Completion Of Construction Projects Research Articles

The S-Curve as a Tool for Planning and Controlling of Construction Process—Case Study

One of the key tasks of an investor and a contractor at the stage of planning and implementing construction works is to measure the progress of execution with regard to the planned deadlines and costs. During the execution of construction works, the actual progress of the works may differ significantly from the initial plan, and it is unlikely that the construction project will be implemented entirely according to the planned work and expenditure schedule. In order to monitor the process of deviations of the deadline and the budget of the investment task, several rudimentary methods of planning—as well as the cyclical control of the progress of construction projects—are used. An effective tool for measuring the utilization of the financial outlays of a construction project is the presentation of the planned financial flows on a timeline using a cumulative cost chart, the representation of which is the S-curve. The purpose of this paper is to analyze the course of an sample construction project comparing the planned costs of the scheduled works with the actual costs of the performed works, as well as identifying the reasons leading to the failure to meet the planned deadlines and budget of the project implementation. As part of the research conducted at a construction site of a hotel facility, the authors of this paper analyzed each of the 20-month effects of financial expenditures on construction works that were developed and processed by the Bank Investment Supervision (BIS) over a period of three years (between 2017 and 2019). Based on these results, charts and tables of the scheduled and actual cumulative costs of the completed construction project were prepared, the careful analysis of which enables interesting conclusions to be drawn.

Application of knowledge discovery in database (KDD) techniques in cost overrun of construction projects

Currently, cost overrun is a global challenge to completing construction projects successfully. To overcome this problem, earlier studies investigated factors of cost overrun. Knowledge Discovery in Data (KDD) and data mining techniques have been implemented effectively in various research areas to extract novel and valuable knowledge from historical data but have only recently been implemented in the construction industry. The aim of this research is to develop a model that predicts project cost overrun using a suitable data mining technique and cost overrun factors as predictors. A review of the literature identified twelve factors that can be easily measured and analyzed in construction projects. A case study was performed to validate the model with an actual data set of executed projects. The resulting model is simple, interpretable, and relatively accurate (60.87%), and it uses three steps of data mining – clustering, feature selection, and classification. These steps improve model performance.

Contractor project manager leadership style based on path goal theory to support construction sustainability

Sustainability in construction projects has become an essential issue To implement sustainability in the construction project, trained personnel are needed. However, the temporary projects and the turnover of the project team members remained obstructions in performing these principles. Leadership is a competency that must be possessed by the contractor project manager in leading the project and making sure the construction project sustainability constructing subordinate engagement and organizational learning. Various studies on leadership styles that are suitable to be applied in the construction industry have been carried out in various countries, but in Indonesia, it has not been a concern. This study describes the leadership styles of the project manager uses Path-Goal leadership theory. From the research carried out on projects in Jakarta and Bandung, it was found that project managers in construction projects tend to lead by using achievement oriented style. Short construction project completion times and penalties if the project finish late, both are making PM oriented towards achievement, giving targets to the project team and expecting the best performance from the project team. However, project managers lack trust in subordinates to take decisions (participative). It affected the subordinate engagement at work that needed in supporting construction sustainability.

System dynamics modeling approach to quantify change orders impact on labor productivity 1: principles and model development comparative study

In construction projects, change orders are commonly issued by the owner and may cause interruption to the contractor’s work, resulting in loss of labor productivity, delay, and cost overruns, which may lead to claims. Because of the complexity in linking the cause of the productivity loss to the change order, the relationship is not well understood. The contractor must prove with a credible calculation that the productivity loss was a result of the change order issued by the owner to be able to receive compensation. Compared to all available productivity loss quantification methods, the measured mile approach is considered the most acceptable and popular in litigation. This study is a discourse on the understanding, use, and potential application of system dynamics within quantifying change orders’ impact on labor productivity. The model boundary, including Endogenous and Exogenous variables, are identified. After determining the parameters used in modeling, the CLD is constructed to understand the system structure better. A system dynamics model is developed using Vensim software, verified, validated, and utilized to quantitatively measure the influence of the change in the project scope on labor productivity. To examine the applicability of the proposed model, it has been implemented to a real highway construction case study, and the impacts of various variables on labor productivity loss have been studied using the data for two completed highway construction projects from FDOT. The study can be applied to construction projects that faced change orders, and as a result of these changes, the contractor’s performance was affected. The proposed model can be used to determine the primary sources that result in labor productivity loss on a specific type of task.

Emerging Challenges in the Completion of Construction Projects through Public Private Partnerships: Empirical Literature Review

The process of engaging private sector in the provision of public infrastructure can improve economic value in service delivery and enable the government to utilize the capabilities and expertise held by private sector. This has a positive impact on the public in terms of improving their well-being. This literature review attempts to compare the challenges encountered in implementation of PPP projects across the world, with a view to determining cross cutting challenges and recommendations for developing countries. A literature review was conducted for empirical studies focusing on construction projects. The studies are reviewed by analyzing the methodology used and conclusions made to identify the geographical study area, Public Private Partnership project financing model, emerging challenges and recommendation for future project management. The literature search was based on four keywords: construction projects, completion of construction projects, public private partnership, challenges in construction projects. Thus, a total of 11 empirical studies were identified for review in Europe, Asia and Africa. The review revealed that most risk factors affecting project completion can be categorized as human and organizational factors and unknown geotechnical conditions that were not known beforehand. Specifically, project owners’ interference was also attributable to delays with the delays having significant implications for project execution. Risks have great impact on the performance of construction projects because they affect their quality, time and cost. The general remedy to such potential risk factors is adoption of reliable prediction mechanisms which would estimate those factors using a probabilistic model that does not rely on expert judgments as they tend to be unreliable.

Project Manager's Skills Acquisition: A Comparative Study of Indigenous and Multinational Construction Firms

Abstract Project manager’s skills which are the competencies required for successful completion of the project play an important role in the construction industry, therefore acquiring these skills is significant for project success. However, indigenous construction firms compete with their multinational counterparts for the available skilled workforce. The study, therefore, investigates the level of project managers’ skills acquisition in indigenous and multinational construction firms in Lagos State, Nigeria. The study adopted cross-sectional survey research and data were collected using a structured questionnaire. One hundred and thirty-three questionnaires (106 indigenous and 27 multinational) were administered to project managers and 106 retrieved and valid for analysis representing 73% response rate. The techniques for analysing the data are mean score and t-test. The study revealed that the most important skill type as perceived by project managers is a technical skill and there is no significant difference in the level of skill acquisition by project managers in indigenous and multinational construction firms. Hence, the study concludes that the categorisation of construction firms has no effect on the level of skills acquisition by project managers; therefore, it plays no role in project managers’ skills acquisition. Project managers should strive towards developing themselves through training for improved performance and for successful completion of construction projects.

Automated excavators activity recognition and productivity analysis from construction site surveillance videos

The productivity of construction equipment plays an important role in completing construction projects within schedule and under budget. However, the current productivity monitoring on construction sites highly depends on manually observing and recording equipment activities, which is labor-intensive and time-consuming. To address this problem, an increasing number of research studies focused on automatically identifying equipment activities from site surveillance videos. However, these studies failed to accurately conduct the activity recognition and productivity analysis, when multiple pieces of equipment are working together. This research proposes a novel framework for automatically analyzing the activity and productivity of multiple excavators. In this framework, three convolutional neural networks are designed to detect, track and recognize the activities of excavators. The results are further compiled to analyze excavator's activity time, working cycle, and productivity. The proposed framework has been tested with the videos recorded from real construction sites. The overall activity recognition has achieved 87.6% accuracy. The productivity calculation has achieved 83% accuracy, which indicates the feasibility of the proposed framework for automating the monitoring of excavator's productivity.

Probabilistic assessment of the buildings and structures construction duration

One of the main conditions for achieving the set value of the buildings and structures construction duration is the construction production planning. Due to the fact that the construction process has an uncertain dynamic nature, it is recommended to use probabilistic analytical models to determine such a calculated parameter as the duration. The article proposes a probabilistic model for estimating the duration of construction of buildings and structures on the basis of the normalized Erlang's law of the n-th order. This model allows, by changing the number and duration of work lying on the critical path, to assess the construction project completion probability within the prescribed period.

Assessing the predictability of construction time overruns using multiple linear regression and Markov chain Monte Carlo

PurposeThis paper aims to focus on developing a baseline model for time overrun.Design/methodology/approachInformation on 321 completed construction projects used to assess the predictive performance of two statistical techniques, namely, multiple regression and the Bayesian approach.FindingsThe eventual results from the Bayesian Markov chain Monte Carlo model were observed to improve the predictive ability of the model compared with multiple linear regression. Besides the unique nuances peculiar with projects executed, the scope factors initial duration, gross floor area and number of storeys have been observed to be stable predictors of time overrun.Originality/valueThis current model contributes to improving the reliability of predicting time overruns.

Analysis of additional works in completed railway construction projects

The railway construction investments are characterized by a significant extension of individual phases and an increased number of entities participating in the construction investment process. Despite the use of many modern methods and techniques unforeseen phenomena and events which may occur during the execution of the investment cannot be excluded. All unexpected situations usually initiate consequences in the form of additional workload and costs not included by the contractor and longer duration of works. This fact is commonly referred to as the occurrence of the so-called additional works. The paper presents the results of research concerning additional works occurring during the realization of railway construction projects performed in southern Poland. The analysis was based on data which concerned projects completed in the period from 1 January 2012 to 31 December 2018.

Preface

Over the last decades, we have witnessed an explosion in information generation related to all aspects of life, including all engineering disciplines. There has been an increase in active information collection to be used for solving critical engineering problems.Computers are basic means for the efficient completion of construction projects from the design phase to the monitoring of constructions behaviour and facilities during exploitation.The growing sophistication of computer programs, their availability, increased speed of computations and their ever-decreasing costs have already had a significant impact on Civil Engineering and Architecture and that can be considered a paradigm change.The permanent development of the computing tools and the requirements for the delivery of complex and complete products / projects highlight the need to anticipate and, hence, the need to be at the current with the computational trends in constructions.The Conference Computational Civil Engineering become traditional (since 2003).The 15th edition CCE2019 was organized by Faculty of Civil Engineering and Building Services of Iasi in partnership with the International Centre for Numerical Methods in Engineering (CIMNE) Barcelona Spain, the “Matei Teiu-Botez” Academic Society Iasi and the “Anton Sesan” Civil Engineering Academic Society Iasi, Romania. The scientific event was held on May 30th-31st at Iasi, Romania.CCE2019 aimed to bring together leading academic scientists, researchers, practitioners, numerical analysis programs developers to exchange / share their experiences to the Computational Civil Engineering domain to share their research findings to the theoretical and practical levels.

Impact of Utilizing Construction Engineering and Inspection Consultants on Highway Construction Project Cost and Schedule Performance

State transportation agencies (STAs) are tasked with constructing and maintaining complex transportation networks while facing changes in agency construction staff with respect to age, experience level, turnover, retirement, and increasing levels of consulting services that are used to manage STA construction operations. To keep up with the increasing demands of projects, STAs are utilizing Construction and Engineering Inspection (CEI) consultants to fill the demand for construction-related human resources. However, the impact of using CEIs on project cost and schedule performance has not been quantified. The current work addresses this knowledge gap by analyzing a database of 305 completed highway construction projects across 16 STAs. The analysis found that projects that utilized CEI consultants had higher levels of full-time equivalent construction staffing than projects that utilized only agency staff. The use of CEI consultants had no impact on project cost compared with projects that relied solely on agency staff. Projects that used CEI consultants had an average cost overrun of 20.2% compared with agency-staffed projects, which experienced average schedule overruns of −7.7% (i.e., the project, on average, finished early). The schedule difference was statistically significant.

Strategies for a Safe Integration of Water-Bearing MEP Systems in Prefabricated CLT Room Modules

The market for modular buildings based on prefabricated room modules made of cross-laminated timber (CLT) is rapidly growing in Europe. Short construction times combined with high construction quality due to prefabrication are highly attractive features of this construction method. While structural design aspects have been steadily improved, only little attention has been paid to mechanical, electrical and plumbing (MEP) systems so far. However, the integration of water-bearing pipes and surface seals must be done with great care, since a permanent exposure of the timber construction to moisture caused by leaks may result in biological degradation of the wooden structure. The risk of moisture damage can be reduced by decreasing the length of horizontal piping by means of intelligent pipe routing. In addition, sensor technology can be used to detect water leaks in critical areas where other measures fail to provide sufficient protection. Both aspects are addressed in this paper. First, the results of an analysis of nine completed construction projects based on CLT modules including hotels, student residences, nursing homes, and health care centers are presented. The projects were used to identify the current state of practice regarding the design of CLT room modules with respect to the integration of MEP installations. In a next step, general strategies for a safe integration of MEP installations in timber buildings are outlined. A bathroom layout with optimized pipe routing based on three individual shafts is chosen to show the application of these strategies. The approach enables a considerable reduction of horizontal pipe length while maintaining the functional and architectural requirements of the room module. Finally, a new planar sensor for detecting water leaks is presented. The results were developed in the research project SensGT and will lead to standardized solutions for a safe integration of MEP installations in CLT room modules.

Risk of Increase in Prices of Performance in Indian Construction Industry and Sufficiency of Contractual Provision to Mitigate the Same in EPC and Item Rate Contract

Escalation is change in price levels driven by underlying economic conditions. From construction point of view, it is increase in price of cement, steel and other construction material used in construction industry for a defined period of project. Escalation in the construction industry in recent years has been extremely volatile and same scenario is expected to continue in future and also due to tremendous competition for resources like material and skilled workers which are the key factors to complete construction projects. Many contract these days are fixed price contract and it is expected that the risk of price escalation should be taken up by the contractor. It is very difficult for the contractor to take whole risk and in many cases quality delivery becomes difficult. Even the provision of escalation in contract does not cater all the aspects or the escalation in prices of work. Whatever the escalation clause is used outside India, cannot be directly applied in Indian scenario because of low inflation rate present there as compared to India. Therefor in present study, an attempt has been made to study and analyze the volatility of price. The objective of this research is to Study the escalation clauses presently used in construction contracts adopted by various Government departments/ agencies in India and to study the provision of existing contract & its sufficiency to cater the risk arising, its adequacy & coming up with proper solution so that risk can be mitigated and also to study how far cost escalation has an impact on the outcome of construction projects. For this, data has been collected from Economic advisory of India and Labour Union for estimation of percentage of price volatility of construction material and labors from 2012 to 2018 in the interval of 6 months. Years showing maximum fluctuation have been highlighted along with efficiency of present escalation clause with an example. Questionnaire survey will be conducted to highlight important factors related to escalation and come up with proper solution to mitigate the risk for above. And finally a case study will be conducted for the application of above solution.

Influence of Social Economic Factors on Completion of Construction Projects in Public Secondary Schools in Bungoma County, Kenya

The aim of this paper was to establish the influence of social economic factors on completion of construction projects in public secondary schools in Bungoma County, Kenya. The study was informed by construction management and soft value management theories. In the study purposive sampling technique was used in choosing 461study respondents (Principals and Chairpersons of Parents Teachers Association) who were sampled to ensure homogeneity of the selected sample in ensuring that samples were drawn from each region encompassed in the target population, then followed by simple random sampling technique from each sub county. Questionnaires and interview schedules were the main data collection instruments. Data analysis involved use of statistical package for social sciences, SPSS version 21 tool where both descriptive and inferential statistics were used. Cronbach Alpha of coefficient of 0.831 was attained on all constructs of social economic factors, which was above 0.7 as recommended by Cronbach (1951) implying the research instruments were reliable. The correlation coefficient (R) or the beta value β of 0.423≠0 at p=0.00 indicated that the hypothesis was accepted. The coefficient of determination, R-square of 0.422 implied that 42.2% of the variance in completion of construction projects was attributed to social economic factors. From the study findings, social economic factors namely; interpersonal skills of project manager, inflation, corruption and community involvement affects completion of construction project progress. The community should be involved directly in school projects. This will improve their perception and goodwill towards completion of construction projects in public secondary schools. The management need to be keen on price fluctuations that eventually have an effect on completion of construction projects. Future research is encouraged to cover other sectors other than education and compare the findings. The findings are of importance to the Ministry of Education in Kenya and other interested parties in future. Researchers in future have a basis for reference from this study. Keywords : Social economic factors, Completion of construction projects, Public Secondary schools. DOI : 10.7176/EJBM/11-12-05 Publication date : April 30 th 2019

Identifying and Measuring Engineering, Procurement, and Construction (EPC) Key Performance Indicators and Management Strategies

Delay and cost overrun in construction projects are two widespread problems that arouse practitioners’ and scholars’ concern. Therefore, the objective of this study was to identify Key Performance Indicators (KPIs) in each of Engineering, Procurement and Construction (EPC) phases separately and then calculate the weight impact that is associated with each of identified KPIs. Additionally, this study aimed to find the most appropriate Best Practices (BPs) for the identified phase-based indicators. Through a review of existing literature, a survey was developed to collect data from the completed construction projects. Various statistical methods, including two sample T-test and Kruskal-Wallis test, were utilized to analyze the data. Subsequently, Epsilon-Squared effect size method was applied to prioritize the identified KPIs. Since the lack of communication and slowness in decision making were found as the primary schedule Performance Indicators, schedule performance was identified to be highly affected by managerial approaches and actions. In addition, clients play a decisive role, because owner-driven change orders were found as the most crucial cost performance indicator affecting the performance of the engineering and construction phases. The outcome of this study helps practitioners and scholars to understand the phase-based cost and schedule KPIs, and appropriate mitigating strategies to improve the construction performance in EPC phases and save time and money.

Modelling Construction Site Cost Index Based on Neural Network Ensembles

Construction site overhead costs are key components of cost estimation in construction projects. The estimates are expected to be accurate, but there is a growing demand to shorten the time necessary to deliver cost estimates. The balancing (symmetry) between time of calculation and satisfaction of reliable estimation was the reason for developing a new model for cost estimation in construction. This paper reports some results from the authors’ broad research on the modelling processes in engineering related to estimation of construction costs using artificial intelligence tools. The aim of this work was to develop a model capable of predicting a construction site cost index that would benefit from combining several artificial neural networks into an ensemble. Combining selected neural networks and forming the ensemble-based models compromised their strengths and weaknesses. With the use of data including training patterns collected on the basis of studies of completed construction projects, the authors investigated various types of neural networks in order to select the members of the ensemble. Finally, three models that were assessed in terms of performance and prediction quality were proposed. The results revealed that the developed models based on ensemble averaging and stacked generalisation met the expectations of knowledge generalisation and accuracy of prediction of site overhead cost index. The proposed models offer predictions of cost in an accepted error range and prove to deliver better predictions than those based on single neural networks. The developed tools can be used in the decision-making process regarding construction cost estimation.

Competency Assessment for State Highway Agency Project Managers

Project managers (PMs) play a key role in the successful completion of construction projects. Therefore, PM competencies have been heavily investigated over the past few decades, often focusing on two types of skills: hard skills, including technical knowledge; and soft skills, including personal traits. This extensive body of knowledge was lacking the focus on PMs of transportation projects who work in a state highway agency (SHA). This research addresses this gap by developing a comprehensive competency assessment model tailored for SHA PMs. This research identified 55 PM essential competencies spanning five categories: project management knowledge and experience; leadership; SHA operational knowledge and experience; industry knowledge and experience; and cognitive/personal effectiveness. Also, by collecting extensive data from 90 PMs, this research developed a mathematical model to compute competency weights for differentiating exceptional and average PMs. The weights were then used to create a comprehensive score that can assess PM competencies as an overall percentage. This score was benchmarked using the collected data to distinguish between exceptional and average PMs. To effectively communicate the research findings to industry practitioners, a computer-based tool was created. This tool assists SHAs and their PMs in assessing competencies, and identifies training needs to improve overall PM performance and skill sets. The tool was tested and validated by five Wisconsin Department of Transportation PM supervisors, all of whom acclaimed its accuracy and potential value.

Logistic Regression Modeling to Determine Projects impacted by Schedule Compression

The competitive market realities in industrial environments demand timely completion of construction projects making time conservation a major concern for both owners and contractors. And the unpredictability of a construction project often leads to disputes followed by litigations between owners and contractors. Schedule compression is a common practice to achieve this timely completion of projects, however, can have detrimental consequences in terms of labor productivity and subsequent cost increase. Loss of productivity, however, is difficult to quantify especially when stemming from compressed schedule. Numerous researchers and trade associations have developed productivity factors to quantify the impact of schedule compression on labor productivity, but there has not been a method to quantitatively determine whether the project was impacted by schedule compression or not. This paper introduces a logistic regression impact model by analyzing the quantitative definition of schedule compression. The model will enable the user to determine if the schedule compression resulted in productivity loss or not. Based on the analysis of eight different factors, the logistic model will allow contractors and owners to determine the probability of a project being impacted by schedule compression.

Risk management in construction projects: a study on the state-of-practice

Construction, a key economic activity worldwide, is vulnerable to several risks, which compromise the successful completion of construction projects, and call for the implementation of risk management activities. Within this context, it is the aim of the study presented in this paper to investigate the current state of risk management practice in construction projects along with the factors that affect it, and the relationship of risk management with projects' success. To this end, a questionnaire survey was undertaken among professionals of medium and large construction companies active in the regions of Greece and Middle East, and data were analysed with statistical techniques. The analysis results indicate that construction companies, irrespective of size and region, get engaged in, but do not systematically implement, risk management activities during the whole lifecycle of their projects, a fact that influences projects' successful completion in terms of compliance to budget, time schedule, and specifications and standards.