Effectiveness of communication of spatial engineering information through 3D CAD and 3D printed models
© Dadi et al.; licensee Springer. 2014
Received: 19 June 2014
Accepted: 23 October 2014
Published: 12 November 2014
Poor engineering information provided to construction crews results in inefficient communication of design, leading to construction rework, disputes, and lower worker morale. The resulting errors, omissions, and misinterpretations indicates that a significant opportunity exists to improve the traditional documentation of engineering information that craft professionals use to complete their work. Historically, physical three dimensional (3D) models built by hand provided 3D physical representations of the project to assist in sequencing, visualization, and planning of critical construction activities. This practice has greatly diminished since the adoption of three dimensional CAD (computer-aided design) and BIM (building information modeling) technologies. Recently, additive manufacturing technologies have allowed for three dimensional printing of 3D CAD models.
The effectiveness of 2D drawings, a 3D computer model, and a 3D printed model in delivering engineering information to an end-user are scientifically measured.
The 3D printed model outperformed the 2D drawings and 3D computer interface in productivity and workload measures.
A physical model has the ability to improve communication of spatial design for certain tasks. This could lead to improved productivity, reduction of errors, and better quality for construction tasks. This paper's primary contribution to the body of knowledge is that it identifies how different mediums of engineering information impact the communication of spatial engineering information.
KeywordsCommunication Visualization 3D printing Labor productivity
While the U.S. construction industry comprises 5.4% of the overall U.S. Gross Domestic Product (GDP) (Bureau of Economic Analysis ; and Huesman et al. ), it lags the rest of the economy in terms of productivity measurement and improvement (Goodrum et al. ). Due to changes in real output and differences in accounting procedures, there is no isolated industry level measure of labor productivity (Bureau of Labor Statistics ), although recently the U.S. Bureau of Labor Statistics has developed an aggregate multifactor productivity measure for the combined agricultural, mining, and construction industries (Bureau of Labor Statistics ). Regardless of this recent advancement, an isolated productivity index for the U.S. construction industry makes it difficult to track progress, benchmark, and measure the effects of governmental and industrial policies across the construction industry.
However, at a project level, productivity figures are more diligently kept, although still inconsistently company to company. With profit margins near 3%, firms must do what they can to track their performance and make necessary changes (Cooper and Lee ). A major contributor to the overall productivity is execution of workface practices. If construction practitioners are not equipped with the necessary tools, information, materials, and equipment to effectively perform their tasks, the productivity of the project will be negatively affected. This research presents insights into how different mediums, specifically two dimensional drawings, three dimensional computer models, and three dimensional physical models, of engineering information influence the performance of task execution.
Information delivery and its effect on construction productivity
Traditionally, two dimensional drawings (commonly referred to as blueprints) are the spatial and technical communication media for all project participants (Gould and Joyce ). Drawings are presented in a variety of formats including plan views, elevations, detailed sections, and isometrics. Individual drawings are often scaled, list dimensions, and frequently reference other sheets to help provide the user a representation of the final design intent from all viewpoints.
In this standard, linear model of communication, the sender must encode their interpretation of the desired end user information. This interpretation is then translated in the message medium, whether it is verbal or non-verbal. Receivers must decode the message into their individual interpretation, where the final message is processed and understood. However, the intermediary steps of encoding the message, the creation of the message, and decoding the message increases the potential for noise that can affect the ultimate outcome of the communication. The message channel is the actual flow of the message, verbal or nonverbal, from the sender to the receiver. In the linear model, there is very little, if any, feedback from the receiver to the sender, where ultimately only a downstream process occurs (Shannon and Weaver ; Schramm ; and Berlo ). In the communication of construction documents, poor design and poor design communication are two distinct opportunities for errors (Eckert and Boujut ). Poor design is the result of errors made by designers in the encoding process, while poor design communication are errors made in the interpretation or decoding of the message.
Errors made through poor design have been studied and identified as having an impact on project performance. Several studies discuss errors in the design outputs concluding that many practitioners feel information delivery is a deterrent to efficiently performing their job (Goodrum and Dai ; Dai et al. [2009a]; Dai et al. [2009b]; Mourgues and Fischer ; and Rojas ). The main inefficiencies from drawing management according to a survey of construction craft workers are due to errors in the drawing, availability of the drawings, slow management response to questions, legibility, and omission of necessary information on the documents (Goodrum and Dai ; Dai et al. [2009a]; and Dai et al. [2009b]). In a focus group study, Dai et al. () found that craft professionals believe 32% of their negative productivity is due to inaccuracy and poor quality of information, with individuals claiming that “some drawings could not be physically built”. Rojas () discusses inefficiencies from design drawings ultimately leading to increased rework on the project. Borcherding and Samelson () found that rework was one of the three most significant drivers to poor productivity and decreased morale, oftentimes as a result of poor engineering information design. Haas et al. () found that design, engineering, instruction, and monitoring accounted for 29.08% of the total amount of rework on an analysis of records from the industrial sector. Supervisors and foremen then become focused on correcting engineering errors and rework instead of planning future work and focusing on crew performance. In the highway construction sector, an analysis of change orders showed that omissions of information led to a 4.53% increase in original contract amount among a sample of roadway projects (Taylor et al. ). With 40% of the total construction cost being in direct and indirect craft labor, there is a need to maximize efficiency and reduce non-value adding activities of the workers (Haas et al. ).
Similarly, poor design communication can lead to errors and confusion in task execution. Some of the issues result from the difference in the message intended versus the message received. The format and intent of drawings is easier to comprehend by the designer that creates the drawing than it is for the contractor and his/her workforce that has to interpret the message (Emmitt and Gorse ). This problem is magnified when the contractor must reference several different drawings to understand the design intent for a particular building element. Further, different symbols and terminology used by various designers can also lead to confusion and complications (Emmitt and Gorse ). Mourgues and Fischer () argue that communication of project information to the workface is ineffective and can negatively impact quality, safety, and productivity. Studying the understanding and communication of design throughout all project participants can greatly improve project outcomes by increasing project morale, improving productivity, and reducing rework.
Physical models and additive manufacturing
Physical scale models have had several uses in the construction industry, for tasks such as sequencing of concrete and steel or for planning rigging, reinforcing steel, or excavation operations. Oglesby et al. () surveyed managers who concluded that physical models were a useful tool for planning and communications, and that modeling pays for itself easily. However, no direct benefit was quantified. Physical models, as a supportive piece of information delivery, can be a useful information tool. A study estimates that labor costs can be reduced by 25% minimizing productivity losses on indirect work through a detailed execution planning strategy (O'Brien et al. ). The use of physical models in construction was prevalent historically, but their current use is diminished (Emmitt and Gorse ). The development of 3D CAD technologies is cited as one of the main causes behind the fading use of physical scale models (Emmitt and Gorse ).
3D printing technologies have developed and advanced to the point where 3D CAD models can be effectively printed (Breen et al. ; and Simondetti ). These modeling techniques are defined to be “the process of joining materials to make objects from 3D model data, usually layer upon layer” (Buswell et al. ; and Lim et al. ). Having the ability to create physical models through a printing process presents an opportunity to support traditional information delivery with an accurate, scaled representation of a three dimensional space. There are many models of 3D printers with similar technologies. The printers work by essentially building up an object with individual thin layers of a material, such as plastics or starch. ABS (acrylonitrile butadiene styrene) plastics or PLA (polylactic acid) plastics are two of the more common plastic outputs. A 3D printed model was selected to represent a physical model to test its applicability as a format for information display. Ultimately, the means to achieve the end result of a physical model are not relevant to the outcomes of the study, as the primary objective was to test a physical model as opposed to blueprints or a CAD model.
A critical cognitive component to the design of information delivery is in the mental workload requirements. Assuming that everyone has a fixed maximum cognitive capacity, mental workload is the amount of mental resource required compared to the total resources available to that person (Carswell et al. ). An effective method of information delivery should reduce the mental workload requirements while allowing the necessary information to be conveyed. Typically, this involves reducing the amount of time the user must retain the information in their working memory and reduce the irrelevant, distracting mental operations that may occur. Ultimately, a desirable level of mental workload leads to improvements in design communication as information is able to be retained.
Research in cognitive design has identified three classes of mental workload metrics used to evaluate the outcome of the study. The classes are physiologic, secondary task, and subjective measures (Carswell et al. ). Physiologic measures use indirect measures of mental workload by studying ocular and cardiac responses. These physiologic responses have a relationship with cognitive activity in the brain. Increased cognitive activity has been found to result in small increases in pupil dilation, slowing blinking patterns, more consistent heart rates, and heightened heart rates (Carswell et al. ). Secondary task measures look to identify the remainder of the mental workload, which is not occupied by performing the desired task. These secondary tasks are developed for certain applications such as aviation and high-demand environments. For this study, objective and subjective measures are used as they are readily available, universally accepted, nonintrusive, and the simplest to administer.
One of the most widely used standardized subjective measures of mental workload is the National Aeronautics and Space Administration Task Load Index (NASA-TLX) (Hart and Staveland ). This tool has been used to measure mental workload in over 1,200 studies since its inception (Hart ). Although its use is widespread across fields (e.g. aviation, transportation, and other display oriented fields), its use within construction research has been limited. Mitropoulos and Memarian () investigated task demands in masonry work using the NASA-TLX to identify factors affecting activity performance and propose strategies to improve performance.
However, there are drawbacks to current subjective measures. The subjects must self-evaluate their performance and their cognitive capacity. Response bias could factor into the results if the subjects are stakeholders in the study (Carswell et al. ). For instance, if conducting this study with a veteran journeyman electrician, he or she may be inclined to prefer the traditional 2D drawing set because of their familiarity with the medium.
This research focuses on spatial information format as an initial mechanism for improving understanding and communication of design and evaluates three different formats using several measures. The ability to evaluate cognitive abilities of practitioners in using various information delivery formats requires defined performance metrics. In a discussion of construction communication deliverables, Emmitt and Gorse () suggest that information formats must yield quick, simple, and easily interpretable results. Using those guidelines along with the cognitive principles and measures previously discussed, a series of evaluations have been created for assessment.
Age range (Mean/Median)
Number of engineering course work hours in post-secondary education
Years of experience (Mean/Median)
Position titles (Number,%)
Carpenter foreman (11, 42%)
Laborer foreman (5, 19%)
Electrical foreman (2, 8%)
Mechanical foreman (2, 8%)
Project fngineer (4, 16%)
Design fngineer (2, 8%)
Five-minute rating analyses have been performed on construction field projects to measure the effectiveness of a construction crew and identify sources of delay, travel, and other non-effective work activities (Oglesby et al. ). For this experiment, a five-minute rating yielded the percent of the task time that was spent on direct or effective work and on non-effective work or rework. The percentage can be applied to the overall time to completion to give the amount of time spent on each activity category. The data yields effective work percentages of each information delivery format. To conduct a five-minute rating, a time sheet was prepared and divided into subsets of time and then columns for notation of the activity classification. The classification categories are direct work, indirect work, rework, and delay due to rework. Direct work is defined as any physical building of the model towards the final product. Indirect work is defined as any activities performed towards the end result that is not physically building the model. This includes time getting familiar with the building elements, and manipulating and processing the information delivery format. Rework includes any disassembling or reassembling of a previously built portion of the model. Finally, delay due to rework includes time spent reprocessing the information delivery medium after rework occurs.
To measure the cognitive performance of the subjects, the National Aeronautics and Space Administration Raw Task Load Index (NASA-rTLX) was utilized to measure mental workload. Mental workload is a measure of the amount of mental resources required to complete a task compared to the total amount of mental resources available to that individual (Carswell et al. ). Often, the desired workload imposed by a task has a balance. Too much mental workload and the user may not have the capacity to maintain a proper level of performance. Too little mental workload and the user may not have the focus and diligence required to complete the task appropriately (Hart and Staveland ; and Mitropoulos and Memarian ).
NASA-rTLX factors and descriptions ( Hart and Staveland, )
How much mental and perceptual activity was required (e.g., thinking, deciding, calculating, remembering, looking, searching, etc.)? Was the mission easy or demanding, simple or complex, exacting or forgiving?
How much physical activity was required (e.g., pushing, pulling, turning, controlling, activating, etc.)? Was the mission easy or demanding, slow or brisk, slack or strenuous, restful or laborious?
How much time pressure did you feel due to the rate or pace at which the mission occurred? Was the pace slow and leisurely or rapid and frantic?
How successful do you think you were in accomplishing the goals of the mission? How satisfied were you with your performance in accomplishing these goals?
How hard did you have to work (mentally and physically) to accomplish your level of performance?
How discouraged, stressed, irritated, and annoyed versus gratified, relaxed, content, and complacent did you feel during your mission?
Statistical analysis procedure
A key statistical measure used in the study's experiments is the analysis of variance (ANOVA) procedure. ANOVA models seek to test whether there is a difference between means of several populations (Dielman ; Fellows and Liu ). The often performed procedure estimates statistically significant differences between the means through an F value, while also measuring the amount of variation in the dependent variable that is explained by the independent variables (η2) (Dielman ; and Fellows and Liu ).
For this study, the three populations tested are individuals completing the experiment using the two dimensional drawing set, individuals completing the experiment using the three dimensional computer model, and individuals completing the experiment using the three dimensional printed, physical model.
To quantify these statistically significant differences, there are several post hoc tests available to compare multiple means. The original post hoc test was Fisher's Least Significant Difference (LSD) test. This test compared multiple means through a series of t-tests. However, no adjustment was made to the error rate for the comparisons. In the assumptions of a t-test, the sampling distribution was intended for only one test. When multiple comparisons are made, the true alpha value for significance is lower than 0.05, which is the value assumed in the LSD test (Dielman ). Another, more reasonable post hoc test is the Bonferroni method. Bonferroni uses t tests to perform pairwise comparisons but sets the critical alpha value as the experiment-wise error rate divided by the total number of tests. This corrects for the effect that multiple tests has on the tested t value (Dielman ).
Results and discussion
ANOVA results: model type by dependent variables, all subjects
Time to completion (secs)
NASA composite workload (1-100 scale)
Direct work rate (%)
Rework rate (%)
The physical model had preferred outcomes in time to complete the experiment, composite mental workload, and direct work rate. The 2D drawing set resulted in the lowest rework rate percentage. Of significance, individuals spent more time interpreting the information from the 3D computer interface, whereas the physical model required little processing time. As with all information processing, the work task will demand the optimal display format.
Preferences for engineering and construction tasks
2D versus 3D display comparisons
Tasks where 2D displays are advantageous
Tasks where 3D displays are advantageous
Projective ambiguity concern
You are a structural steel subcontractor and need to plan and present an erection sequence, which information delivery format (s) would you use to complete the task (2D, 3D Interface, Physical Model)?
If you are calculating the necessary cubic yards of concrete for an upcoming slab pour, which information delivery format (s) would you use to complete the task (2D, 3D Interface, Physical Model)?
If you are a mechanical, electrical, or plumbing (MEP) engineer and need to design piping runs with sufficient access space, which information delivery format (s) would you use to complete the task (2D, 3D Interface, Physical Model)?
If you are estimating the quantity of earthwork that will have to be cut and/or filled on a project, which information delivery format (s) would you use to complete the task (2D, 3D Interface, Physical Model)?
In a construction setting, a structural steel erection plan requires an understanding of relative positioning, as it involves coordination of the construction of steel shapes in two directions or dimensions. Therefore an ideal information format choice would be the 2D drawings. Calculating the required yardage of concrete for a future placement event requires an understanding of the shape and the ability to measure distances. Shape understanding presents well in three dimensions, which would point towards the 3D computer model or the physical model. Being that distances are represented and automatically calculated in the computer software, the 3D computer model provides the best representation. MEP runs are typically associated with having sufficient access and coordination between the trades to fit the pipes in the allowable space provided. This requires depth cues and shape understanding without projective ambiguity. The depth cues and shape understand lends itself towards a 3D model, while projective ambiguity concerns lead the user towards a 2D representation. However, a physical model provides the necessary depth cues and shape understanding in a proper and efficient 3D representation. Finally, estimating the quantity of earthwork for cut and fill requires project information and layout understanding of the terrain. Similar to the concrete placement scenario, a 3D computer has the necessary display, information, and calculating tools to complete the task.
For the steel erection sequence plan, subjects preferred the 3D computer model 58% of the time, 2D drawings 23%, and a physical model 19%. Literature suggests the 2D drawings would be preferred as it gives a proper viewing of relative positioning of the steel members. A 3D computer model would distort distances due to projective ambiguity and does not provide addition information that would be desirable. In addition, the practitioners did not perform a simple steel erection sequence during the task completion. It would be a reasonable assumption that a more complex project with more moving parts would prove even more difficult.
When calculating concrete quantities for a slab placement, 62% of practitioners preferred using 2D drawings compared 38% preferring a 3D computer model and 0% for a physical model. This task requires shape understanding and understanding of necessary dimensional properties, which makes a 3D computer model a superior choice. Given this information, subjects likely prefer the 2D drawings due to their limited experiences with CAD technologies. In the current CAD software packages, a concrete slab element can be clicked on and exact quantities will immediately be presented. Without this knowledge and experience, practitioners revert to their familiarity with quantity takeoffs from two dimensional drawings.
With the need for depth cues, shape understanding, and avoidance of projective ambiguity, coordinating the locations of mechanical, electrical, and plumbing pipes is a demanding task. On one hand, depth cues and shape understanding require a 3D display, while a standard 3D display presents issues of projective ambiguity. The issue is averted in a physical model where subjects benefit from depth cues and shape understanding of a 3D display and avoiding projective ambiguity from a true three dimensional, haptic output. However, 77% of practitioners preferred a 3D computer model, while 15% and 8% chose 2D drawings and a physical model respectively.
Calculating cut and fill earthwork quantities requires a knowledge of the terrain and layout, and ideally, the ability to quickly calculate volumes. 3D CAD software packages are readily equipped with this capability and provide a 3D display that is optimal to complete the task. 58% of practitioners appropriately identify the 3D computer model as the information format of choice for this operation, while 42% would use the standard 2D drawing set and 0% would reference a physical model.
The current work tested subjects using various formats of information delivery (2D, 3D interface, and a physical model) to complete a building task and evaluated performance through cognitive and productivity measures. This paper's primary contribution to the body of knowledge is identification of how different mediums of engineering information influences the performance of construction task execution. By conducting a cognitive task experiment, the performance and preferences of subjects with a 3D computer interface and a physical, haptic model was evaluated.
The cognitive workload mean scores were lower with the physical model than the two dimensional drawings and three dimensional computer model average composite workload. These findings suggest that 3D printed models present a promising alternative as at lease a supplemental form of spatial information to the traditional forms of information delivery. There are forms of engineering information, such as dimensions and material specifications, that are impractical to be imposed on a 3D printed model with current printing capabilities. While a 3D computer model may be a more convenient method to representing 3D design due to its current prevalence in industry, it does not appear to be an effective method for spatial designs. This can negate the benefits of the third dimension, since space is the critical addition to the information presentation.
For construction practitioners, the current work implies that physical models present a potential alternative for spatial representation than a 3D computer model and even 2D drawings for spatial tasks. The physical models have higher direct work rates and lower composite workload scores than a 3D computer model and 2D drawings. The task dependent nature of information displays, along with the performance of subjects in the experiment suggest specific situations control the advantages and disadvantages of various mediums. For example, 2D drawings can be useful in tasks involving layouts and relative positioning of objects. A 3D computer model can be useful for dimensioning 3D elements, repetitive calculations, and shape properties due to the intelligence contained within the model. Physical models provide an advantage for tasks that require visualization of spatial elements, coordination of space, and a scaled understanding of depth such as critical lift planning, structure steel sequencing, and planning staging areas for material. Limitations of the current work include a limited sample size and the limited applicability of the model building task to an actual construction task. With a larger sample size and replication of actual construction tasks, the implications for construction practitioners become more impactful. Future work in this area includes further analysis of mental workload in construction professionals to begin the process of understanding the cognitive work demands associated with construction tasks. The outcomes of such studies can identify components of tasks that are mentally demanding and inhibit understanding and execution of the task efficiently. Furthermore, there is a need to examine the use of 3D printed models for more complex designs that can be physically assembled and dissembled by craft workers and other end users and the influence this would have on construction performance. This in essence would replicate the function that traditional modeler built physical models provided on jobsites decades ago. In addition, the current cost to 3D print a project model is substantial, and the print sizes can be relatively small. A cost-benefit analysis on the use of a 3D printed model as a means of information presentation is needed.
- Berlo DK: The Process of Communication: An Introduction to Theory and Practice. The University of Michigan, New York: Holt, Rinehart and Winston; 1960.Google Scholar
- Borcherding S, Samelson : Improving motivation and productivity on large projects. Journal of the Construction Division 1980, 106: No. CO1, 77.Google Scholar
- Boyer BS, Wickens CD: 3D weather displays for aircraft cockpits. Aviation Research Laboratory, Savoy, IL; 1994.Google Scholar
- Breen J, Nottrot R, Stellingwerff M: Tangible virtuality - perceptions of computer-aided and physical modelling. Journal of Automation in Construction, Elsevier 2003, 12: 649–653. 10.1016/S0926-5805(03)00053-0View ArticleGoogle Scholar
- Bureau of Economic Analysis. (2013). National Income and Product Accounts Tables. U.S. Department of Commerce. (July 16th, 2013)., [http://www.bea.gov/iTable/iTable.cfm?ReqID=9&step=1#reqid=9&step=3&isuri=1&903=317]
- Bureau of Labor Statistics. (2012). Overview of BLS Productivity Statistics. U.S. Department of Labor. (April 21st, 2013)., [http://www.bls.gov/bls/productivity.htm]
- Bureau of Labor Statistics. (2013). News Release: Multifactor Productivity Trends for Detailed Industries, 2011 (USDL-13-1941). U.S. Department of Labor. ., [http://www.bls.gov/news.release/pdf/prin3.pdf]
- Buswell RA, Soar RC, Gibb AGF, Thorpe A: Freeform construction: mega-scale rapid manufacturing for construction. Journal of Automation in Construction, Elsevier 2007, 16: 224–231. 10.1016/j.autcon.2006.05.002View ArticleGoogle Scholar
- Byers JC, Bittner AC, Hill SG: “Traditional and raw task load index (TLX) correlations: Are paired comparisons necessary?”. In Advances in Industrial Ergonomics and Safety. Edited by: Mital A. Taylor and Francis, London; 1989:481–485.Google Scholar
- Carswell CM, Clarke D, Seales WB: Assessing mental workload during laparoscopic surgery. Surgical Innovation 2005, 12(1):80–90. 10.1177/155335060501200112View ArticleGoogle Scholar
- Cockburn A, McKenzie B: Evaluating the Effectiveness of Spatial Memory in 2D and 3D Physical and Virtual Environments. In CHI 2002. ACM, New York, Minneapolis, MN; 2002.Google Scholar
- Cooper, K, & Lee, G. (2009). “Managing the Dynamics of Projects and Changes at Fluor.” Conference of the System Dynamics Society. In Proceedings from the 27th International Conference of the System Dynamics Society (pp. 1-27). Albuquerque, New Mexico.Google Scholar
- Dadi GB, Goodrum PM, Taylor TRB, Carswell CM: Cognitive workload demands using 2D and 3D spatial engineering information formats. Journal of Construction Engineering and Management 2014, 140(5):04014001. 10.1061/(ASCE)CO.1943-7862.0000827View ArticleGoogle Scholar
- Dai, J, Goodrum, PM, Maloney, WF, & Sayers, C. (2005). Analysis of Focus Group Data Regarding Construction Craft Workers' Perspective of the Factors Affecting Their Productivity. In Proceedings from the 2005 Construction Research Congress (pp. 1-10). Published by the American Society of Civil Engineers.Google Scholar
- Dai J, Goodrum PM, Maloney WF: Construction craft workers' perceptions of the factors affecting their productivity. Journal of Construction Engineering and Management 2009, 135(3):217–226. 10.1061/(ASCE)0733-9364(2009)135:3(217)View ArticleGoogle Scholar
- Dai J, Goodrum PM, Maloney WF, Srinivasan C: Latent structures of the factors affecting construction labor productivity. Journal of Construction Engineering and Management 2009, 135(5):397–406. 10.1061/(ASCE)0733-9364(2009)135:5(397)View ArticleGoogle Scholar
- Dielman TE: Applied Regression Analysis: a Second Course in Business and Economic Statistics 4th ed. South-Western Cengage Learning, Mason, OH; 2005.Google Scholar
- Eckert C, Boujut JF: The role of objects in design co-operation: communication through physical or virtual objects. Computer Supported Cooperative Work 2003, 12: 145–151. 10.1023/A:1023954726209View ArticleGoogle Scholar
- Emmitt S, Gorse C: Construction Communication. Blackwell Publishing, Oxford; 2003.Google Scholar
- Fellows R, Liu A: Research Methods for Construction. Wiley-Blackwell Publishing, Malden, MA; 2008.Google Scholar
- Goodrum PM, Dai (CII) J: Work Force View of Construction Productivity. The Construction Industry Institute, University of Texas at Austin; 2006.Google Scholar
- Goodrum PM, Haas CT, Glover R: The divergence in aggregate and activity estimates of U.S. construction productivity. Journal of Construction Management and Economics E & F N Spon 2002, 20(5):415–423. 10.1080/01446190210145868View ArticleGoogle Scholar
- Gould F, Joyce N: Construction Project Management. Prentice Hall, New Jersey; 2013.Google Scholar
- Haas CT, Goodrum PM, Caldas CH, Granger R, Zhang D: A Guide to Construction Rework Reduction. The Construction Industry Institute, University of Texas at Austin; 2011.Google Scholar
- Hart SG: “NASA-task load index (NASA-TLX); 20 years later”. In Proceedings of the Human Factors and Ergonomics 50th Annual Meeting. Human Factors and Ergonomics Society, Santa Monica, CA; 2006:904–908.Google Scholar
- Hart SG, Staveland LE: Development of the NASA-TLX (Task Load Index): Results of empirical and theoretical research. 1988.Google Scholar
- Hickox JG, Wickens CD: Effects of elevation angle disparity, complexity and feature type on relating out-of-cockpit field of view to an electronic cartographic map. Journal of Experimental Psychology: Applied. 1999, 5: 284–301.Google Scholar
- Huesman, J, Holland, L, & Langley, T. (2013). December 2012 Construction at $885.0 Billion Annual Rate. U.S. Census Bureau News. U.S. Department of Commerce. . (July 16th, 2013)., [http://www.census.gov/construction/c30/pdf/pr201212.pdf]
- Lim S, Buswell RA, Le TT, Austin SA, Gibb AGF, Thorpe T: Developments in construction-scale additive manufacturing processes. Journal of Automation in Construction, Elsevier 2012, 21: 262–268. 10.1016/j.autcon.2011.06.010View ArticleGoogle Scholar
- Mitropoulos P, Memarian B: Task demands in masonry work: sources, performance implications and management strategies. Journal of Construction Engineering and Management. 2013, 139(5):581–590. 10.1061/(ASCE)CO.1943-7862.0000586View ArticleGoogle Scholar
- Moroney WF, Biers DW, Eggemeier FT, Mitchell JA: A comparison of two scoring procedures with the NASA Task Load Index in a simulated flight tasks. Proceedings from NAECON, Dayton, OH; 1992.View ArticleGoogle Scholar
- Moroney WF, Biers DW, Eggemeier FT: Some measurement and methodological considerations in the application of subjective workload measurement techniques. International Journal of Aviation Psychology. 1995, 5(1):87–106. 10.1207/s15327108ijap0501_6View ArticleGoogle Scholar
- Mourgues, C, & Fischer, M. (2008). A Work Instruction Template for Cast-in-Place Concrete Construction Laborers: Center for Integrated Facility Engineering. Stanford University. Working Paper #109.Google Scholar
- O'Brien B, Leite F, Meeks S: Enhanced Work Packaging: Design through Workface Execution. The Construction Industry Institute, University of Texas at Austin; 2011.Google Scholar
- Oglesby CH, Parker HW, Howell GA: Productivity improvement in construction. McGraw-Hill, New York; 1989.Google Scholar
- Rojas EM: Construction productivity: a practical guide for building and electrical contractors. J. Ross Publications, Fort Lauderdale; 2008.Google Scholar
- Schramm W: The Process and Effects of Communication. University of Illinois Press, Champaign, IL; 1954.Google Scholar
- Shannon CE, Weaver W: The Mathematical Theory of Communication. University of Illinois Press, Champaign, IL; 1949.MATHGoogle Scholar
- Simondetti A: Computer-generated physical modelling in the early stages of the design process. Journal of Automation in Construction, Elsevier 2002, 11: 303–311. 10.1016/S0926-5805(00)00105-9View ArticleGoogle Scholar
- St John M, Cowen MB, Smallman HS, Oonk HM: The use of 2D and 3D displays for shape-understanding versus relative-position tasks. Human Factors 2001, 43(1):79–98. Human Factors and Ergonomics Society Human Factors and Ergonomics Society 10.1518/001872001775992534View ArticleGoogle Scholar
- Taylor TRB, Uddin M, Goodrum PM, McCoy A, Shan Y: Change orders and lessons learned: knowledge from statistical analyses of engineering change orders on Kentucky highway projects. Journal of Construction Engineering and Management, ASCE 2012, 138(12):1360–1369. 10.1061/(ASCE)CO.1943-7862.0000550View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.