Quantitative Assessment for Piles Productivity Factors

The management process of any construction operation usually can be defined as a chain of decisions. A decision-maker can bid, plan, and organize the bored pile projects based on an accurate estimate of productivity. To estimate productivity efficiently, piling process qualitative and quantitative factors have to be considered. This paper focuses on assessing the effect of subjective factors on bored pile construction productivity. A productivity index (PI) model is developed to represent this subjective effect in refining productivity assessment using simulation and deterministic techniques. The analytic hierarchy process and fuzzy logic are used to develop the proposed PI model that relies on the actual performance of 10 main piling process subjective factors. Subjective data were collected from bored pile (drilled shaft) contractors considering these subjective factors. The developed PI model implementation to piling process resulted in PI=0.7. This value has been validated using simulation model outputs.     

Process versus Data Oriented Techniques in Pile Construction Productivity Assessment

A large number of problems faces the installation of pile (drilled shaft) foundations: unseen subsurface obstacles, lack of contractor experience, site planning, etc. These problems make it difficult for the estimator to assess the pile construction productivity and cost. Several techniques might be good candidates for this assessment problem. A fundamental question arises: which technique is the most appropriate to solve this assessment problem? This study focuses on answering this fundamental research question. Data were collected through designed questionnaires, site interviews, and telephone calls to experts in different construction companies. Four different techniques were listed as candidates to solve this problem: deterministic, simulation, multiple regression, and artificial neural network (ANN). They were categorized into two groups: process oriented techniques, deterministic and simulation; and data oriented techniques (DOT), regression and ANN. All techniques were used to assess productivity and cost of pile construction. Their results were compared to determine the closest assessment to real world practice. Research results show that the DOT techniques were the most appropriate whereas they had the lowest deviation from real world practice.     

Simulation as a Tool for Pile Productivity Assessment

Subsurface obstacles, lack of contractor experience, site planning in addition to other factors complicated the installation process of pile foundation. Productivity and cost estimates for such a process are affected by these difficulties. Therefore this study focuses on the piling process productivity and cost assessment using simulation. Data were collected for this study through designated questionnaires, site interviews, and telephone calls to experts in different construction companies. Many variables have been considered in the piling construction process. Two simulation models have been designed and validated to assess piling process productivity and cost. Consequently, two sets of charts have been developed based upon the validated models to provide the decision-maker with a solid planning, scheduling, and control tool for piling process projects.     

Centrifuge Modeling of Large-Diameter Bored Pile Groups with Defects

In this research, centrifuge model pile-load tests were carried out to failure to investigate the behavior of large-diameter bored pile groups with defects. The model piles represented cast-in-place concrete piles 2.0?m in diameter and 15?m in length. Two series of static loading tests were performed. The first series of tests simulated the performance of a pile founded on rock and a pile with a soft toe. The second series of tests simulated the performance of three 2×2 pile groups: One reference group without defects, one group containing soft toes, and one group with two shorter piles not founded on rock. The presence of soft toes and shorter piles in the defective pile groups considerably reduced the pile group stiffness and capacity. As the defective piles were less stiff than the piles without defects, the settlements of the individual piles in the two defective pile groups were different. As a result, the applied load was largely shared by the piles without defects, and the defective pile groups tilted significantly. The rotation of the defective pile groups caused large bending moments to develop in the group piles and the pile caps. When the applied load was large, bending failure mechanisms were induced even though the applied load was vertical and concentric. The test results confirm findings from numerical analyses in the literature.     

Productivity and Cost Regression Models for Pile Construction

The estimating process of pile construction productivity and cost is intricated because of several factors: unseen subsurface obstacles; lack of contractor experience; site planning; and pile equipment maintainability. This study intends to assess cycle time, productivity, and cost for pile construction considering the effect of the above factors using regression technique. Data were collected through designated questionnaires, site interviews, and telephone calls to experts in different construction companies. Many variables have been considered in the pile construction process. Seven regression linear models have been designed and validated to assess productivity, cycle time, and cost. Consequently, three sets of charts have been developed based upon the validated models to provide the decision maker with a solid planning, scheduling, and control tool for pile construction projects. This research is relevant to both industry practitioners and researchers. It provides sets of charts and models for practitioners’ usage to schedule and price out pile construction projects. In addition, it provides the researchers with the methodology of designating regression models for the pile construction process, its limitations, and future suggestions.     

Field Studies in Construction Equipment Economics and Productivity

Earthmoving and construction equipment have evolved significantly during the past century. During the same time, our ability to study and understand equipment economics and productivity has also gained significant ground. This paper relates some of the recent academic research to industry practices. In doing so, it validates some parts of the research and makes new observations in three areas: repair costs, residual values, and total cost of ownership (TCO) and productivity. It also provides a few pointers for future research.     

Productivity and Cost Assessment for Continuous Flight Auger Piles

Continuous flight auger (CFA) piles do not receive sufficient research attention although they are widely used in the United States, United Kingdom, and other countries in the world. Therefore, this paper focuses on describing the CFA pile installation features, exploring its construction methods, and determining the factors that affect the CFA pile productivity and cost. A productivity index (PI) is developed to quantify the effect of subjective factors on the CFA process productivity. Current research concludes a PI value of 0.78 out of 1.0 (efficiency). Several models are designated to assess the CFA pile’s cycle time, productivity, and cost using deterministic approach. Various charts are developed to show this assessment based upon these models. Results show that the cycle time for 12.19?m depth and (0.36?m) (14?in) diameter piles is 16?min; however, its productivity is 24 piles (holes)/day. The developed tools play an essential role in the CFA decision making process. This paper is relevant to both researchers and practitioners. It provides charts for practitioners’ usage to schedule and price out the CFA pile construction projects. In addition, it provides the researchers with a methodology of applying the deterministic approach to the CFA pile construction process and its limitations.     

Benchmarking Productivity Indicators for Electrical/Mechanical Projects

Labor-intensive industries such as the electrical and mechanical trades are considered high risk due to the high percentage of labor costs. Because of this high risk, it is important for contractors in these industries to closely track labor costs on projects and compare these costs to industry benchmarks. In this paper, benchmark indicators for these industries are established on the basis of actual project data. These benchmarks include the relationship between the percent complete or percent time and cumulative work hours or cost, project size and duration, project size and average man power, project size and peak man power, and average versus peak man power. These relationships were developed using regression analysis. Man power loading charts and the related S-curves were developed from actual project data. The man power loading charts and the related S-curves are useful for resource planning and for tracking progress on a construction project. They can be used to show the cause-and-effect relationship between projects impacted by outside factors and normal labor productivity.     

Case Study of Obsolescence and Equipment Productivity

The change in productivity is studied for six different pieces of earthmoving equipment over a period of 15 years. It is shown that the production rate of this group of equipment has increased 1.58% on average per year. During the same period, without adjusting for inflation and making specific assumptions, the unit price of production has increased at an average rate of 1.77% per year. The results of this analysis may be used in modeling obsolescence costs in equipment replacement analysis. The equipment considered includes two models of wheel-type loaders, one track-type loader, one scraper, and two track-type dozer models.     

Benchmarking of Construction Productivity

Construction productivity has been a cause of great concern in both the construction industry and academia. Even though many companies have developed their own productivity tracking systems based on their experiences and accounting systems, none have been successful in establishing common definitions and developing a survey tool that collects standard productivity data at the appropriate levels. This research was initiated to establish a common set of construction productivity metrics and their corresponding definitions. As a result of this research effort, the Construction Productivity Metrics System (CPMS), which contain a list of direct and indirect accounts and 56 data elements grouped into seven major categories, was developed. The Construction Productivity Metrics System is a standard construction productivity data collection tool and provides a framework to report industry norms to benchmark construction productivity. Input from 73 industry experts was used in determining the 56 measuring elements and their corresponding definitions. Preliminary findings from initial sample of 16 industrial projects indicate that the productivity metrics can be produced and should be meaningful for construction productivity benchmarking. Because of the small sample size, more than general preliminary conclusion would be inappropriate. Based on the analyses, the developed CPMS is believed to be a reasonable productivity data collection tool and when sufficient data are available should be capable of producing reasonable industry benchmarks.     

Is Construction Labor Productivity Really Declining?

Macroeconomics data suggest that labor productivity declined significantly in the construction industry during the 1979–1998 period. However, microeconomic studies indicate the contrary. This paper critically examines the construction labor productivity macroeconomic data in the United States from 1979 to 1998 to determine their validity and reliability. Data collection, distribution, manipulation, analysis, and interpretation are reviewed and problems are identified. The paper also presents a comparison of construction and manufacturing labor productivity during this period. The main conclusion of the study is that the raw data used to calculate construction productivity values at the macroeconomic level and their further manipulation and interpretation present so many problems that the results should be deemed unreliable. The uncertainty generated in the process of computing these values is such that it cannot be determined if labor productivity has actually increased, decreased, or remained constant in the construction industry for the 1979–1998 period.     

Impact of Routine Quality Assurance on Reliability of Bored Piles

Quality assurance (QA) tests, such as integrity tests, are routinely conducted to ensure the safety of pile foundations. QA tests provide additional information and result in changes in estimated reliability of pile foundations. This paper aims to formalize a procedure to quantitatively evaluate the impact of routine QA tests on the reliability of pile foundations. Three cases of reliability analyses based on interface coring of large-diameter bored piles are studied; namely, no toe debris detected, toe debris detected without repair, and toe debris detected and repaired. The prior information of the occurrence probability and thickness of toe debris is established based on a practice survey and accumulated QA data. The Bayesian approach is then applied to update the occurrence probability and the mean toe debris thickness based on outcomes of on-site QA tests and remedial actions taken after these QA tests. Subsequently, the reliability of the piles can be updated. The updated reliability can be significantly higher than that before the QA tests. The degree of reliability improvement depends on the pile specifics, the outcomes from the QA tests, and the remedial actions taken after the QA tests.     

Measuring and Modeling Labor Productivity Using Historical Data

Labor productivity is a fundamental piece of information for estimating and scheduling a construction project. The current practice of labor productivity estimation relies primarily on either published productivity data or an individual’s experience. There is a lack of a systematic approach to measuring and estimating labor productivity. Although historical project data hold important predictive productivity information, the lack of a consistent productivity measurement system and the low quality of historical data may prevent a meaningful analysis of labor productivity. In response to these problems, this paper presents an approach to measuring productivity, collecting historical data, and developing productivity models using historical data. This methodology is applied to model steel drafting and fabrication productivities. First, a consistent labor productivity measurement system was defined for steel drafting and shop fabrication activities. Second, a data acquisition system was developed to collect labor productivity data from past and current projects. Finally, the collected productivity data were used to develop labor productivity models using such techniques as artificial neural network and discrete-event simulation. These productivity models were developed and validated using actual data collected from a steel fabrication company.     

Productivity Analysis of Construction Operations

An interactive system for analysis of construction operations is proposed. The analysis is carried out in the context of various work modules which address quantity development, resource definition, and production and cost analysis. The quantity work module generates quantities based on information available in the design documents. The resource definition module receives and stores data regarding the labor∕equipment combination to be used to execute work tasks. This module provides the user with a set of standard useful construction process models. For each construction operation to be analyzed, the terminal describes the standard models. The user makes input of a set of parameters for process keyname, quantity, work task durations, number of resources, production capacity of each unit, and cost per hour of each unit to the standard model to be used. Using input from the resource definition module, the productivity and cost analysis module generates production rates and unit costs based on process simulation using CYCLONE methodology.     

Construction Productivity Improvement

Construction productivity has been on the decline in the last decade. The results are presented on a survey of the Engineering News‐Record 400 largest contractors to obtain their views on where productivity improvements would most help and to compare the trends with a similar survey carried out in 1979. Data were collected on the general company characteristics of the responding contractors, and on the contractors' opinions on potential areas for productivity improvement in the office and in the field. Findings indicate that immediate research should concentrate on improving marketing practices, planning and scheduling, labor‐management relations, site supervision, industrialized building systems, equipment policy and engineering design; and that governmental regulations have lost the immediate urgency attached to them in 1979.It is also recommended that similar surveys be conducted every 3 to 4 years to identify new trends and to steer research in the appropriate direction.     

Latent Structures of the Factors Affecting Construction Labor Productivity

At any moment in time, a multitude of factors simultaneously impact construction productivity. Utilizing the knowledge of thousands of construction craft workers, the writers quantitatively analyzed the underlying structure of the factors affecting construction productivity and identified which factors the craft workers consider to be more relatively important as well. This research identified 83 factors affecting construction labor productivity through 18 focus groups with craft workers and their immediate supervisors on nine jobsites throughout the U.S. Next, a nationwide survey was administered to 1,996 craft workers to assess the impact of these factors on construction labor productivity. Principal factor analyses identified 10 latent factors to represent the underlying structure of 83 productivity factors. In addition, the relative importance of the factors’ impact on construction productivity was examined based on the crafts’ union status, trade, and position (craft worker versus foreman). The writers also compared their results to similar previous efforts, and more importantly, identified significant differences that may impact future productivity improvement strategies. This research will help industry and the research community better understand the factors affecting construction labor productivity and more effectively direct future efforts to improve its performance.     

Factors in Productivity and Unit Cost for Advanced Machine Guidance

This paper presents an initial step in seeking to understand just how the adoption of advanced machine guidance technology, especially global positioning systems, leads to improvements in performance by the earthwork contractor. Two grading scenarios and one dozing scenario are examined based upon site observations and interviews with field personnel. Analysis demonstrated that productivity and unit cost improvements result from a reduction in surveying support, grade checking, an increase in operational efficiency, and a decrease in the number of passes. These results are in agreement with published results of benefits of 3D guidance over 2D guidance.     

Labor Productivity Drivers and Opportunities in the Construction Industry

The study of labor productivity in the construction industry is gaining increasing attention as the industry faces multiple problems related to its workforce. This paper presents the results of a survey instrument applied to determine the relative level of relevance of construction labor productivity drivers and opportunities. Owners, general contractors, electrical contractors, mechanical contractors, consultants, and others participated in this survey. Management skills and manpower issues were identified as the two areas with the greatest potential to affect productivity according to survey respondents. Surprisingly, external factors, which are often cited as a major cause for reduced productivity in the construction industry, were considered to be one of the least relevant productivity drivers. These results suggest that respondents consider the improvement of labor productivity within their reach and control rather than determined by external conditions.     

Learning Curve Models of Construction Productivity

Current research into learning curve models of construction productivity is presented. Five mathematical models are identified and each of these are used to model unit rates for 65 sets of data. The correlation between predicted and actual unit rates is determined, and on this basis, it is concluded that the best predictor is a cubic model. The often cited straight‐line model is only marginally adequate. The validity of the straight‐line model is further undermined by showing that the learning rate is not a constant value. Time data for erecting and setting 466 precast concrete floor planks is used to support the conclusion that the straight‐line model is not a reliable model for predicting future performance.     

Theoretical Study on Pile Length Optimization of Pile Groups and Piled Rafts

Pile groups are frequently designed with equal or similar pile lengths. However, the significant interaction effects among equal-length piles imply that this may not be the optimized configuration. This paper presents the optimization analyses of piled rafts and freestanding pile groups, where pile lengths are varied across the group to optimize the overall foundation performance. The results of the analyses are applicable in cases where the piles derive a majority of the capacity from the frictional resistance. It is demonstrated that, with the same amount of total pile material, an optimized pile length configuration can both increase the overall stiffness of the foundation and reduce the differential settlements that may cause distortion and cracking of the superstructure. The benefits of the optimization can be translated to economic and environmental savings as less material is required to attain the required level of foundation performances. The reliability of the optimization benefits in relation to construction-induced variability is also discussed.