Volume 13 (2021): Issue 1 (January 2021)

Even though horizontally linear projects have low complexity schedules, they are still not successful in meeting planned time. The deadlines are mostly based on estimations done in front-end project development when limited data are available. Early time estimation models in literature rely on few variables and, almost in all cases, one of them is the estimated cost. Early cost estimations can significantly deviate from actual costs and thus lead to unreliable time estimation. Time estimation models based on neural network and other alternative methods require databases and software, which complicates the process of time estimation. The purpose of this paper is to bridge the gap of scarce time estimation models and unreliable time estimates by developing a new method for time estimation. This research has been done on one large sewer system project. The case study shows how to extract several continuous activities for a pipeline project chosen from a sewer system. Moreover, a new algorithm for the calculation of project duration is devised based on the existing equation related to the linear scheduling method, and this algorithm works with continuous activities. The new method for construction time estimation is based on the extraction of linear continuous activities, usage of the algorithm for identification of minimal buffer between activities, and calculation of the project duration. To verify the algorithm, this method is used on another pipeline project from a sewer system. The limitation is that this method can be used only for base estimation. Further research needs to be done to include uncertainties and risks in the method.

There is no doubt that each project manager uses different methodologies, methods, tools and techniques (MMTTs) while dealing with constant pressure to deliver results in a complex and changing environment, where complexity and stakeholders’ expectations are continually growing. This article presents results from the study about project managers’ choice on artefacts that they frequently use in their regular work. This study was conducted within the context of selected environment (one country and one sector of engineering projects), but designed methodology and results might also be significant for a wider audience. The results were based on the feedback that were received from 31 project management (PM) professionals gathered in the form of a structured questionnaire, followed by an interview with three PM experts for validation. The results confirmed a variety of PM artefacts in use and provided their ranking by perceived usefulness. The additional results indicated correlation between PM success and usage of MMTTs, as perceived by project managers. This study also resulted in several interesting findings, such as popularity and usefulness of artefacts, rigidity of PM processes, and maturity of PM organizations, about the topic. Despite of rather small sample taken from one country's environment, the results could serve as a solid information for moving ahead, while considering the significance of PM artefacts, as well as for their further development. The key findings are also messages to PM professionals to consider whether they use available and appropriate PM artefacts and is there a room for more efficient and effective usage.

Current estimates of the actual productivity of heavy construction machinery at a construction site are not supported by an appropriate and widely used methodology. Recently, for the purpose of estimating the actual productivity of heavy construction machinery, vision-based technologies are used. This paper emphasizes the importance of estimating actual productivity and presents a way (i.e. a research framework) to achieve it. Therefore, the aim of this paper is to propose a simple research framework (SRF) for quick and practical estimates of excavator actual productivity and cycle time at a construction site. The excavator actual productivity refers to the maximum possible productivity in real construction site conditions. The SRF includes the use of a video camera and the analysis of recorded videos using an advanced computer program. In cases of continuous application of SRF, a clear and transparent base for monitoring and control of earthworks can be obtained at an observed construction site.

Large infrastructure investments are expected to be of sustained value to society for a long time. Such investment projects include, for instance, hospitals, tunnels, sport arenas, power plants, roads, railways, and bridges. They involve a complex organization of contracts and agreements. The client is expected to plan, procure, and determine the critical steps of a project, while the contractor should solve issues raised by the client. Many of these agreements are path-dependent and reflect past routines, experiences, and contacts. As such, many investments tend to return to similar sources instead of replacing routines and collaborations that did not work. This can cause change orders that furthermore reflect consequences such as cost and time overruns. While much is known about these effects in construction projects, this paper sheds light on the drivers of change orders. We build upon a sample of 234 observations responding to a survey on investment planning. The results show that project assumptions are often wrong and inadequate in large investments. Such wrong assumptions are caused by interpersonal and leadership issues, poor planning, or sometimes even intentional profit-seeking. Our results show that clients and contractors have different perceptions and enter contractual obligations differently. The implication is, therefore, that better routines of documentation, more frequent feedback, and more accurate or precise standards may close the gap between planning and what is actually achieved. More precise contractual agreements may also create a better process to procure, manage projects, and allocate resources.

The objective of this study is to analyse the impact of management practices on the productivity of building construction projects in India. The methodology adopted for this study is to identify and analyse the management practice attributes from the literature review and expert focus group interviews. The identified attributes were analysed and priorities using relative weight were given by the respondents. The collected data were further analysed using SPSS 21 software. The quantitative research methodology was adopted to analyse the collected data and the following tools and techniques were applied to the data: reliability analysis to check the consistency of data collected for this study and relative importance index (RII) to prioritise and rank the attributes based on the weighted average score given by the respondents. The findings of this study concluded that coordination between all stakeholders, ability to handle the crisis by the project managers, social skills of key team managers, timely payment of completed works and design capability and frequent design changes are the most significant attributes of management practice, affecting construction productivity and having a rating on the RII of 0.91, 0.90, 0.88, 0.87 and 0.87.

This paper aims to develop a framework to achieve sustainability by overcoming the challenges of the construction supply chain (CCSC) during the design process. To achieve this, two approaches, namely theoretical and practical, were used to accomplish four objectives. For the theoretical approach, based on literature review and case studies, the objective used was to identify, classify and validate the challenges that the construction supply chain (CSC) encounter. For the practical approach, a survey questionnaire was employed to quantify the CCSC and investigate the perception of architectural design firms (ADFs) in Egypt towards achieving sustainability by overcoming the CCSC during the design process. Based on the results, the research developed a framework to overcome the CCSC as an approach towards achieving sustainability in construction projects during the design process. The research identified and validated 20 challenges that the CSC encounter towards achieving sustainability during the design process. These challenges were classified under four categories, namely (1) design and technical process; (2) coordination, information flow and accuracy; (3) material specification, technology, supplier rework and whole life cycle cost; and (4) skills gap of the qualified architects and design managers and non-compliance to building codes, regulations, laws and standards. In addition, a survey questionnaire was employed to rank these challenges according to their importance on 1–5 Likert scale using the measure of central tendency and dispersion and relative importance index (RII).

Buildings are interactive environments in which their operations and occupants are linked. Although buildings are operated according to the standards, occupant complaints may arise when there is a mismatch between indoor environmental conditions and actual user needs. Therefore, the accuracy of thermal comfort prediction models suggested by the standards and alternative prediction models need to be investigated. This study aims at assessing the performance of the predicted mean vote (PMV) model suggested by the ISO 7730 Standard to detect occupant thermal dissatisfaction. In addition, a multivariate logistic regression model was developed to predict thermal complaints with respect to “too warm” and “too cold.” This case study was conducted in a commercial building located in Paris, France, between January 2017 and May 2018. Indoor environmental conditions were monitored via sensors and an online tool was used to collect occupant thermal complaints. A total of 53 thermal complaints were analyzed. The results showed that all the operative temperature measurements in both the heating and cooling seasons were within the thresholds suggested by the standards. The PMV method suggested that only 4% of the occupants were dissatisfied with the indoor environment whereas the actual dissatisfaction ratio was 100% under these indoor environmental conditions. In addition, the multivariate logistic regression model showed that operative temperature and season have a significant effect on thermal complaints. Furthermore, the accuracy of the developed model was 90.6%.