This paper presents results obtained for the vibratory compaction process of road structures, in which the natural soil is used for the foundation infrastructure. The experiments and the optimization of the compaction process were carried out on five road lanes in Transilvania, Romania. A self-propelled single-drum roller compactor, BOMAG BW 213 S-5, was used for the compaction, layer by layer, with six successive passes over each layer. For each layer, the initial degree of compaction was measured, and after the fifth pass, it achieved the value prescribed in the road construction project. After each pass over the same layer, its settlement increased due to the plastic deformation and the soil’s rigidity receiving discrete higher values. This is how five different discrete values for rigidity were obtained. Modeling the compaction process is carried out using the Kelvin–Voigt model, with discrete variable experimental values for soil rigidity and assumed constant viscous damping values. Based on the two-degree-of-freedom linear elastic model, graphs were plotted for vibration amplitude variation and for the force transmitted to the soil when the excitation pulsation varies continuously and the soil rigidity varies discretely. There is a relationship between the initial and final degree of compaction values in the ratio that was proven to be dependent on the ratio of amplitude values corresponding to the final and initial roller passes cycle. The result is a useful relationship for the “in-situ” estimation of the compaction process effect. The novelty of this research is that it demonstrates the change in soil rigidity values after each pass of the vibratory roller and, thus, the increase of its settlement (plastic deformation) and the “slipping” for the amplitude resonance peak by discrete increasing values. Calibration of the resonance vibrations regime in accordance with the degree of compaction determined by geotechnical methods for “in-situ” sample prelevation stands as a fast and efficient method for the evaluation of the final degree of compaction value. This is, implicitly, the method for estimating the number of vibratory roller passes in the road construction project. In conclusion, the novelty of the research consists in the fact that, through using the resonance response of the vibratory roller, a correlation was made with the degree of compaction achieved after each pass.

The purpose of this research is to design an energy-efficient off-grid vending machine that has a refrigeration system powered by an environmentally friendly refrigerant. The paper presents details from the equipment’s design phase in terms of the resistance structure, photovoltaic panels area dimensioning, but also in terms of the necessary cooling load to be provided by the refrigeration system. The refrigerant chosen was R290 following a comparative analysis, which showed that it has an efficiency 6.6% higher than R134a. During the monitoring periods, the outdoors temperature varied between −4–28 °C, the relative humidity between 22–100% and yet only a slight indoor temperature variation of ±1.5 °C compared to the set value was recorded. The battery module managed to store enough energy from the photovoltaic panels to keep the vending machine functional through three consecutive days with adverse environmental conditions, as evidenced by the low variation of indoor temperature

The management of construction equipment on a construction site is particularly important for increasing productivity on construction sites. High productivity has financial implications for the economics of construction projects and leads to reduced energy consumption. Reducing energy consumption in construction machinery has an impact on reducing greenhouse gase emissions. Monitoring construction equipment and optimizing movement trajectories is a challenge on a construction site and is possible in real ti me with drone photogrammetry technology. Using this technology, we can track the construction machines and the progress of the works in real time, and later, by using AI technology, the management of equipment on construction sites can be improved in real time.

This article presents novel research on the utilization of a neural-network-based time control system for microwave oven heating of food items within a solar-powered vending machine. The research aims to explore the control of heating time for various food products, considering multiple variables. The neural network controller is calibrated through extensive experimentation, allowing it to accurately predict optimal heating times based on input parameters such as food type, weight, initial temperature, water content, and desired doneness level. The results demonstrate that the neural-network-controlled microwave oven achieves precise and desirable heating durations, mitigating the risk of overheating and ensuring superior food quality and taste. Moreover, the solar-powered vending machine showcases a commitment to sustainable energy sources, effectively reducing dependence on non-renewable energy and minimizing greenhouse gas emissions. To maintain food quality and freshness, a food refrigeration unit is integrated into the vending machine, employing load-balancing technology to control the refrigeration chamber’s temperature effectively. Energy efficiency is prioritized in both the refrigeration unit and the microwave oven through intelligent algorithms and system optimization. The combination of a neural-network-controlled microwave oven, a solar-powered vending machine, and a food refrigeration unit introduces a novel and sustainable approach to food preparation and energy management.

Escalators are an important piece of equipment used for vertical transportation of people in buildings. In today’s context, when the reduction of greenhouse gas emissions is a global challenge, the energetic efficiency of escalators in operation becomes a priority. The research aims to identify possibilities to reduce the energy consumption of this category of equipment. In this respect, we have undertaken a series of tests to establish a correlation between passenger traffic and electrical energy consumption. The tests were carried out for an existing escalator and represent the first step in developing solutions to optimize the operation of such equipment. The energetic efficiency of escalators in operation needs to be customized for each model and involves finding parameters that can be modelled to reduce electrical energy consumption.