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Computer simulation in construction planning has been the subject of research for the last few decades. The present paper describes simulation models geared toward improving the productivity of concreting operations. It is primarily concerned with a study of the sensitivity of concreting operations to a set of possible resource combinations. Thirteen models are examined by the two well‐known methods of concreting: (1) crane and bucket; and (2) the pump. Concreting into slabs, beams and columns are considered. The simulation software Micro‐CYCLONE is used for the actual generation of models. Sensitivity parameters considered in resource combinations include the number of truck‐mixers, buckets and labourers in concrete placing crews. The simulation models developed are compared and the results are discussed. The results enable planners to realize how the resource quantities and capacities in one cycle affect the ones in another period for cyclic operations like concreting. It can be concluded that the maximum number of resources, the interaction of work crews caused by work space limitations and the interaction of equipment because of sharing with other activities of the project may bring limitations.

With an increasingly competitive global market, the UK construction industry finally realised that in order to survive, a marked increase in efficiency and effectiveness have to be achieved in all areas. This paper will describe the UK's approach to planning and designing the concrete operations that form a major part of many civil engineering construction projects. A productivity study has been carried out on three different construction projects and over 200 concrete pours have been observed. The data and knowledge collected on site have been subjected to lean construction philosophies, producing a “lean” measure of productivity, and it has been shown that major productivity increases could be achieved by implementing several relatively simple principles.

The crane plays an essential role in modern construction sites as it supports numerous operations and activities on-site. Additionally, the crane produces a big amount of data that, if analyzed, could significantly affect productivity, progress monitoring and decision-making in construction projects. This paper aims to show the usability of crane data in tracking the progress of activities on-site.

This paper presents a pattern-based recognition method to detect concrete pouring activities on any concrete-based construction sites. A case study is presented to assess the methodology with a real-life example.

The analysis of the data helped build a theoretical pattern for concrete pouring activities and detect the different phases and progress of these activities. Accordingly, the data become useable to track progress and identify problems in concrete pouring activities.

The paper presents an example for construction practitioners and researcher about a practical and easy way to analyze the big data that comes from cranes and how it is used in tracking projects' progress. The current study focuses only on concrete pouring activities; future studies can include other types of activities and can utilize the data with other building methods to improve construction productivity.

The proposed approach is supposed to be simultaneously efficient in terms of concrete pouring detection as well as cost-effective. Construction practitioners could track concrete activities using an already-embedded monitoring device.

While several studies in the literature targeted the optimization of crane operations and of mitigating hazards through automation and sensing, the opportunity of using cranes as progress trackers is yet to be fully exploited.

Due to considerable contributions of the construction industry to the global carbon emissions, a great deal of attention is placed on possible incorporation of carbon footprint minimization as an important objective in the planning of construction operations. The purpose of this paper is to present a framework to estimate and minimize the carbon emissions of the concrete placing operation through identifying the optimal number of pumps and the inter-arrival time of truck mixers.

The proposed framework integrates discrete event simulation and multi-objective optimization to estimate and minimize the carbon emission, costs and production rate of the concrete placing operation. An actual construction project is used to demonstrate the application of the proposed framework. Furthermore, a sensitivity analysis is performed to investigate the sensitivity of the results to variations in modeling parameters including the ratio of idle to non-idle emission rates of equipment and the activity duration distributions.

The results of the case study highlight that variations in the number of pumps and inter-arrival time of truck mixers significantly affect the carbon emissions, cost and production rate of the concrete placing operation. Furthermore, the results of the sensitivity analysis show that variations in the ratio of idle to non-idle emission rates for pumps and truck mixers have little effects on the selected setting for the project. This is contrary to the effect of uncertainty in the activity duration distributions, which was found to be significant.

Results of this study provide an insight into the trade-off between carbon emissions, cost and production rate of the concrete placing operation.

The present investigation utilizes a bespoke methodology to analyse and compare the productivity rates of contractors' planning engineers for concrete placing operations amongst three European construction industries, namely Germany, France and the UK. An analysis of variance (anova) was used to investigate differences between the productivity rates. Based on such rates, the analysis shows that German contractors achieve the most efficient levels of labour productivity for this particular operation, whilst amongst the sample surveyed, British contractors are less productive than French and German companies. Although leading British contractors can compete with the best on the continent, the least productive companies in the UK sample were inferior to the least productive in France and Germany. Using national all‐in rates for labour, actual (labour) costs for this concrete operation were calculated to be lowest in France despite French wage rates being marginally higher than in the UK. This was because of the superior labour output of French contractors. The apparent lower productivity of British firms sampled in the present research concurs with the findings of two other international studies, indicating that the methodology utilized can provide meaningful and accurate productivity information.

A contrast of site productivity levels for an in situ concrete operation (reinforcement fixing) on a high‐rise project amongst construction contractors from Germany, France and the UK is given. The productivity rates provided by contractors' planning engineers for a model construction project form the basis of this evaluation. Conclusions drawn, based on relatively small samples, are considered approximations of the actual productivity levels in each international location. An analysis of variance based on international origin indicates significant differences between these productivity rates. Generally, amongst the sample surveyed, UK and German contractors exhibit the most efficient levels of labour productivity for the operations observed, whilst French contractors are by far the least productive. For the model building, UK contractors are the most productive, requiring less labour input than those from Germany and France. The UK contractors also demonstrate a high degree of performance variation. Leading on from these analyses, a construction (labour) cost comparison indicates the UK to be the most economic location. A comparison with previous research indicates contrasting findings. It is concluded that the performance ranking of French, German and UK contractors will vary depending upon the construction operations concerned, and therefore, assumptions regarding national contracting industries should not be based on individual operations. Contractors could benefit from developing closer links with their international counterparts since this would facilitate dissemination of European ‘best practice’.

The present paper reports on the development of SITE EXPERT: a prototype knowledge‐based expert system. It is an advisory system. SITE EXPERT is intended to be used for productivity improvement in construction and provides advice on: (1) the productivity of three basic operations of construction, i.e. pouring and placing of concrete, erection and removal of formwork, and fixing reinforcement; and (2) human resources and site layout as productivity factors. The system uses information from construction experts, text books, data recorded at construction sites and the engineer's own knowledge, as well as knowledge obtained by running simulation models. In the present paper, the development, operation and evaluation of the prototype system is described. The results of this prototype system development demonstrate that artificial intelligence methodologies provide powerful facilities for capturing information about construction processes and advising the practitioners of construction on productivity improvement within a computer format close to human reasoning.

This paper aims to study the effects of different weather conditions on typical concrete work tasks’ productivity. Weather is one important factor that has a negative impact on construction productivity. Knowledge about how weather affects construction works is therefore important for the construction industry, e.g. during planning and execution of construction projects.

A questionnaire survey method is used involving means to perform pairwise comparisons of different weather factors according to the analytical hierarchical process (AHP). The survey also contains means to enable assessment of the loss in productivity for typical work tasks exposed to different weather types. The survey targets practitioners involved in Swedish concrete construction projects, and the results are compared with previous research findings.

The survey covers responses from 232 practitioners with long experience of concrete construction. The pairwise comparisons reveal that practitioners rank precipitation as the most important followed by wind and temperature. The loss in productivity varies significantly (from 0 to 100%) depending on the type of work and the type of weather factor considered. The results partly confirm findings reported in previous research but also reveal a more complex relationship between weather and productivity indicating several underlying influencing factors such as type of work, type of weather (e.g. rain or snow) and the intensity of each weather factor.

This paper presents new data about how 232 practitioners assess the effects of weather on construction productivity involving novel means to perform objective rankings such as the AHP methodology.

The purpose of this paper is to investigate the effects and relative influence of: surface floor area; number of trowelling machines used in the operation; machine floating ring diameter size; and operative employment mode, on power‐trowelling productivity of concrete surface floors.

To achieve this objective, a sufficiently large volume of productivity data was collected and analyzed using the categorical‐regression method. As a result, the effects and relative influence of the factors investigated on trowelling productivity were determined and quantified.

The findings show significant influence of the factors explored on the trowelling productivity of concrete surface floors, which substantiate the importance of the “economy of scale” concept, and confirm the negative effect of “overcrowding”. On the other hand, contrary to many previous productivity research findings, and anecdotal perceptions of industry practitioners, the results obtained provide little evidence to the positive impact of the subcontracting employment mode on the trowelling operation.

Further research into the influence of other factors which could not be determined by this study, i.e. quantifying the difference in trowelling productivity between the “walk‐behind” and “ride‐on” machine types, and the average change in the operation efficiency as a result of utilizing the 0.60 m “walk‐behind” floating ring diameter size, in comparison with the 0.80, 1.00, and 1.20 m explored ring‐size, is recommended. On the other hand, the results obtained suggest that for a specific surface floor area, there may be an optimum number of trowelling machines, which leads to optimum trowelling productivity. It is, therefore, recommended to determine this number in order to rationalize the use of such devices and optimize the efficiency of the operation.

The findings of this research can provide estimators, planners and construction managers guidance for reasonable estimates, effective planning and efficient operative utilization. The results obtained may be further used to “benchmark” the efficiency of the operation, and “formalize” the specific productivity knowledge acquired.

The outcomes of this study fill a gap in productivity knowledge of primary factors influencing an important surface floor finish technique, which is frequently encountered on concrete construction sites, especially parking structures, storages and industrial facilities.

To define the ratio of usability of various options in the choice of construction machinery for concreting operations which is realistic in practice, as well as the ratio of usability of various mathematical tools.

In construction industry optimal choice of options of construction machines is obtained mostly by a conventional procedure. However, a conventional procedure does not often give a clear answer to a question which option should be chosen as the best one, so that some other method for the choice of machinery should be employed in a decision‐making process. Owing to uncertain and imprecise environment in which a system of construction machines exists, the theories of rough sets and fuzzy sets are chosen as mathematical framework to solve a problem of optimal choice of construction machinery.

The need for application of three models – conventional procedure and rough and fuzzy sets approaches, in optimal choice of options of construction machines, is recognized.

Accessibility and availability of data are the main limitations which model will be applied.

A very useful advice for construction processes managers.

The new approach of optimal choice of options of construction machines due to fuzzy and rough sets. The paper is aimed at operational researches and engineers, especially those who dealt with machinery.