Journal of Construction Engineering and Management

Upon receiving the Second Roebling Award, the writer presents the Second Roebling Lecture 1991. The award recognizes individuals who have contributed toward the advancement of construction engineering. After recalling some of the engineering projects he has worked on, and making special mention of an early mentor, the writer reviews some facts of John August Roebling's life.

A significant portion of performance failures are due to human error in the construction stage of a structural engineering project. Statistical studies indicate that the construction of in‐situ reinforced concrete elements is most prone to error, and that most errors are committed by contractors (either site staff or workmen). Results are reported herein of a survey investigating the error rate and error magnitude of contractor construction errors, and the frequency of engineering inspections and their influence on construction errors. The survey recipients were practicing structural engineers. Probabilistic models have been proposed for the distribution of error rates and error magnitudes, and include within them the influence of inspections. It was found that engineering inspections reduced the initial on‐site construction error content by approximately two orders of magnitude. The models provide construction task performance information that can be incorporated into a probabilistic risk analysis of reinforced‐concrete construction.

Cranes are the most important pieces of equipment on many construction sites. While technological advances have been made in crane hardware, the communication system used to coordinate the crane operator's actions with other craftsmen has not changed in decades. Crane operators frequently cannot see the loads they are moving, so they rely on hand signals relayed among craftsmen. This paper introduces the CRANIUM, a video system designed to improve productivity and safety of crane operations by improving communications. A video camera mounted on the crane boom transmits an image to a television monitor in the crane cab. The operator has a real‐time picture of the loads and craftsmen which might otherwise be out of direct line of sight. A full‐scale fully‐operational prototype CRANIUM was designed, fabricated, and extensively tested in the field. Experimental results show that for moderate and high precision lifts, productivity can be increased 16–21%. Crane safety is also improved.

Several probabilistic methods use risk analysis for construction networking. In this study, the writers compare and evaluate five nondeterministic methods: (1) Program evaluation review technique (PERT); (2) probabilistic network evaluation technique (PNET); (3) narrow reliability bounds (NRB), (4) Monte Carlo simulation (MCS); and (5) simplified Monte Carlo simulation (SMCS). To compare the five methods, the writers solved 31 networks using each of the methods. The results obtained by each method were compared and evaluated based on the survival function and computer time. The writers found that PERT is the simplest method and yields the most optimistic results, while MCS and SMCS produce the most conservative result. The gap between the survival function obtained with PERT and that obtained with the two simulation methods (MCS and SMCS) is wider for positively skewed distributions of network activities.

One of the difficulties in probabilistic estimating is accounting for the existing correlations among cost components modeled as random variables. Even if the estimator is aware of the existence of correlations among random variables, calculating accurate values of correlation coefficients is not feasible most of the time. A methodology for generating correlated random numbers in a Monte Carlo simulation for construction cost estimating is reviewed. A methodology is then suggested that simplifies the process of incorporating the effect of correlation coefficients in probabilistic estimating. This methodology consists of assigning subjective measures of correlation between variables. Also, a method is suggested for adjusting the covariance matrix in which correlation estimates are not accurate. Procedures presented are further explained by two examples using actual construction cost data.

Geographic Information Systems (GIS) are one of the fastest growing computer‐based technologies of the 1990s, yet, their full potential in the construction industry has not been realized. This technology provides adequate capabilities for solving problems that involve the integration and analysis of large volumes of spatial and descriptive data from a variety of sources. This technology supports the interaction of multiple participants such that they can approach problems in a more comprehensive and systematic way. The primary objectives of this paper are to describe GIS technology and explore potential construction applications that can benefit from the implementation of this technology. An example application of this technology to contractor prequalification is provided. One benefit expected from the application of GIS to contractor prequalification is the creation of a comprehensive data base. This data base can provide a wide range of construction users with a mechanism for rapid retrieval and manipulation capability to satisfy their need of spatial and descriptive information required in the process. Practical considerations regarding GIS application to the construction industry are also addressed. Finally, several research areas worthy of further investigation are noted.

Exception reporting, hardly a new concept, is too often still largely that—a concept. Modern project‐control information systems should generate an assortment of useful exception reports. As decision support systems, they must provide all levels of management with current information about various aspects of project performance in a timely and meaningful manner. Exception reports can enhance management's productivity by focusing on the most critical subset of performance information. This paper presents a systematic approach to exception reporting for construction labor cost and related performance aspects. Several exception reports are used, individually and in combination, to provide structured and guided exception‐reporting and variance‐analysis capabilities. These reports, and their underlying concepts, are demonstrated using a computerized prototype. The purpose of such exception‐reporting capability is to enable managers to identify critical control points, prioritize their limited time and improve their effectiveness. Flexibility, versatility, user‐defined criteria, and multiple reporting perspectives are emphasized.

A study was conducted to examine the problems created by the mismatch between supply and demand for construction craftworkers. The paper identifies that the supply and demand varies greatly by geographic region and should be analyzed by region. This paper presents the factors and a model of the influence of these factors on the career‐choice decisions for the construction industry. The paper reviews the public perceptions of the construction industry and the demographics of the U.S. construction industry. The factors that affect decision making at the total work‐force level leading or deterring an individual to or from a construction career include individual perceptions, family perceptions, wages, weather, the industry structure, family relations, overall industry image, and the individual's educational system. The model represents a first attempt to explain the prevailing paradigm under which the construction industry is able to attract new entrants. Several recommendations for future extensions to the current work are made.

The present paper focuses on the decision‐making methodology involved in deciding whether or not to use modular construction techniques in building a petrochemical or power plant. The paper identifies those factors that help construction owners and engineers involved in conceptual project design in their modularization decisions and proposes a formal framework for decision‐making support. A computerized knowledge‐based system assisting in this task is presented and discussed. The system performs feasibility analysis based upon various factors classified into five influencing factor categories—plant location; labor‐related; environmental and organizational; project characteristics; and project risks. The system also performs an economic analysis to determine the impact of modularization on cost and schedule. The performance of the system is validated comparing its recommendations with those of the experts in the construction industry. A statistical hypothesis test, conducted on the recommendations of the system and those of the experts, proves the system's credibility.

The major drawback of the low‐bid method, often used for competitive bidding in the U.S. construction industry, is the possibility of awarding a construction contract to a contractor that submits, either accidentally or deliberately, an unrealistically low bid price. Often, such an occurrence works to the owner's and contractor's detriment by promoting disputes, increased costs, and schedule delays. To address this problem, other countries have adopted the average‐bid method and award the contract to the contractor whose price is closest to the average of all bids submitted. This paper presents a competitive bidding model for the average‐bid method and explores its merits relative to the low‐bid method. The bidding process is analyzed both mathematically and through Monte Carlo simulation. The final results of the average‐bid model, as well as Friedman's low‐bid model, are presented in four nomographs that can be used to analyze a competitive situation without the need for any mathematical or numerical manipulation. A comparison of the two methods reveals that the average‐bid method and its variations have the potential to improve contracting practices both for the owner and the contractor and deserve the industry's increased attention.

This paper describes the common characteristics of marine construction firms, identifies opportunities for and constraints to advanced technology in marine construction, forwards recommendations for advancing marine construction technology, and identifies implications regarding technology in other segments of the construction industry. Although marine construction firms innovate and apply new technologies to win and successfully construct specialized marine projects, many technologies are abandoned after meeting the challenges of the target project. A short‐term view of technology in marine construction due to varying project demands, market forces, and the competitive‐bidding and ‐contracting system is a major barrier to innovation and technological advancement. Marine construction firms can gain competitive advantages based on low cost or distinctive capabilities by developing sustainable technologies to meet the unique challenges inherent in the marine project. A managerial framework, defined as an organizational structure and procedures for developing new technologies, is forwarded to help marine construction firms develop sustainable technologies for competitive advantage. Reforms to the competitive‐bidding and ‐contracting system are recommended for public owners to help facilitate innovation and new technology.

For the most part, existing productivity forecasting models are based on extrapolating from historical data instead of considering the effects of project‐related factors. These factors can change daily and can significantly affect the productivity of a labor‐intensive activity. This paper describes a statistical model developed to forecast the productivity of masonry activities. The model is an additive regression model and is based on data collected from 11 masonry projects. The model was tested by predicting the productivity of the 11 projects, with seven of the 11 being predicted within 10% of the actual productivity. This is noteworthy, given that the projects included a number of different masonry activities and types of facilities. Other analyses of the model indicate that the model is statistically valid and reflects what would be expected. Construction managers could easily use the model to estimate the labor requirements for a project and then to better manage the project as it progresses. Other labor‐intensive activities could be modeled using the same methodology.

This paper describes a proposed system, Multi‐Task Motion Planning (MTMP) for planning and modeling operations of large material‐handling equipment typically found in construction. The system will provide the capability to effectively plan conflicting multiple tasks to be performed concurrently and also provide a mechanism to directly output code to automate the overall process. MTMP aims at integrating structural dynamic behavior of the equipment at the path‐planning stage to resolve conflicts. A geometric/graphical approach is proposed that considers equipment constraints and degrees of freedom. The system is conceived within a three‐dimensional computer‐aided design (CAD) animation and simulation graphics package, which allows modeling of the environment and provides an interactive tool for motion planning. A capability to store motion variables for individual objects will allow for automatically generating code for automation. The system has been envisioned to meet the demand for automation to improve planning and operational safety in the construction industry. However, the underlying concepts can be useful for a variety of material‐handling operations and off‐line programming of robots.