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In the late 1990s, image processing was first applied to steel bridge coating assessment in the United States. Yet, there has not been any robust method that could solve nonuniform illumination problems to date. In this regard, this paper aims to develop an approach that handles nonuniform illumination in rust image recognition. The proposed approach also contributes to rust intensity recognition and process automation. This paper adopts the [a.sup.*][b.sup.*] color configuration (of the [L.sup.*][a.sup.*][b.sup.*] color space) which is shown to be the best coordinate system for rust recognition by the proposed method. Different rust colors are regarded as relating to different degrees of rust intensity. Due to the difficulty of defining light rust colors, the adaptive-network-based fuzzy inference system (ANFIS) is used as the framework for the box-and-ellipse-based ANFIS (BE-ANFIS). Illumination adjustment is used to overcome the nonuniform illumination problem. The proposed BE-ANFIS is trained using 120 rust images of the size of 256 x 256 pixels. The performance of BE-ANFIS is compared to the fuzzy C-means (FCM) method, one of the most popular image recognition methods, and the adaptive ellipse approach (AEA) which is the writers' previous approach. Based on testing 35 rust images, the results show that BE-ANFIS can better recognize rust intensity (i.e., distinguishing light and dark rusts) than FCM and AEA. DOI: 10.1061/(ASCE)CP.1943-5487.0000041 CE Database subject headings: Coating; Image techniques; Fuzzy sets; Bridges, steel; United States. Author keywords: Coating defect recognition; Image processing; Color configuration; Adaptive-network-based fuzzy inference system (ANFIS); Fuzzy C-means.

The experiential learning theory is one of the best-known learning theories in education and has been explored and developed for decades through various studies in higher education. This learning method is expected to provide a significantly better learning environment for engineering subjects, such as construction techniques and design methods and alternatives, which may occur in most of the construction engineering disciplines. Nevertheless, the theory has hardly been used in construction engineering courses. In this study, the authors perform an in-depth review of the learning theory and present a case study, formwork design example, wherein the theory can be explicitly applied in construction engineering education. Research findings reveal students' responses to the learning theory and lessons learned. Correlation analysis is conducted to explore how students' performance in learning can be influenced by this learning theory. DOI: 10.1061/(ASCE)1052-3928(2008)134:2(158) CE Database subject headings: Engineering education; Construction management; Engineering education.

Abstract: The development of digital image recognition techniques has contributed to increased precision in pattern recognition and led to numerous applications in industries. In September 1999, the Indiana Department of [...]

Engineering and design professionals constitute a major driving force for a successful project undertaking. Although the industry has been active in addressing the performance of construction labor and methods to estimate or predict such performance, relatively fewer efforts have been conducted for the engineering profession. In an attempt to fill out this gap, the paper presents a study to utilize neurofuzzy intelligent systems for predicting the engineering performance in a construction project. First, neurofuzzy systems are introduced as integrated schemes of artificial neural networks and fuzzy control systems. The use of these neurofuzzy intelligent systems, particularly fuzzy neural networks, in predicting engineering performance is then demonstrated in the industrial construction sector. The development of the system is based on actual project data that was collected through questionnaire surveys. Statistical variable reduction techniques are further employed to develop linear regression models of the same engineering performance prediction scheme, and results are being compared between both techniques. DOI: 10.1061/(ASCE)0733-9364(2005)131:5(548) CE Database subject headings: Performance evaluation; Predictions; Construction industry; Models; Neural networks; Fuzzy sets.

Engineering and design activities constitute a critical link in a project's life cycle. Thus, an efficient project control system requires an instrument to measure and assess engineering performance among different projects. In conventional industry practices, the measurement of engineering performance is usually tied to the production of design documents during the detailed design phase of the project. Realizing the far-reaching impacts of engineering activities, researchers in the past have investigated other comprehensive measures of engineering performance that address the entire project life cycle. As part of a study for the Construction Industry Institute, this particular research employs a multiple attribute utility function method for the collective assessment of engineering performance in construction projects. The paper begins with a summary of previous studies in the area of engineering performance measurement and assessment. Afterwards. the concept of engineering performance is thoroughly explained. The paper then reviews the background and basic theories of using utility functions in assessing engineering performance, along with an illustrative example of the functionality of this approach. The paper goes on to show the development of the utility function model for the industrial construction sector through expert opinion and real project data. The use of the model is further illustrated in the assessment of total and relative engineering performance. Lastly. conclusions of this study are drawn and potential future work is pointed out. DOI: 10.1061/(ASCE)0733-9364(2005)131:5(558) CE Database subject headings: Performance evaluation; Productivity; Measurement; Construction industry.

Digital image recognition has been broadly utilized in different engineering disciplines; in civil engineering, it has been used experimentally in the inspection of steel bridge coatings and underground sewer systems. The multiresolution pattern classification (MPC) method is an image-recognition technique that extracts features from an image and clusters the image into several featured areas. In this paper, the MPC method will be introduced for steel bridge coating assessment with a detailed description of the theoretical background of MPC. An application combining the MPC method and a double-sampling plan for steel bridge coating assessment will also be presented, followed by the conclusions of this paper. DOI: 10.1061/(ASCE)0887-3801 (2002) 16:4(244) CE Database keywords: Classification; Bridges, steel; Coating; Algorithm.

Life-cycle cost analysis is an essential approach to differentiating alternative rehabilitation strategies for steel bridge paint systems. An economic analysis (EA), which is a deterministic method, and the Markov decision process (MDP), which is a stochastic method, were used to carry out the life-cycle cost analysis. These analyses were applied to data from two state Departments of Transportation. The deterioration curves for steel bridge paint condition rating against age were constructed. Different rehabilitation scenarios were proposed for steel bridge paint. The EA and the MDP were used to analyze and differentiate among the proposed rehabilitation scenarios. The results of the EA were different from those of MDP for the two data sets. MDP favored the 'do nothing' scenario until the end of paint life and then a complete repainting. EA indicated that the scenario 'do spot repairs at state 3 of the paint life' and repeat that until the end of the bridge life was superior. The results were analyzed to determine the reason for the conflict. DOI: 10.1061/(ASCE)0887-3828(2002)16:2(63) CE Database keywords: Stochastic models; Life-cycle cost; Bridges, steel; Painting; Cost analysis.

Life-cycle cost analysis was used to compare different alternative strategies for steel bridge paint systems. It was also used as a tool for steel bridge paint rehabilitation planning. The existing paint systems are lead-based and zinc-vinyl, while the new system is an inorganic/organic zinc, epoxy, and urethane paint system (three-coat). Economic analysis using present value (PV) and equivalent uniform annual cost (EUAC) was applied to compare several steel bridge paint system alternatives. The PV and EUAC were also used to compare different rehabilitation scenarios within the same alternative. Life-cycle cost analysis computations indicate that the three-coat paint system was better than others. Researchers concluded that the best scenario for three-cost system rehabilitation was doing spot repairs every 15 years of paint life. A maintenance plan based on life-cycle cost analysis also favored the 'spot repairs every 15 years' scenario. A sensitivity analysis was also conducted to account for uncertainty in the cost, conditions, and subjective data. DOI: 10.1061/(ASCE)0887-3828(2002) 16:2(55) CE Database keywords: Life-cycle cost; Bridges, steel; Protective coating; Painting; Bridge maintenance.

A performance function is the relationship between the bridge paint condition rating and time. This function reflects the level of service of the bridge paint. The performance functions of steel bridge paint for state structures maintained by the Indiana Department of Transportation (INDOT) were developed using both regression analysis and Markov chains. The regression and Markov models were used to predict the condition rating of a bridge's paint at a given age. The probabilistic model that was developed with Markov chains was used to reflect the stochastic nature of bridge paint conditions. These deterministic and stochastic methods have been applied to the existing and the new bridges' paint systems in INDOT structures. The existing paint systems are lead-based and zinc-vinyl. On the other hand, the new paint system is three-coat. The results indicate that both models are very efficient in representing the deterioration model for bridge paint systems based on historical data. DOI: 10.1061/(ASCE)0887-3828(2002) 16:2(46) CE Database keywords: Bridges, steel; Performance; Protective coating; Painting; Markov chains; Indiana.

The bridge deck expansion joint is an important element in the functioning of bridge structures. When joints fail to function properly, they can create problems out of proportion to their size. Selection of a good joint for use can create fewer bridge maintenance problems. The purpose of the study was to evaluate the performance of several types of joints currently in use on Indiana highway bridges. The types of joints investigated are compression seal, strip seal, integral abutment, poured silicone, and polymer modified asphalt. The research was accomplished through questionnaire surveys, analysis of Indiana Department of Transportation roadway management data, and expert interviews. The questionnaire survey identified the problems and their causes and the merits and potential improvements of each type of joint. The analysis of Indiana roadway management data ranked the performance of different types of joints based on the deterioration rates estimated by the regression coefficients. The expert interviews investigated the practices of Indiana and its surrounding states regarding the selection and maintenance of joints. Based on the research results, several suggestions were proposed to ensure the longer service life of expansion joints. DOI: 10.1061/(ASCE)0887-3828(2002)16:1(3) CE Database keywords: Bridge decks; Expansion joints; Regressional analysis; Performance evaluation.

The build-operate-transfer (BOT) approach for project delivery, where the private sector has to finance, design, build, operate, and maintain the facility and then transfer it to the government after a specified concession period, is now gaining widespread popularity in developing countries. Compared with conventional project delivery methods, BOT sponsors expose themselves to a high risk, so that special attention must be paid to analyzing and managing risks. The identification, analysis, and allocation of various types of risks are an important aspect for the validation of privately promoted infrastructure projects. The BOT risk model presented in this paper is a prototype evaluation model that provides a logical, reliable, and consistent procedure for assessing the BOT project risk. The proposed model introduced the BOT risk index (F), which relied on the actual performance of eight main BOT risk areas. Two different modeling approaches were used in constructing this index: a new developed and an adapted Dias and Ioannou model. Not only can this index be used for BOT projects' risk evaluation, but also for ranking them to select the lowest risk project as well. DOI: 10.1061/(ASCE)0742-597X(2002) 18:1(7) CE Database keywords: Risk analysis; Prototypes; Project management; Developing countries; Private sector.

Computerized intelligent systems can simulate human expertise as well as analyze and process vast amounts of data instantaneously. This paper presents a hybrid intelligent computerized model for constructed facilities surface quality assessment. The model uses computers to analyze digital images of the areas to be assessed to identify and measure defects. Moreover, neural networks are used to train the system to automate the process and replicate the experts' knowledge in identifying the defects. Most techniques, currently used in construction quality assessment, rely mostly on subjective criteria. The model applies digital image processing and neural network techniques for constructed facilities surface quality assessment to make the process objective, quantitative, consistent, and reliable. Highway steel bridge coating assessment was used to exemplify the genetic model.

In-place test methods are available that can be used as alternatives to testing field-cured specimens to evaluate concrete strength in a structure. However, in-place test results do not represent the compressive strength of concrete directly but are empirically correlated to the compressive strength. Therefore, to use inplace test methods to estimate the strength of concrete, a preestablished relationship is required between the inplace test results and standard cylinder test results. In addition, a statistically based procedure is needed for a reliable estimate of concrete strength based on the strength relationship and in-place test results. This paper describes the statistical framework for developing the strength relationship and provides a step-by-step procedure to evaluate the characteristic compressive strength of concrete from in-place test results. Pullout test data are used to illustrate the use of these computational procedures.

An acceptance-sampling method plays an important role in designing quality-assurance (QA) specifications. It applies statistics to specify the requirements of (1) how many measurements are needed; and (2) how to make an acceptance or rejection decision based on measured data. The key function of the acceptance-sampling method is to guide the decision between accepting or rejecting the quality of the products. The purpose of this paper is to present four acceptance-sampling methods, including (1) variable-single-sampling method; (2) quality-index-sampling method; (3) attribute-double-sampling method; and (4) attribute-proportion-sampling method. In this paper, the theories will be derived and the applications of the four developed quality-acceptance-sampling methods will be described. The advantages and disadvantages of using different sampling methods are compared. The application of the developed quality-acceptance-sampling methods is illustrated using bridge-painting examples.