycle Cost ◽  
Construction Projects ◽  
Significant Cost ◽  
Building Projects ◽  
Factors Affecting ◽  
Complicated Process ◽  
Collection Data ◽  
Management Effort ◽  
Cost Factors

Construction projects have numerous variable factors affecting the value of life cycle-cost (LCC) and there is interaction between these factors, leading to complicated process. Therefore the current LCC models suffer from the absence of a standardized and a simple methodology of both the collection data and estimation LCC. Identification of the significant cost factors affecting the output of LCC is important step before embarking upon the collection of progress information. Furthermore, components of construction differ in their cost and time-importance, and thus management effort must be equivalently distributed. The concept of cost significant items (CSIs) is the best method to simplify estimation methodology as well as the collection data of construction projects. In general, The CSIs concept aims to determine the small number of items which represent a constant percentage of the LCC of construction projects. Therefore, this paper aims to present the explanation of each stage of LCC, the current classifications of asset components, the CSIs concept and previous practice of the significant cost items on the construction sector.

scholarly journals Evaluation of non-cost factors affecting the life cycle cost: an exploratory study

2016 ◽  
Vol 14 (4) ◽  
pp. 818-834 ◽  
Author(s):  
Ayedh Alqahtani ◽  
Andrew Whyte
Keyword(s):  
Life Cycle ◽  
Survey Data ◽  
Cost Estimation ◽  
Life Cycle Cost ◽  
Accurate Estimation ◽  
Capital Operation ◽  
Building Projects ◽  
Content Type ◽  
Factors Affecting ◽  
Cost Factors

Purpose This paper aims to identify the main non-cost factors affecting accurate estimation of life cycle cost (LCC) in building projects. Design/methodology/approach Ten factors affecting LCC in building project cost estimates are identified through literature and interviews. A questionnaire survey is conducted to rank these factors in order of priority and provide the views of cost practitioners about the significance of these factors in the accurate estimation of LCC. The data from 138 construction building projects completed in UK were collected and analysed via multiple regression to discover the relationship between capital and LCCs and between non-cost factors and cost estimation at each stage of the life cycle (capital, operation, maintenance and LCC). Findings The results of analysis of existing LCC data of completing project and survey data from cost professionals are mostly consistent with many literature views and provide a reasonable description of the non-cost factors affecting the accuracy of estimates. Originality/value The value of this study is in the method used, which involves analysis of existing life data and survey data from cost professionals. The results provide a plausible description of the non-cost factors affecting the accuracy of estimates.

IDENTIFICATION OF THE KEY FACTORS FOR ACCURATE LIFE-CYCLE COST ESTIMATION FOR CONSTRUCTION

2014 ◽  
Vol 1 (1) ◽  
Author(s):  
Ayedh Alqahtani ◽  
Andrew Whyte
Keyword(s):  
Life Cycle ◽  
Cost Estimation ◽  
Life Cycle Cost ◽  
Prediction Models ◽  
Key Factors ◽  
Plant Resources ◽  
Factors Affecting ◽  
Low Visibility ◽  
The Cost ◽  
Cost Factors

A major limitation of Life-Cycle Cost (LCC) estimation/prediction modelling is the current typical reliance only on those factors that can be readily quantified and come easily to hand. While estimation of the cost of the most common labor, material and plant resources receive consideration because of their high visibility factor, there are several non-cost factors (low visibility factors) affecting the estimate that are often overlooked and, it is argued here, require equal consideration in estimation processes that seek optimum accuracy. Unfortunately, such (low-visibility) factors are neglected or ignored by current prediction models. Identification of these non-cost factors (low visibility factors) affects LCC estimate accuracy and can improve estimation process confidence. This paper critically reviews secondary research on identification of these important non-cost factors and subsequently determines their influence on the accuracy level(s) of construction cost estimation.

scholarly journals Risk Assessment of Ex-Post Transaction Cost in Construction Projects Using Structural Equation Modeling

2018 ◽  
Vol 10 (11) ◽  
pp. 4017 ◽  
Author(s):  
Zaigham Ali ◽  
Fangwei Zhu ◽  
Shahid Hussain
Keyword(s):  
Structural Equation Modeling ◽  
Life Cycle ◽  
Transaction Cost ◽  
Life Cycle Cost ◽  
Structural Equation ◽  
Construction Projects ◽  
Structural Model ◽  
Equation Modeling ◽  
Ex Post ◽  
Industry Professionals

The transaction cost (TC) escalation is the pervasive problem in the construction industry, which is continuously a threat to maintaining the life cycle cost of projects. Researchers have described the reality of risk for economic transactions. This study has taken the risk as a phenomenon to explore its influence on ex-post TC in construction projects. A questionnaire survey was undertaken from industry professionals to assess the risk of ex-post TC escalation in public-sector construction projects. In total, 475 surveys were conducted in Pakistan and used in the analysis. The data were analyzed using structural equation modeling (SEM) and the measurement and structural model was validated to determine the influence of risk on ex-post TC. The final SEM results show that internal and external risk, including sub hypothesized risks, positively influence TC. The weight of relative importance shows technical risk (23.82%) and environmental risk (22.88%) as significant sub-contributors from internal and external sources, respectively. This study recommends substantial investment in human capacity development to reduce the deficiencies in the ex-ante phase of the projects that help to reduce the risk of ex-post TC escalation. It also suggests the adoption of strict policies on contingency claims, and recommends nontraditional ways of monitoring to overcome the risk of ex-post TC. This study’s results provide valuable information for industry professionals and practitioners to maintain life cycle costs as a contribution to sustainable construction.

scholarly journals Development of a Model for Predicting Probabilistic Life-Cycle Cost for the Early Stage of Public-Office Construction

2019 ◽  
Vol 11 (14) ◽  
pp. 3828 ◽  
Author(s):  
Jin ◽  
Kim ◽  
Hyun ◽  
Han
Keyword(s):  
Life Cycle ◽  
Prediction Model ◽  
Life Cycle Cost ◽  
Construction Projects ◽  
Prediction Models ◽  
Early Stage ◽  
Case Based Reasoning ◽  
Probabilistic Prediction ◽  
Construction Costs ◽  
Early Stages

Decisions made in the early stages of construction projects significantly influence the costs incurred in subsequent stages. Therefore, such decisions must be based on the life-cycle cost (LCC), which includes the maintenance, repair, and replacement (MRR) costs in addition to construction costs. Furthermore, as uncertainty is inherent during the early stages, it must be considered in making predictions of the LCC more probabilistic. This study proposes a probabilistic LCC prediction model developed by applying the Monte Carlo simulation (MCS) to an LCC prediction model based on case-based reasoning (CBR) to support the decision-making process in the early stages of construction projects. The model was developed in two phases: first, two LCC prediction models were constructed using CBR and multiple-regression analysis. Through k-fold validation, one model with superior prediction performance was selected; second, a probabilistic LCC model was developed by applying the MCS to the selected model. The probabilistic LCC prediction model proposed in this study can generate probabilistic prediction results that consider the uncertainty of information available at the early stages of a project. Thus, it can enhance reliability in actual situations and be more useful for clients who support both construction and MRR costs, such as those in the public sector.

scholarly journals Effects of Design Quality on Delay in Residential Construction Projects

2021 ◽  
Vol 28 (1) ◽  
pp. 118-129
Author(s):  
Ibrahim Mahamid
Keyword(s):  
Construction Projects ◽  
Design Quality ◽  
Frequent Change ◽  
Building Projects ◽  
Factors Affecting ◽  
Schedule Delay ◽  
Major Factors ◽  
Design Schedule ◽  
The Impact ◽  
Project Delay

This study is conducted to establish the effect of design quality on project delay in building projects. It aims at: 1) investigating the major factors of design quality, 2) identifying the main delay factors in building projects, 2) establishing the relationship between design quality and delay in building projects. To achieve these objectives, a questionnaire survey is performed. Seventeen (17) factors that might affect design quality, and 15 delay factors are listed in a questionnaire form. Sixty (60) contractors and 40 consultants are asked to identify the severity of the identified factors. Results indicate that the top factors affecting design quality are: delay in payments by client for design services, staff allocation for many projects at the same time, copying and modifying from previous work to minimize time and cost, tight design schedule, lack of designer knowledge with techniques and materials available in the market. The study also concludes that the top five delay factors include: payments delay, poor labor productivity, lack of skilled manpower, frequent change orders and rework. Regression analysis for data collected from 36 building projects shows a good correlation between design quality and delay in projects. This study is the first one that addresses the problem of design quality in the West Bank in Palestine. Furthermore, it is the first study that addresses the effect of design quality on project delay in Palestine and the neighboring countries. It is hoped to be helpful for researchers and professionals to understand the impact of design quality on schedule delay.

scholarly journals Significant Factors Affecting the Life Cycle Cost Elements of a Building

FARU Journal ◽  
2021 ◽  
Vol 8 (1) ◽  
pp. 57
Author(s):  
J. Samaranayake ◽  
T. Ramachandra ◽  
U. G. D. Madushika
Keyword(s):  
Life Cycle ◽  
Life Cycle Cost ◽  
Factors Affecting ◽  
Significant Factors

scholarly journals Artificial neural networks incorporating cost significant Items towards enhancing estimation for (life-cycle) costing of construction projects

2013 ◽  
Vol 13 (3) ◽  
pp. 51-64 ◽  
Author(s):  
Ayedh Alqahtani ◽  
Andrew Whyte
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Life Cycle ◽  
Life Cycle Cost ◽  
Construction Projects ◽  
Back Propagation ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Excel Solver

Industrial application of life-cycle cost analysis (LCCA) is somewhat limited, with techniques deemed overly theoretical, resulting in a reluctance to realise (and pass onto the client) the advantages to be gained from objective (LCCA) comparison of (sub)component material specifications. To address the need for a user-friendly structured approach to facilitate complex processing, the work described here develops a new, accessible framework for LCCA of construction projects; it acknowledges Artificial Neural Networks (ANNs) to compute the whole-cost(s) of construction and uses the concept of cost significant items (CSI) to identify the main cost factors affecting the accuracy of estimation. ANNs is a powerful means to handle non-linear problems and subsequently map between complex input/output data, address uncertainties. A case study documenting 20 building projects was used to test the framework and estimate total running costs accurately. Two methods were used to develop a neural network model; firstly a back-propagation method was adopted (using MATLAB SOFTWARE); and secondly, spread-sheet optimisation was conducted (using Microsoft Excel Solver). The best network was established as consisting of 19 hidden nodes, with the tangent sigmoid used as a transfer function of NNs model for both methods. The results find that in both neural network models, the accuracy of the developed NNs model is 1% (via Excel-solver) and 2% (via back-propagation) respectively.

Investigation into the Current Practice of Cost Estimating and the Introduction of a Standard Price Book in the Malaysia Construction Industry

2013 ◽  
Vol 838-841 ◽  
pp. 3109-3114
Author(s):  
Byung Gyoo Kang ◽  
Vin Shern Eng ◽  
Boon Hoe Goh ◽  
Wee Kang Choong ◽  
Tuck Wai Yeong
Keyword(s):  
Construction Industry ◽  
Construction Projects ◽  
Current Practice ◽  
Construction Project ◽  
Competitive Advantages ◽  
Cost Estimating ◽  
Primary Methods ◽  
Factors Affecting ◽  
Complicated Process ◽  
The Government

Cost estimating is one of the most important areas in construction project management.Three are various ways to do cost estimating with different efficiencies and possibly with differentaccuracies. In addition it is a complicated process including various activities. Standardized pricebooks will help improve the process and accuracy of cost estimating. However there is no publishedprice book in the Malaysia construction industry. This research has investigated and identified thecurrent practice of estimating in the Malaysia construction industry together with the possibilities ofintroducing a price book to the industry. Primary methods of estimating, efficiency of the currentmethods, significance of activities related to estimating, factors affecting the accuracy of estimatingare investigated through a survey. Further the obstacles in introducing a price book have beenidentified. The survey participants agree that the government should initiate the introduction of a pricebook in the Malaysia construction industry. The outcomes of this research can be also used to improvethe competitive advantages of quantity surveyors, consulting engineers/architects and contractors inrespect to cost estimating for construction projects in Malaysia.

scholarly journals Assessing and Prioritising Delay Factors of Prefabricated Concrete Building Projects in China

2018 ◽  
Vol 8 (11) ◽  
pp. 2324 ◽  
Author(s):  
Yingbo Ji ◽  
Lin Qi ◽  
Yan Liu ◽  
Xinnan Liu ◽  
Hong Li ◽  
...  
Keyword(s):  
Decision Making ◽  
Construction Industry ◽  
Construction Projects ◽  
Analytic Network Process ◽  
Building Projects ◽  
Factors Affecting ◽  
Expert Interview ◽  
Concrete Building ◽  
Network Process ◽  
Improved Performance

Prefabricated construction has been widely accepted as an alternative to conventional cast-in-situ construction, given its improved performance. However, prefabricated concrete building projects frequently encounter significant delays. It is, therefore, crucial to identify key factors affecting schedule and explore strategies to minimise the schedule delays for prefabricated concrete building projects. This paper adopts the decision-making trial and evaluation laboratory (DEMATEL) model and analytic network process (ANP) method to quantify the cause-and-effect relationships and prioritise the key delay factors in terms of their importance in the Chinese construction industry. The DEMATEL model evaluates the extent to which each factor impacts other factors. The quantified extents are then converted into a prioritisation matrix through ANP. The delay factors of prefabricated construction projects are selected and categorised based on a literature review and an expert interview. Questionnaires are then implemented to collect the data. The results reveal that the issue of inefficient structural connections for prefabricated components is found to be the most significant factor and most easily affected by other delay factors. This research also suggests prioritising major delay factors, such as ‘lack of communication among participants’ and ‘low productivity’, in the Chinese construction industry during scheduling control. Overall, this research contributes an assessment framework for decision making in the scheduling management of prefabricated construction.

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