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Pavement deterioration models are an important part of any pavement management system. Many of these models suffer from endogeneity bias because of the inclusion of independent variables correlated with unobserved factors, which are captured by the model's error terms. Examples of such endogenous variables include pavement overlay thickness and maintenance and rehabilitation activities, both of which are not randomly chosen but are in fact decision variables selected by pavement engineers based on field conditions. Inclusion of these variables in a pavement deterioration model can result in biased and inconsistent model parameter estimates, leading to incorrect insights. Previous research has shown that continuous endogenous variables, such as pavement overlay thickness, can be corrected using auxiliary models to replace the endogenous variable with an instrumented variable that has lower correlation with the unobserved error term. Discrete endogenous variables, such as the type of maintenance and rehabilitation activities, have been accounted for by modeling the likelihood of each potential outcome and developing individual deterioration models for each of the potential responses. This paper proposes an alternative approach to accommodate discrete endogenous variables-the selectivity correction method-that allows a single model to incorporate the impacts of all discrete choices. This approach is applied to develop a pavement-roughness progression model that incorporates both continuous and discrete endogenous variables using field data from Washington State. The result is a roughness progression model with consistent parameter estimates, which have more realistic values than those obtained in previous studies that used the same data.

Bridge management problems are computationally difficult to solve when considering network connectivity, because all bridges must be analyzed together. Past research has usually adopted simulation-based numerical methods, which are inaccurate and time-consuming. In this paper, an analytical and efficient approach is presented to determine the optimal maintenance, rehabilitation, and replacement (MR&R) plan. The objective is to ensure an adequate level of network reliability at the lowest possible life-cycle maintenance cost. Instead of considering the evolution of network reliability over the whole life cycle, the optimization is formulated with a constraint on the lower bound of network reliability, which reduces the network-level problem to one of optimizing the set of reliability levels for individual bridges. For simple decomposable networks, the network-level optimization can be tackled with standard techniques in polynomial time. For complex networks that are nondecomposable, an algorithm is provided to transform the nonlinear integer programming problem into binary integer linear programming. Finally, the framework is extended to networks with multiple origin-destination pairs. The contribution of this paper is a novel approach to solve the network connectivity problem, which makes it possible to use standard optimization tools (with guaranteed convergence to optimality), as opposed to the heuristic algorithms used in related literature. [ABSTRACT FROM AUTHOR]

Research has separately existed for life-cycle costs and environmental assessment of photovoltaic systems. This study provides a framework for the identification of the optimal set of residential photovoltaic system options in terms of costs and selected air emissions by assessing the life cycle of the product from manufacturing to disposal. Analyzed air emissions include carbon, nitrogen oxides, sulfur oxides, carbon monoxide, and particulate matter. The study focuses on general residential installations within the San Francisco Bay Area of California. Pareto frontiers are determined for costs and emissions, and the best options for suppliers are identified in each case, including sensitivity analysis of the installation planning horizon. Choosing multicrystalline silicon modules over thin film modules has the greatest impact on cost reduction. The electricity mix with which the modules are produced has the greatest impact on the emissions. [ABSTRACT FROM AUTHOR]