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This chapter aims to verify predictors of marital quality in Croatia. As a theoretical starting point, the Huston Socio-ecological model was used. Huston’s so-called “wide-angle and close range” variables were included in to study as predictors of marital quality. A two-level hypothetical model was created consisting of Six groups of predictor variables: Level 1 predictors included Partners’ demographic variables, Partners’ personality, Partners’ value system, Marital processes or dynamics, and Partners’ wellbeing. Level 2 predictors included four Marriage characteristics. Altogether at both levels, 42 variables represented predictors. Marital quality in the marriage was a dependent variable. Eight hundred and eighty-four marital couples from 14 counties in various parts of Croatia and from Zagreb, the country’s capital, were included in the study. Factor analysis, Maximum likelihood with Promax rotation was used to extract factors. Eight factors were extracted: Marital harmony, Distress, Partners’ personality, Negative spillover from work, Traditionalism, Engagement in child care, Participation in decision-making, and Economic hardship. Multilevel analysis using the Mix model in Statistical Package for Social scientist, version 20 was used in data analysis. Predictive on Marital quality in a marriage turned out variables: Marital harmony, Distress, Partners’ personality, Traditionalism, Engagement in child care, and Participation in decision-making as level 1 and Marriage duration (Marriage stages) as a level 2 variable. Huston’s Ecological model proved to be adequate and useful in explaining marital quality.

Ready-mix concrete delivery problem (RMCDP), a specific version of the vehicle routing problem (VRP), is a relevant supply-chain engineering task for construction management with various formulations and solving methods. This problem can range from a simple scenario involving one source, one material and one destination to a more challenging and complex case involving multiple sources, multiple materials and multiple destinations. This paper presents an Internet of Things (IoT)-supported active building information modeling (BIM) system for optimized multi-project ready-mix concrete (RMC) delivery.

The presented system is BIM-based, IoT supported, dynamic and automatic input/output exchange to provide an optimal delivery program for multi-project ready-mix-concrete problem. The input parameters are extracted as real-time map-supported IoT data and transferred to the system via an application programming interface (API) into a mixed-integer linear programming (MILP) optimization model developed to perform the optimization. The obtained optimization results are further integrated into BIM by conventional project management tools. To demonstrate the features of the suggested system, an RMCDP example was applied to solve that included four building sites, seven eligible concrete plants and three necessary RMC mixtures.

The system provides the optimum delivery schedule for multiple RMCs to multiple construction sites, as well as the optimum RMC quantities to be delivered, the quantities from each concrete plant that must be supplied, the best delivery routes, the optimum execution times for each construction site, and the total minimal costs, while also assuring the dynamic transfer of the optimized results back into the portfolio of multiple BIM projects. The system can generate as many solutions as needed by updating the real-time input parameters in terms of change of the routes, unit prices and availability of concrete plants.

The suggested system allows dynamic adjustments during the optimization process, andis adaptable to changes in input data also considering the real-time input data. The system is based on spreadsheets, which are widely used and common tool that most stakeholders already utilize daily, while also providing the possibility to apply a more specialized tool. Based on this, the RMCDP can be solved using both conventional and advanced optimization software, enabling the system to handle even large-scale tasks as necessary.