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 Life Cycle Cost Analysis of a Single-Family House in Sweden

Buildings ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 215
Author(s):  
Bojana Petrović ◽  
Xingxing Zhang ◽  
Ola Eriksson ◽  
Marita Wallhagen
Keyword(s):  
Life Cycle ◽  
Life Cycle Cost ◽  
Single Family ◽  
Labor Costs ◽  
Construction Costs ◽  
Economic Parameters ◽  
A Minor ◽  
Family House ◽  
Replacement Operation

The objective of this paper was to explore long-term costs for a single-family house in Sweden during its entire lifetime. In order to estimate the total costs, considering construction, replacement, operation, and end-of-life costs over the long term, the life cycle cost (LCC) method was applied. Different cost solutions were analysed including various economic parameters in a sensitivity analysis. Economic parameters used in the analysis include various nominal discount rates (7%, 5%, and 3%), an inflation rate of 2%, and energy escalation rates (2–6%). The study includes two lifespans (100 and 50 years). The discounting scheme was used in the calculations. Additionally, carbon-dioxide equivalent (CO2e) emissions were considered and systematically analysed with costs. Findings show that when the discount rate is decreased from 7% to 3%, the total costs are increased significantly, by 44% for a 100-year lifespan, while for a 50 years lifespan the total costs show a minor increase by 18%. The construction costs represent a major part of total LCC, with labor costs making up half of them. Considering costs and emissions together, a full correlation was not found, while a partial relationship was investigated. Results can be useful for decision-makers in the building sector.

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 Decision to paving solutions in road infrastructures based on life-cycle assessment

2016 ◽  
Vol 11 (1) ◽  
pp. 43-52 ◽  
Author(s):  
Maria de Lurdes Antunes ◽  
Vânia Marecos ◽  
José Neves ◽  
João Morgado
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Decision Process ◽  
Life Cycle Cost ◽  
Road Network ◽  
Construction Costs ◽  
Road Pavement ◽  
Pavement Structures ◽  
Using Data ◽  
Term Performance

The construction and maintenance of a road network involve the expenditure of large budgets. In order to optimize the investments in road infrastructures, designers and decision makers should have the instruments to make the most suitable decision of paving solutions for each particular situation. The life-cycle assessment is an important tool of different road pavement solutions with this purpose. This paper presents a study concerning the life-cycle cost analysis of different flexible and semi-rigid paving alternatives, with the objective to contribute for a better support in the decision process when designing new pavement structures. The analysis was carried out using data on construction costs of certain typical pavement structures and taking into consideration appropriate performance models for each type of structure being selected. The models were calibrated using results from long term performance studies across Europe and the maintenance strategies considered have taken into account the current practice also found in the European context. Besides the life-cycle administration costs, the proposed methodology also deals with user and environmental costs through its inclusion in the decision process using multi-criteria analysis. It was demonstrated that this methodology could be a simple and useful tool in order to achieve the most adequate paving solutions of a road network, in terms of construction and maintenance activities, based simultaneously on technical, economic and environmental criteria.

scholarly journals Determination of Significant Features for Building an Efficient Heart Disease Prediction System

2019 ◽  
Vol 8 (2) ◽  
pp. 4499-4504
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Prediction Model ◽  
Prediction Models ◽  
Heart Diseases ◽  
Medical Diagnostics ◽  
Medical Data ◽  
Machine Learning Algorithms ◽  
Prediction System ◽  
Early Stages

Heart diseases are responsible for the greatest number of deaths all over the world. These diseases are usually not detected in early stages as the cost of medical diagnostics is not affordable by a majority of the people. Research has shown that machine learning methods have a great capability to extract valuable information from the medical data. This information is used to build the prediction models which provide cost effective technological aid for a medical practitioner to detect the heart disease in early stages. However, the presence of some irrelevant and redundant features in medical data deteriorates the competence of the prediction system. This research was aimed to improve the accuracy of the existing methods by removing such features. In this study, brute force-based algorithm of feature selection was used to determine relevant significant features. After experimenting rigorously with 7528 possible combinations of features and 5 machine learning algorithms, 8 important features were identified. A prediction model was developed using these significant features. Accuracy of this model is experimentally calculated to be 86.4%which is higher than the results of existing studies. The prediction model proposed in this study shall help in predicting heart disease efficiently.

Comparison of Building Construction and Life-Cycle Cost for a High-Rise Mass Timber Building with its Concrete Alternative

2020 ◽  
Vol 70 (4) ◽  
pp. 482-492
Author(s):  
Hongmei Gu ◽  
Shaobo Liang ◽  
Richard Bergman
Keyword(s):  
Life Cycle ◽  
Life Cycle Cost ◽  
Building Materials ◽  
Building Design ◽  
Cost Data ◽  
Construction Costs ◽  
High Rise ◽  
Front End ◽  
Concrete Building ◽  
Mass Timber

Abstract Mass timber building materials such as cross-laminated timber (CLT) have captured attention in mid- to high-rise building designs because of their potential environmental benefits. The recently updated multistory building code also enables greater utilization of these wood building materials. The cost-effectiveness of mass timber buildings is also undergoing substantial analysis. Given the relatively new presence of CLT in United States, high front-end construction costs are expected. This study presents the life-cycle cost (LCC) for a 12-story, 8,360-m2 mass timber building to be built in Portland, Oregon. The goal was to assess its total life-cycle cost (TLCC) relative to a functionally equivalent reinforced-concrete building design using our in-house-developed LCC tool. Based on commercial construction cost data from the RSMeans database, a mass timber building design is estimated to have 26 percent higher front-end costs than its concrete alternative. Front-end construction costs dominated the TLCC for both buildings. However, a decrease of 2.4 percent TLCC relative to concrete building was observed because of the estimated longer lifespan and higher end-of-life salvage value for the mass timber building. The end-of-life savings from demolition cost or salvage values in mass timber building could offset some initial construction costs. There are minimal historical construction cost data and lack of operational cost data for mass timber buildings; therefore, more studies and data are needed to make the generalization of these results. However, a solid methodology for mass timber building LCC was developed and applied to demonstrate several cost scenarios for mass timber building benefits or disadvantages.

scholarly journals Alzheimer’s Disease Prediction Model Using Demographics and Categorical Data

2019 ◽  
Vol 15 (15) ◽  
pp. 96
Author(s):  
Aunsia Khan ◽  
Muhammad Usman
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Prediction Model ◽  
Clinical Diagnosis ◽  
Prediction Models ◽  
Early Stage ◽  
Class Imbalance ◽  
Detection Accuracy ◽  
Features Selection ◽  
Neuropsychological Battery

<p class="abstract">Diagnosing Alzheimer’s disease (AD) is usually difficult, especially when the disease is in its early stage. However, treatment is most likely to be effective at this stage; improving the diagnosis process. Several AD prediction models have been proposed in the past; however, these models endure a number of limitations such as small dataset, class imbalance, feature selection methods etc which place strong barriers towards the accurate prediction. In this paper, an AD prediction model has been proposed and validated using categorical dataset from National Alzheimer’s Coordination Center (NACC). The different categories such as Demographics, Clinical Diagnosis, MMSE &amp; Neuropsychological battery, is preprocessed for important features selection and class imbalance. A number of predominant classifiers namely, Naïve Bayes, J48, Decision Stump, LogitBoost, AdaBoost, and SDG-Text have been used to highlight the superiority of a classifier in predicting the potential AD patients. Experimental results revealed that Bayesian based classifiers improve AD detection accuracy up to 96.4% while using Clinical Diagnosis category.</p>

scholarly journals HYBRID FORECASTING SYSTEM BASED ON CASE-BASED REASONING AND ANALYTIC HIERARCHY PROCESS FOR COST ESTIMATION

2013 ◽  
Vol 19 (1) ◽  
pp. 86-96 ◽  
Author(s):  
Sangyong Kim
Keyword(s):  
Analytic Hierarchy Process ◽  
Cost Estimation ◽  
Prediction Models ◽  
Early Stage ◽  
Case Based Reasoning ◽  
Cost Estimating ◽  
Analytic Hierarchy ◽  
Highway Projects ◽  
Hierarchy Process ◽  
Case Based

Cost estimating of highway projects with high accuracy at the early stage of project development is crucial for planning and feasibility studies. Various research have been attempted to develop cost prediction models in the early stage of a construction life cycle. This study uses the hybrid estimating tool to provide an effective cost data management for highway projects and accordingly develops a realistic cost estimating system. This study focused on the development of a more accurate estimate technique for highway projects in South Korea at the early stage using hybrid analytic hierarchy process (AHP) and case-based reasoning (CBR). Real case studies are used to demonstrate and validate the benefits of the proposed approach. It is expected that the developed CBR system is to provide decision-makers with accurate cost information to asses and compare multiple alternatives for obtaining the optimal solution and controlling cost.

Early Stages in the Institutionalization of Integrated Project Delivery

2019 ◽  
Vol 50 (2) ◽  
pp. 128-143 ◽  
Author(s):  
Daniel M. Hall ◽  
W. Richard Scott
Keyword(s):  
Construction Projects ◽  
Early Stage ◽  
Project Delivery ◽  
Integrated Project Delivery ◽  
Project Organization ◽  
Multistage Model ◽  
Early Stages ◽  
Infrastructure Project ◽  
Integrated Project ◽  
Institutional Entrepreneur

Integrated project delivery (IPD), an emerging form of project organization for North American construction projects, offers a compelling case study to understand how new innovative infrastructure project delivery models can emerge and institutionalize. This article frames the early stages of IPD through the actions of an institutional entrepreneur—Sutter Health—working to construct a new arrangement for the delivery of its large healthcare projects. The resulting account uses Suchman’s (1995) multistage model of institutionalization to understand the early-stage actors, processes, conditions, and actions present for creation of an innovative delivery model within a fragmented, project-based industry context.

Creating an Information Environment for Construction Projects

2021 ◽  
Vol 27 (12) ◽  
pp. 992-1002
Author(s):  
I. V. Karakozova ◽  
Yu. S. Prokhorova
Keyword(s):  
Life Cycle ◽  
Construction Projects ◽  
Methodological Approach ◽  
Integrated Design ◽  
Information Environment ◽  
Information Modeling ◽  
Construction Costs ◽  
Management Algorithm ◽  
Analysis And Synthesis ◽  
Capital Construction

Aim. The presented study aims to create a unified information environment for all participants of construction project implementation and to monitor key indicators of construction during its implementation.Tasks. The authors develop a methodological approach to substantiate a unified information environment for the implementation of capital construction projects; develop modules of the information environment that reflect the processes of designing a facility and managing costs at all stages of its life cycle. Methods. This study uses the methods of analysis and synthesis, graphical modeling, expert and comparative assessments, methods of pricing in construction.Results. An information environment for the implementation of capital construction projects is developed. Its major modules reflect the processes of designing and constructing a facility and managing costs at all stages of its life cycle.Conclusions. Introduction of the proposed information environment for managing construction costs at the enterprise level with an integrated design module based on information modeling technologies will improve the efficiency of interaction between the participants of the investment and construction process and will provide a unified cost management algorithm in the context of the active digitalization of the industry.

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.

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