Crew Performance Measurement Via Activity Sampling

Thomas, H. Randolph; Daily, Jeff

doi:10.1061/(asce)0733-9364(1983)109:3(309)

Cited by 51 publications

(30 citation statements)

References 1 publication

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“…Work measurement techniques such as activity sampling and time and motion study is used for calculating task level LP on construction site. Activity sampling is a method in which observations are taken at specified intervals and are not continuous which has been successfully utilized for calculating the labour output in the past [22]. These observations constitute to the sampling of a construction activity.…”

Section: Work Measurement Techniquesmentioning

confidence: 99%

The Analysis of Essential Factors Responsible for Loss of Labour Productivity in Building Construction Projects in India

Karthik¹,

Rao²

2019

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The purpose of this paper is to find the essential factors influencing the loss of labour productivity (LP) in construction in India and substantiate these factors with naturalistic observation method in a building construction project. From the past studies, factors influencing the LP are specifically selected for the survey and statistically analyzed to form into defined group of factors. The influence of these factors is then correlated to the field level LP. For this, a case study is conducted using TMS technique to inspect factors influencing LP in an ongoing multi storied residential building construction site in Telangana State, India. The LP factors identified by survey analysis are quantitatively validated with the field study. It is noted that work characteristics, organizational characteristics, assured and safety work and workers management were responsible for 15% loss of LP in the field. Of these, major loss of about 11% is shown by work characteristics factors such as material delay and tools delay. This method can be utilized by construction personnel to measure the loss of LP with the data available from survey methods and also assists the construction personnel in making timely decisions towards the improvement of LP for various activities on the construction project sites.

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Section: Work Measurement Techniquesmentioning

confidence: 99%

The Analysis of Essential Factors Responsible for Loss of Labour Productivity in Building Construction Projects in India

Karthik¹,

Rao²

2019

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“…(CPMS) (Park et al, 2005); activity/work sampling techniques (Richardson, 1976;Thomas, 1981Thomas, , 1991Thomas and Daily, 1983;Thomas et al, 1984;Liou and Borcherding, 1986;Oglesby et al, 1989); time (or stopwatch) studies (Salvendy, 2001); and questionnaires and surveys (e.g. foreman delay survey and craftsman questionnaire sampling introduced respectively by Tucker et al in 1982, and.…”

Section: Productivity Monitoringmentioning

confidence: 99%

An exploratory study of the relationship between construction workforce physical strain and task level productivity

Gatti

Migliaccio

Bogus

et al. 2013

Construction Management and Economics

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The monitoring of construction workforce physical strain can be a valuable management strategy in improving workforce productivity, safety, health, and quality of work. Nevertheless, clear relationships between workforce performance and physical strain have yet to be established. An exploratory investigation of the relationship between task level productivity and physical strain was conducted. Nine participants individually performed a four-hour simulated construction task while a wearable physiological status monitor continuously assessed their physiological condition. Heart rate, relative heart rate, and breathing rate were utilized as predictors of physical strain, and task level-single factor productivity was used as an index of productivity. Numerous regression models were generated using the collected data. This investigation initially unsuccessfully attempted to establish a relationship between physiological condition and productivity at the individual worker level. However, an analysis of the regression models showed that there is a relationship between productivity and either heart rate or relative heart rate at the group level, and that this relationship is parabolic. Breathing rate was proved to not be a significant predictor of productivity. Research results significantly improve understanding of the relationship between work physiology and task productivity. Researchers and practitioners may use the tested monitoring devices, analysis methods, and results to design further applied studies and to improve workforce productivity.

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“…Finally, the achieved results are discussed and compared with relevant research. It is worthy to note that authors do not intend to estimate the size of crew or machinery for concrete pouring which has been discussed in literature extensively (Thomas and Sakarcan, 1994, Thomas et al, 1984, Thomas and Daily, 1983, Thomas, 1991, Sonmez and Rowings, 1998, Crawford and Vogl, 2006, Borcherding and Alarcon, 1991, Zahraee et al, 2013, Dunlop and Smith, 2004. Beyond the construction sites there are important variables that have not been carefully taken into account for predicting the productivity of concrete pouring.…”

Section: Methodsmentioning

confidence: 99%

Predicting the Duration of Concrete Operations Via Artificial Neural Network and by Focusing on Supply Chain Parameters

Maghrebi¹,

Sammut²,

Waller³

2014

Building Research Journal

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Being able to precisely predict the duration of concrete operations can help construction managers to organize sites and machineries more efficiently, especially when there is limited space for equipment on site. Currently there is no theoretical method for estimating the duration of the concrete pouring process. Normally, the maximum capacity of pumping facilities on construction sites is not used, and concrete pumps are idle for a considerable time as a result of the arrival of concrete trucks being delayed. In the light of this issue, this paper considers the supply chain parameters of Ready Mixed Concrete (RMC) as a means of solving this problem. Artificial Neural Network (ANN) is hired for modelling/predicting the productivity of a concrete operation. The proposed model is tested with a real database of an RMC in the Sydney metropolitan area that has 17 depots and around 200 trucks. Results show that there is an improvement in the achieved results when these are compared to the results of relevant studies that only considered the construction parameters for predicting the productivity of concrete operations.

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Crew Performance Measurement Via Activity Sampling

Cited by 51 publications

References 1 publication

The Analysis of Essential Factors Responsible for Loss of Labour Productivity in Building Construction Projects in India

The Analysis of Essential Factors Responsible for Loss of Labour Productivity in Building Construction Projects in India

An exploratory study of the relationship between construction workforce physical strain and task level productivity

Predicting the Duration of Concrete Operations Via Artificial Neural Network and by Focusing on Supply Chain Parameters

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