This paper presents a model that quantifies the causal relations among safety variables (latent variables) and workers' safety behavior (indicator) using statistical data and hypotheses obtained from construction workers and existing literatures, respectively. The safety variables that affect workers' safety behaviors are identified from existing studies and operationalized to measure their causal relations with the workers' behaviors. The model identifies the directions and degrees of the effect of every latent variable on the other latent variables and the indicator. Survey questionnaires were administered to construction workers in South Korea. Exploratory and confirmatory factor analyses, Cronbach's α and structural equation modeling were performed to test the causal hypotheses using SPSS 18.0 and AMOS 18.0. This study provides the theoretical model that predicts construction workers' safety behavior on construction sites using path diagram and analysis.

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Low carbon construction is an important operation management goal because greenhouse gas (GHG) reduc­tion has become a global concern. Major construction resources that contribute GHG, such as equipment and labour, are being targeted to achieve this goal. The GHG emissions produced by the resources vary with their operating conditions. It is commendable to provide a statistical GHG emission estimation method that models the transitory nature of resource states at micro-scale of construction operations. This paper proposes a computational method called Stochastic Carbon Emission Estimation (SCE2) that measures the variability of GHG emissions. It creates construction operation models consisting of atomic work tasks, utilizes hourly equipment fuel consumption and hourly labourer respiratory rates that change according to their operating conditions classified into five categories, and identifies an optimal resource combi­nation by trading off eco-economic performance metrics such as the amount of GHG emissions, operation completion time, operation completion cost, and productivity. The study is of value to researchers because SCE2 fill in a gap to eco-economic operation modelling and analysis tool which considers operating conditions at micro-scale of construction operation having many stochastic work tasks. This study is also relevance to practitioners because it allows project man­agers to achieve eco-economic goals while honouring predefined constraints associated with time and cost.

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Mobile cranes have been used extensively as essential equipment at construction sites. The productivity improvement of the mobile crane affects the overall productivity of the construction project. Hence, various studies have been conducted regarding mobile crane operation planning. However, studies on solving RCP (the repositioning mobile crane problem) are insufficient. This article presents a mobile crane reposition route planning optimization method (RPOS) that minimizes the total operating time of mobile crane. It converts the construction site into a mathematical model, determines feasible locations of the mobile crane, and identifies near-global optimal solution (s) (i.e., the placement point sequences of mobile crane) by implementing genetic algorithm and dijkstra’s algorithm. The study is of value to practitioners because RPOS provides an easy-to-use computerized tool that reduces the lengthy computations relative to data processing and Genetic Algorithms (GAs). Test cases verify the validity of the computational method.

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Existing SCS (space-constrained scheduling) studies fall short of minimizing the effect of the stacking of trades that decline productivity due to an increase in resources within a physically limited work area. This article presents a space-constrained scheduling optimization (i.e., SSO) method for minimizing the stacking of trades. It imports schedule information from the project database, extracts IFC files of construction site area from the BIM model, defines the occupation density function of each activity to track the level of stacking of trades, and identifies the optimal solution (i.e., the optimal set of pairs of execution pattern alternatives and start times of activities) by implementing genetic algorithm (GA) optimization analysis. The study is of value to practitioners because SSO provides an easy-to-use computerized tool that reduces the lengthy computations relative to data processing and GAs. Test cases verify the validity of the computational method.

Existing resource leveling (RL) approaches fall short of analyzing the trade-off relation between the total float consumption and project completion probability in a real-life project RL problem. This article presents a stochastic resource leveling optimization (SOLO) method that minimizes the total float consumption along with maximizing the project completion probability. It initializes the earliest start times of noncritical activities, measures the level of resource fluctuations of each candidate solution, computes the probability of completing a project in a target deadline by executing simulation-based scheduling, and identifies optimal solution(s) (i.e., optimal start times of noncritical activities) by implementing genetic algorithm, thereby identifying an optimal resource-leveled baseline.The study is of value to practitioners because SOLO considers both the amount of total float consumption and project completion probability. This study facilitates experimentation with different computation time-saving options given various constraints (i.e., the residual of project completion probabilities, threshold of release and rehire, ratio of criticality index, and number of critical activities). Test cases verify the validity of the computational method.How to cite this article: Gwak H-S, Lee D-E. Stochastic resource leveling optimization method for trading off float consumption and project completion probability.