Revisiting storey enclosure method for early estimation of structural building construction cost

2018 ◽  
Vol 25 (7) ◽  
pp. 877-895 ◽  
Author(s):  
Chau Ngoc Dang ◽  
Long Le-Hoai
Keyword(s):  
Regression Analysis ◽  
Residential Building ◽  
Construction Cost ◽  
Design Parameters ◽  
Cumulative Probability ◽  
Range Estimation ◽  
Building Projects ◽  
Content Type ◽  
Enclosure Method ◽  
The Cost

Purpose The purpose of this paper is to develop several predictive models for estimating the structural construction cost and establish range estimation for the structural construction cost using design information available in early stages of residential building projects. Design/methodology/approach Information about residential building projects is collected based on project documents from construction companies with regard to the design parameters and the actual structural construction costs at completion. Storey enclosure method (SEM) is fundamental for determining the building design parameters, forming the potential variables and developing the cost estimation models using regression analysis. Nonparametric bootstrap method is used to establish range estimation for the structural construction cost. Findings A model which is developed from an integration of advanced SEM, principle component analysis and regression analysis is robust in terms of predictability. In terms of range estimation, cumulative probability-based range estimates and confidence intervals are established. While cumulative probability-based range estimates provide information about the level of uncertainty included in the estimate, confidence intervals provide information about the variability of the estimate. Such information could be very crucial for management decisions in early stages of residential building projects. Originality/value This study could provide practitioners with a better understanding of the uncertainty and variability included in the cost estimate. Hence, they could make effective improvements on cost-related management approaches to enhance project cost performance.

Construction time prediction model for public building projects

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shambel Kifle Alemu
Keyword(s):  
Regression Analysis ◽  
Addis Ababa ◽  
Linear Regression Analysis ◽  
Research Work ◽  
Time Cost ◽  
Construction Time ◽  
Time Model ◽  
Building Projects ◽  
Public Building ◽  
Content Type

PurposeThe aim of the study was to develop a practical construction time model for public building projects in Addis Ababa, Ethiopia.Design/methodology/approachThis research work used regression analysis and also exploratory scatter and residual plot techniques. Simple and multiple regressions were used for the investigation of the best fit time model. The analyses were carried out using IBM SPSS statistical software, version 20.FindingsThe result revealed that the Bromilow time-cost principle was moderately applicable. However, the cubic regression model (CUB) was found a better time-cost relationship. On the contrary, the study has shown a poor relationship between actual time and gross floor area. Furthermore, multiple linear regression analysis (MLR) consists of three statistically significant variables were found a better fit time model.Research limitations/implicationsThe study is limited to only six project scope factors. Further research is recommended to include more building projects of similar type and implications of other factors to improve the reliability of the models.Practical implicationsThe developed model was not intended as a replacement for detailed construction scheduling techniques. The resulting model is applicable for front-end predictions of construction duration.Originality/valueThe main parties involved in the building projects should apply the model for benchmarking a precise construction time during the early planning phase.

Investigating cost efficiency in the branch network of a Greek bank: an empirical study

2008 ◽  
Vol 34 (3) ◽  
pp. 160-171 ◽  
Author(s):  
Athanasios G. Noulas ◽  
Niki Glaveli ◽  
Ioannis Kiriakopoulos
Keyword(s):  
Regression Analysis ◽  
Commercial Bank ◽  
Cost Efficiency ◽  
Comparative Evaluation ◽  
Design Methodology ◽  
Future Research ◽  
Data Envelopment ◽  
Content Type ◽  
Dea Method ◽  
The Cost

PurposeThe purpose of this study is to examine the cost efficiency of 58 branches of a major Greek commercial bank, in six major Greek cities, for the years 2000 and 2001.Design/methodology/approachThe efficiency is measured through the data envelopment analysis (DEA) method. Using regression analysis, the effect of size on cost efficiency is also examined.FindingsThe results indicate that there is a room for substantial efficiency improvements. The average inefficiency is about 30 per cent. It has also been observed that rural branches tend, on average, to be more efficient than urban branches.Research limitations/implicationsA direction of future research would be to extend the analysis of determinants of bank branch efficiency in order to investigate the role that the region and the characteristics of the branch play in relation to efficiency.Originality/valueThe paper provides a comparative evaluation of the efficiency of 58 branches of a major Greek commercial bank using the DEA method.

Statistically constrained economic design of X ¯ control chart with Weibull in-control time for processes subject to multiple assignable causes

2019 ◽  
Vol 36 (4) ◽  
pp. 526-551 ◽  
Author(s):  
Mohammad Hosein Nadreri ◽  
Mohamad Bameni Moghadam ◽  
Asghar Seif
Keyword(s):  
Control Chart ◽  
Cost Model ◽  
Statistical Design ◽  
The Body ◽  
Design Parameters ◽  
Content Type ◽  
Sampling Schemes ◽  
Uniform Sampling ◽  
Non Uniform Sampling ◽  
The Cost

PurposeThe purpose of this paper is to develop an economic statistical design based on the concepts of adjusted average time to signal (AATS) andANFforX¯control chart under a Weibull shock model with multiple assignable causes.Design/methodology/approachThe design used in this study is based on a multiple assignable causes cost model. The new proposed cost model is compared with the same cost and time parameters and optimal design parameters under uniform and non-uniform sampling schemes.FindingsNumerical results indicate that the cost model with non-uniform sampling cost has a lower cost than that with uniform sampling. By using sensitivity analysis, the effect of changing fixed and variable parameters of time, cost and Weibull distribution parameters on the optimum values of design parameters and loss cost is examined and discussed.Practical implicationsThis research adds to the body of knowledge relating to the quality control of process monitoring systems. This paper may be of particular interest to practitioners of quality systems in factories where multiple assignable causes affect the production process.Originality/valueThe cost functions for uniform and non-uniform sampling schemes are presented based on multiple assignable causes withAATSandANFconcepts for the first time.

Predicting implementation cost contingencies for residential construction projects in flood-prone areas

2019 ◽  
Vol 12 (4) ◽  
pp. 1097-1119 ◽  
Author(s):  
Sang Quang Van ◽  
Long Le-Hoai ◽  
Chau Ngoc Dang
Keyword(s):  
Regression Analysis ◽  
Construction Projects ◽  
Bootstrap Method ◽  
Parametric Bootstrap ◽  
Component Analysis ◽  
Residential Construction ◽  
Implementation Cost ◽  
Range Estimation ◽  
Content Type ◽  
Flood Prone Areas

Purpose The purpose of this paper is to predict implementation cost contingencies for residential construction projects in flood-prone areas, where floods with storms frequently cause serious damage and problems for people. Design/methodology/approach Expert interviews are conducted to identify the study variables. Based on bills of quantities and project documents, historical data on residential construction projects in flood-prone areas are collected. Pearson correlation analysis is first used to check the correlations among the study variables. To overcome multicollinearity, principal component analysis is used. Then, stepwise multiple regression analysis is used to develop the cost prediction model. Finally, non-parametric bootstrap method is used to develop range estimation of the implementation cost. Findings A list of project-related variables, which could significantly affect implementation costs of residential construction projects in flood-prone areas, is identified. A model, which is developed based on an integration of principle component analysis and regression analysis, is robust. Regarding range estimation, 10, 50 and 90 percent cost estimates, which could provide information about the uncertainty levels in the estimates, are established. Furthermore, implementation cost contingencies which could show information about the variability in the estimates are determined for example case projects. Such information could be critical to cost-related management of residential construction projects in flood-prone areas. Originality/value This study attempts to predict implementation cost contingencies for residential construction projects in flood-prone areas using non-parametric bootstrap method. Such contingencies could be useful for project cost budgeting and/or effective cost management.

A generative design approach for modular construction in congested urban areas

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yuxi Wei ◽  
Hyungjoo Choi ◽  
Zhen Lei
Keyword(s):  
Urban Areas ◽  
Design Method ◽  
Residential Building ◽  
Information Modeling ◽  
Design Approach ◽  
Design Parameters ◽  
Building Structures ◽  
Modular Construction ◽  
Generative Design ◽  
Content Type

PurposeModular construction is widely adopted and used in the construction industry to improve construction performance with respect to both efficiency and productivity. The evaluation of design options for modular construction can be iterative, and thus automation is required to develop design alternatives. This research aims to explore the potential of utilizing the generative design approach to automate modular construction for residential building structures in urban areas such as New York City.Design/methodology/approachThe proposed research methodology is investigated for a systematic approach to parametrize design parameters for modular construction layout design as well as incorporate design rules/parameters into modularizing design layouts in a Building Information Modeling (BIM) environment. Based on current building codes and necessary inputs by the user, the proposed approach enables providing recommendations in a generative design method and optimizes construction processes by performing analytical calculations.FindingsThe generative design has been found to be efficient in generating layout designs for modular construction based on parametric design. The integration of BIM and generative design can allow industry practitioners to fast generate design layout with evaluations from constructability perspectives.Originality/valueThis paper has proposed a new approach of incorporating generative design with BIM technologies to solve module layout generations by considering design and constructability constraints. The method can be further extended for evaluating modular construction design from manufacturability and assembly perspectives.

Investigating the causes preventing the fulfillment of construction contract requirements

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Fadi A. Fatayer ◽  
Amjad Z. Issa ◽  
Mohammed Abunemeh ◽  
Mohammed A.M. Dwikat
Keyword(s):  
Construction Projects ◽  
Subject Matter Experts ◽  
Structured Interviews ◽  
Building Projects ◽  
Content Type ◽  
Road Projects ◽  
The Subject ◽  
The Mean ◽  
Spss Software ◽  
The Cost

PurposeConstruction contractors in Palestine, as in many other developing countries, suffer from many problems. One of their main problems is their inability to meet contractual requirements, such as completing projects within time, at the agreed cost and to the desired quality. Therefore, this paper aims to investigate the causes of the non-fulfillment of contractual requirements in three different types of projects: building, road and electro-mechanical projects.Design/methodology/approachTwo methods were adopted to collect the data – qualitative and quantitative. In total, 65 causes were identified from the literature and qualitative semi-structured interviews with professional experts. These causes were classified into five categories: managerial, financial, contractor capabilities, regulations and laws and political. In the quantitative approach, a questionnaire was developed and then distributed to 50 professional experts: 20 building experts, 15 road experts and 15 experts in electro-mechanical projects. A five-point Likert scale was used to assess the importance, from the perspective of the subject matter experts, of the causes that had been identified. About 35 responses, which represent a combined response rate of 70%, were received. The quantitative data were analyzed using descriptive statistics, with the mean, standard deviation and degree of importance for each of the identified causes being determined, and the SPSS software platform used to rank them.FindingsThe results reveal that the most important reasons why contractors did not fulfill their contractual requirements in building projects were that contracts were awarded to the contractor offering the lowest price, and the profit margin was low because of intense competition among contractors. In road projects, the most important causes were the poor estimation of the equipment required and a lack of standardized conditions in the construction sector, while in electro-mechanical projects, the most important causes were the inability of the contractor to estimate the cost of the project accurately because of unclear bid documents and a lack of contractor capital.Originality/valueThe results of this study will be useful to stakeholders and Palestinian contractor unions. They can be disseminated to give guidance so that contractors can avoid these problems in future construction projects and enhance their compliance with contractual requirements. Moreover, knowing about these causes may lead to the reduction of conflicts and disputes between contractual parties (owners and contractors), which in turn will be reflected in the work quality and reputation of contractors.

Improving accuracy of conceptual cost estimation using MRA and ANFIS in Indonesian building projects

2018 ◽  
Vol 8 (4) ◽  
pp. 348-357 ◽  
Author(s):  
Dwifitra Jumas ◽  
Faizul Azli Mohd-Rahim ◽  
Nurshuhada Zainon ◽  
Wayudi P. Utama
Keyword(s):  
Regression Analysis ◽  
Cost Estimation ◽  
Fuzzy Inference ◽  
Early Stage ◽  
State Building ◽  
Building Projects ◽  
Inference System ◽  
Content Type ◽  
Proposed Model ◽  
West Sumatra

Purpose The purpose of this paper is to develop a conceptual cost estimation (CCE) model for building project by using a pragmatic approach, which is a mix of tools drawn from multiple regression analysis (MRA) and adaptive neuro-fuzzy inference system (ANFIS), to improve the accuracy of cost estimation at an early stage. Design/methodology/approach This paper presents a set of MRA and integrating MRA with ANFIS or MRANFIS. A simultaneous regression analysis was developed to determine the main cost factors from 12 variables as input variables in the ANFIS model. Cost data from 78 projects of state building in West Sumatra, Indonesia were used to indicate the advantages of the proposed model. Findings The result shows that the proposed model, MRANFIS, has successfully improved the mean absolute percent error (MAPE) by 2.8 percent from MRA of 10.7–7.9 percent for closeness of fit to the model data and by 3.1 percent from MRA of 9.8–6.7 percent for prediction performance to the new data. Research limitations/implications Because the significant variables are different for each building type, the model may be not appropriate for other buildings depending on the characteristics of building. The models can be used and analyzed based on the own historical project data for each case so that the model can be applied. Originality/value The study thus provides better accuracy of CCE at an early stage for state building projects in West Sumatra, Indonesia by using the integrated model of MRA and ANFIS.

The statistical sensitivity of highway project cost to geo-heterogeneity in the tropics

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alolote Amadi
Keyword(s):  
Regression Analysis ◽  
Road Construction ◽  
Temperate Climate ◽  
Index Properties ◽  
Highway Projects ◽  
Content Type ◽  
Subgrade Soils ◽  
Project Costs ◽  
The Cost ◽  
Highway Project

PurposeThe study is carried out to analytically reconnoiter geotechnical index properties of subgrade soils as key variables that shape the cost profile of road infrastructure projects in a tropical geographic setting with starkly heterogenous ground conditions.Design/methodology/approachUsing the Niger Delta region, as a point of reference, data on geotechnical index properties of subgrade soils at spatially dispersed locations for 61 completed highway projects are collated. Exploratory statistical tests were carried out to infer significant associations with final project costs before regression analysis. Regression analysis is principally deployed as an explanatory analytical tool, relevant to quantify the sensitivity of highway project costs to the individual and collective impact of geotechnical variables.FindingsSeveral parameters of expansivity and compressibility exhibited significantly strong associations with the final costs recorded on the highway projects. The statistical analysis further established a cause-effect relationship, whereby small changes in the geotechnical properties of sub-grade soils at project locations, would result in disproportionately large changes in the cost of road construction.Practical implicationsThe study findings provide insight into the sensitivity of road construction costs to geotechnical variables, which can serve as a useful input in financial risk analysis for development appraisal and the generation of location adjustment factors.Originality/valueThe study statistically demonstrates location-induced construction cost profiles, triggered in response to the spatial geotechnical variability and occurrence of problem subgrade soils in the humid tropics, which may be different from those traditionally established in studies of cold and temperate climate soils.

Prediction of construction projects’ costs based on fusion method

2017 ◽  
Vol 34 (7) ◽  
pp. 2396-2408 ◽  
Author(s):  
Fang Shutian ◽  
Zhao Tianyi ◽  
Zhang Ying
Keyword(s):  
Construction Projects ◽  
Design Methodology ◽  
Construction Cost ◽  
Filter Method ◽  
Support Vector ◽  
Content Type ◽  
Filtering Method ◽  
The Cost ◽  
First Time ◽  
Practical Implications

Purpose This study aims to predict the construction cost in China, the authors purposed a fused method. Design/methodology/approach The authors extracted 22 factors which may influence the cost and performed the correlation analysis with cost. They chose the highest 10 factors to predict cost by the fused method. The method fused the Kalman filter with least squares support vector machine and multiple linear regression. Findings Ten factors which affect the cost most were found. The construction cost in China can be predicted by the presented method precisely. The statistical filter method could be used in the field of construction cost prediction. Research limitations/implications The construction cost and construction interior factors are a business secret in China. So, the authors only collected 24 buildings’ data to perform the experiments. Practical implications There is no standard and precise method to predict construction cost in China, so the presented method offers a new way to judge the feasibility of projects and select design schemes of construction. Originality/value The authors purposed a new fused method to predict construction cost. It is the first time that the statistical filtering method was used in this field. The effectiveness was verified by the experiments. Ten factors which have a high relationship with construction cost were found.

Probability of change orders and the effect on cost and schedule for new public school buildings

2019 ◽  
Vol 26 (6) ◽  
pp. 1087-1104 ◽  
Author(s):  
Pramen P. Shrestha ◽  
Kabindra Kumar Shrestha ◽  
Haileab B. Zeleke
Keyword(s):  
Public School ◽  
Statistical Tests ◽  
School Buildings ◽  
School Building ◽  
Change Orders ◽  
Building Projects ◽  
Content Type ◽  
New School ◽  
New Public ◽  
The Cost

Purpose Change orders (COs) adversely affect the cost and schedule of projects, specifically during the construction phase. COs of 95 new public school building projects contracted by the Clark County School District (CCSD) of Nevada were analyzed to quantify the cost and schedule growth as well as to determine the effect of COs on cost and schedule growth. The paper aims to discuss these issues. Design/methodology/approach The data were collected from CCSD through questionnaire survey. Descriptive statistics and statistical tests were conducted to determine the effect of COs on cost and schedule growth. Findings It was found that the average amount of COs as well as cost and schedule overruns were 5.9, 3.0 and 7.4 percent, respectively. Statistical tests showed that the amount of COs had an adverse effect on schedule growth; schedule overruns in projects with less than 4 percent COs were significantly lower than projects with more than 4 percent COs. Cost overruns did not significantly differ in those two types of projects. The primary contribution of this study is that it provides the tools and the framework for school district engineers to determine the probability of the occurrence of COs as well as the optimum percentage of COs for a minimum effect on cost and schedule growth of new public school buildings. Probability curves were also developed to determine the likelihood of the occurrence of COs, cost growth and schedule growth in these projects. These findings could be used by school districts to avoid or reduce COs in future projects, minimizing the effect on cost and schedule growth during the construction phase. Research limitations/implications The findings and the probabilities curves developed in this study should be used carefully in other cases. These data were specific to the owner, location and types of buildings and generalizing these findings may have negative consequences. Practical implications The practical implications are that this study could provide a tool to school building administrators to determine the probability of having COs as well as cost and schedule overruns and the effects of COs on cost and schedule overruns. To the authors’ best knowledge, no other studies of this type have been conducted previously. Social implications The social implication of this study is it will help to efficiently use the tax payers’ money while building new school buildings. Originality/value This study has collected the hard data of COs, cost and schedule data of CCSD new school building projects. Therefore, the data are from the projects completed by CCSD. So, the paper is written from the original data received from CCSD.

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