• Energy Research

Due to the decreasing availability of virgin materials coupled with an increased awareness of environmental sustainability issues, many researchers have focused their efforts on investigating innovative technological solutions in the civil engineering domain. This paper aims to evaluate the suitability of construction and demolition waste (C and DW) and reclaimed asphalt pavement (RAP) reused within asphalt mixtures (AMs) prepared for the binder layer of road pavements. Both hot and cold mixing methodologies were investigated. The technical assessment was based on the volumetric and mechanical suitability, according to saturated surface dry voids (SSDV) and indirect tensile strength (ITS) tests carried out at 10 °C, respectively. Laboratory findings showed that all the hot AMs matched the desired target SSDV at the design gyrations number at different optimum bitumen content levels, alternatively showing a non-significant variation or a significant increase in ITS compared to conventional hot mix asphalt. Conversely, the cold AMs with cement and emulsion bitumen showed a greater volume of voids and moisture sensitivity, and lower temperature susceptibility compared to hot AMs, reaching, on average, 11% lower ITS when using coarse C and DW aggregates and 43% lower ITS when using filler from C and DW. These volumetric and mechanical properties were modeled by means of support vector machines and categorical boosting (CatBoost) machine learning algorithms. The results proved to be satisfactory, with CatBoost determination coefficients R2 referring to SSDV and ITS equal to 0.8678 and 0.9916, respectively. This allowed for the mechanical performance of these sustainable mixtures to be predicted with high accuracy and implemented within conventional mix design procedures.

An integrated approach based on machine learning and data augmentation techniques has been developed in order to predict the stiffness modulus of the asphalt concrete layer of an airport runway, from data acquired with a heavy weight deflectometer (HWD). The predictive model relies on a shallow neural network (SNN) trained with the results of a backcalculation, by means of a data augmentation method and can produce estimations of the stiffness modulus even at runway points not yet sampled. The Bayesian regularization algorithm was used for training of the feedforward backpropagation SNN, and a k-fold cross-validation procedure was implemented for a fair performance evaluation. The testing phase result concerning the stiffness modulus prediction was characterized by a coefficient of correlation equal to 0.9864 demonstrating that the proposed neural approach is fully reliable for performance evaluation of airfield pavements or any other paved area. Such a performance prediction model can play a crucial role in airport pavement management systems (APMS), allowing the maintenance budget to be optimized.

Improving pedestrian safety at urban intersections requires intelligent systems that should not only understand the actual vehicle–pedestrian (V2P) interaction state but also proactively anticipate the event’s future severity pattern. This paper presents a Gated Recurrent Unit-based system that aims to predict, up to 3 s ahead in time, the severity level of V2P encounters, depending on the current scene representation drawn from on-board radars’ data. A car-driving simulator experiment has been designed to collect sequential mobility features on a cohort of 65 licensed university students who faced different V2P conflicts on a planned urban route. To accurately describe the pedestrian safety condition during the encounter process, a combination of surrogate safety indicators, namely TAdv (Time Advantage) and T2 (Nearness of the Encroachment), are considered for modeling. Due to the nature of these indicators, multiple recurrent neural networks are trained to separately predict T2 continuous values and TAdv categories. Afterwards, their predictions are exploited to label serious conflict interactions. As a comparison, an additional Gated Recurrent Unit (GRU) neural network is developed to directly predict the severity level of inner-city encounters. The latter neural model reaches the best performance on the test set, scoring a recall value of 0.899. Based on selected threshold values, the presented models can be used to label pedestrians near accident events and to enhance existing intelligent driving systems.

Nowadays, budget restrictions for road construction, management, and maintenance require innovative solutions to guarantee the user acceptable service levels respecting environmental requirements. Such goals can be achieved by the re-use of various waste materials at the end of their service life in the pavement structure, therefore avoiding their disposal in landfill. At the same time, significant savings are achieved on natural aggregate by replacing it with such waste materials, improving the economic and environmental sustainability of road constructions. The purpose of this study is to discuss a laboratory investigation about foamed bitumen-stabilized mixtures for road foundation layers, in which the aggregate structure was entirely made up of industrial by-products and civil wastes, namely metallurgical slags such as electric arc furnace (EAF) and ladle furnace (LF) slags, coal fly (CF) ash, bottom ash from municipal solid waste incineration (MSWI), glass waste (GW) and reclaimed asphalt pavement (RAP). Combining these recycled aggregates in different proportions, six foamed bitumen mixtures were produced and investigated in terms of indirect tensile strength, stiffness modulus, and fatigue resistance. The leaching test carried out on the waste materials considered did not show any toxicological issue and the best foamed bitumen mixture’s composition was characterized by 20% of EAF slags, 10% of LF slags, 20% of MSWI ash, 10% of CF ash, 20% of GW, and 20% of RAP. Its mechanical characterization presented a dry indirect tensile strength at 25 °C of 0.62 MPa (well above the Italian technical acceptance limits), a stiffness modulus at 25 °C equal to 6171 MPa, and a number of cycles to failure at 20 °C equal to 6989 for a stress level of 300 kPa.

In this study, the effect of seven industrial waste materials as mineral fillers in asphalt mixtures was investigated. Silica fume (SF), limestone dust (LSD), stone dust (SD), rice husk ash (RHA), fly ash (FA), brick dust (BD), and marble dust (MD) were used to prepare the asphalt mixtures. The obtained experimental results were compared with ordinary Portland cement (OPC), which is used as a conventional mineral filler. The physical, chemical, and morphological assessment of the fillers was performed to evaluate the suitability of industrial waste to replace the OPC. The volumetric, strength, and durability of the modified asphalt mixes were examined to evaluate their performance. The experimental data have been processed through artificial neural networks (ANNs), using k-fold cross-validation as a resampling method and two different activation functions to develop predictive models of the main mechanical and volumetric parameters. In the current research, the two most relevant parameters investigated are the filler type and the filler content, given that they both greatly affect the asphalt concrete mechanical performance. The asphalt mixes have been optimized by means of the Marshall stability analysis, and after that, for each different filler, the optimum asphalt mixtures were investigated by carrying out Indirect tensile strength, moisture susceptibility, and abrasion loss tests. The moisture sensitivity of the modified asphalt mixtures is within the acceptable limit according to the Indian standard. Asphalt mixes modified with the finest mineral fillers exhibited superior stiffness and cracking resistance. Experimental results show higher moisture resistance in calcium-dominant mineral filler-modified asphalt mixtures. Except for mixes prepared with RHA and MD (4% filler content), all the asphalt mixtures considered in this study show MS values higher than 10 kN, as prescribed by Indian regulations. All the values of the void ratio for each asphalt mix have been observed to range between 3–5%, and MQ results were observed between 2 kN/mm–6 kN/mm, which falls within the acceptable range of the Indian specification. Partly due to implementing a data-augmentation strategy based on interpolation, the ANN modeling was very successful, showing a coefficient of correlation averaged over all output variables equal to 0.9967.