Recycling of waste tire rubber is a significant environmental problem due to the huge number of waste tires needing disposal. A promising alternative to methodologies usually used for this aim is the use of tire rubber in the construction industry as artificial aggregate in concrete. This paper investigated the effects on the concrete compressive strength of the partial replacement of natural aggregates with waste tire rubber. Different types and amounts of natural aggregate substitutions were considered, with percentages of rubber ranging from 0% to 100% and including only fine aggregates (F), only coarse aggregates (C), and fine and coarse aggregates simultaneously (C&F). A literature review and new experimental data were considered, presenting about 1,500 tests from dozens of different authors in more than 20 independent studies, included the present one. Based on this large data set, new analytical relationships were developed and proposed to analyze the concrete strength reduction factor (SRF) expected as a consequence of natural aggregate substitution with rubber, and the relationship with the equivalent degree of compaction (EDC) of the mixture was considered.

In this study, support vector machine (SVM) and Gaussian process regression (GPR) models were employed to analyse different rubbercrete compressive strength data collected from the literature. The compressive strength data at 28 days ranged from 4 to 65 MPa in reference to rubbercrete mixtures, where the fine aggregates (sand fraction) were substituted with rubber aggregates in a range from 0% to 100% of the volume. It was observed that the GPR model yielded good results compared to the SVM model in rubbercrete strength prediction. Two strength reduction factor (SRF) equations were developed based on the GPR model results. These SRF equations can be used to estimate the compressive strength reduction in rubbercrete mixtures; the equations are provided. A sensitivity analysis was also performed to evaluate the influence of the w/c ratio on the compressive strength of the rubbercrete mixtures.

The article focuses on the dynamic identification and finite element (FE) modelling of a butterfly-arch stress-ribbon pedestrian bridge in Fuzhou, Fujian, China. The Stochastic Subspace Identification method yields an estimate of the operational modal parameters. A highly synchronous tri-axial wireless sensor network was deployed on the bridge deck to record the structure’s ambient vibration. Eight stable modes in the frequency range 3.59–14.92 Hz were found, associated with prevalent bending and torsional deformations. Four distinct FE models of the bridge with progressive complexity and accuracy were developed to investigate the sensitivity of the modal features to the modelling choices. The FE model characterised by the fittest agreement with the experimental modal parameters was used for the automatic parametric optimisation based on a sensitivity-based algorithm. The stiffness of the springs simulating the soil-structure interaction, the elastic modulus of the concrete deck and the elastic modulus of the tendons were chosen as updating parameters for a total of eight parameters. The effect of non-structural elements (handrails) and prestress on the modal features are also investigated. The final advanced FE model developed can serve as baseline for a long-term monitoring of the bridge during its life-cycle, and also provides some recommendations to practitioners and scholars all over the world for the modelling and analysis of this particular kind of footbridges.

The structural health monitoring (SHM) of large and complex infrastructures as well as laboratory tests of new structures and materials resorts to strain gauge measurements to check mechanical stress. A wireless measurement of the strain gauge response is desirable in many practical applications to avoid the cost and the difficulty of wiring, particularly in large structures requiring several sensors and in complex objects where the measurement points are difficult to access. In this paper, a wireless strain gauge which is a hybrid between an RFID tag and a usual thin-film resistive strain gauge is experimented. Installation and maintenance problems of the wireless sensor networks are overcome allowing a high level of measurement accuracy, comparable to that of wired strain sensors, together with a long measurement distance. A large set of measurements has been performed using reference specimens and readings in order to validate the sensor and to develop a calibration procedure that makes the sensor suitable for a large number of different applications in civil engineering.

Shear walls are commonly adopted as main structural members to resist vertical and lateral forces, thanks to their high load capacity and high lateral stiffness. However, their lateral capacity can be impaired in the presence of openings, which can reduce their lateral load capacity and stiffness. A possible solution is to cast shear walls using steel-fiber-reinforced concrete (SFRC), which effectively improves the deformation capacity of shear walls. However, few studies deal with the performance of such SFRC shear walls in the presence of openings. Moreover, the effect of different axial load ratios (ALR) is still not fully known. To study these essential parameters, a detailed Finite Element model has been implemented in ABAQUS. Having validated its accuracy against experimental tests on four SFRC shear walls, with and without openings, it has been subsequently used in a parametric study to analyze the effects of different ALRs, of different opening configurations, and of different reinforcement ratios. It is shown that door openings have a more detrimental effect on the lateral load capacity than window openings and that higher ALR values switch the prevailing failure mechanism from flexural to shear, thus reducing both ductility and deformation capacity.