Nondestructive evaluation of concrete compression strength by means of Artificial Neural Network (ANN)

The evaluation of structural performance of existing concrete buildings, built according to standards and materials quite different to those available today, requires procedures and methods able to cover lack of data about mechanical material properties and reinforcement detailing. To this end detailed inspections and test on materials are required. As a consequence tests on drilled cores are required; on the other end, it is stated that non-destructive testing (NDT) cannot be used as the only mean to get structural information, but can be used in conjunction with destructive testing (DT) by a representative correlation between DT and NDT. The aim of this study is to verify the accuracy of some formulas of correlation available in literature between measured parameters, i.e. rebound index, ultrasonic pulse velocity and compressive strength (SonReb Method). To this end a relevant number of DT and NDT tests has been performed on many school buildings located in Cesena (Italy). The above relationships have been assessed on site correlating NDT results to strength of core drilled in adjacent locations. Nevertheless, concrete compressive strength assessed by means of NDT methods and evaluated with correlation formulas has the advantage of being able to be implemented and used for future applications in a much more simple way than other methods, even if its accuracy is strictly limited to the analysis of concretes having the same characteristics as those used for their calibration. This limitation warranted a search for a different evaluation method for the non-destructive parameters obtained on site. To this aim, the methodology of neural identification of compressive strength is presented. Artificial Neural Network (ANN) suitable for the specific analysis were chosen taking into account the development presented in the literature in this field. The networks were trained and tested in order to detect a more reliable strength identification methodology.

Sustainable pavements for rural scenarios: laboratory and in-situ characterization of recycled materials and innovative construction solutions

Growing need for infrastructure has led to expanding research on advances in road pavement materials. Finding solutions that are sustainable, environmentally friendly and cost-efficient is a priority. Focusing such efforts on low-traffic and rural roads can contribute with a significant progress in the vital circulatory system of transport for rural and agricultural areas. An important alternative material for pavement construction is recycled aggregates from solid wastes, including waste from civil engineering activities, mainly construction and demolition. A literature review on studies is made; it is performed a planned set of laboratory testing procedures aimed to fully characterize and assess the potential in-situ mechanical performance and chemical impact. Furthermore, monitoring the full-scale response of the selected materials in a real field construction site, including the production, laying and compaction operations. Moreover, a novel single-phase solution for the construction of semi-flexible paving layers to be used as alternative material to common concrete and bituminous layers is experimented and introduced, aiming the production and laying of a single-phase laid material instead of a traditional two phases grouted macadam. Finally, on a parallel research work for farming pavements, the possible use of common geotechnical anti-erosive products for the improvement of soil bearing capacity of paddock areas in cattle husbandries of bio-farms is evaluated. this thesis has clearly demonstrated the feasibility of using the sustainable recycled aggregates for low-traffic rural roads and the pavements of farming and agriculture areas. The pavement layers constructed with recycled aggregates provided satisfying performance under heavy traffic conditions in experimental pavements. This, together with the fact that these aggregates can be available in most areas and in large quantities, provides great impetus towards shifting from traditional materials to more sustainable alternatives. The chemical and environmental stability of these materials proves their soundness to be utilized in farming environments.

Development and chemo-mechanical characterization of eco-compatible extenders containing REOB and recycled tyres’ rubber for the production of greener bituminous binders

The concepts of circular economy and sustainability are the basis of the present experimental research that seeks to reduce the environmental impact of traditional road construction materials. This study mainly focuses on the development and the chemo-mechanical characterization of bitumen extenders containing rubber (R) from end-of-life tyres (ELTs) and re-refined engine oil bottoms (REOBs) for the production of innovative and eco-friendly extended bitumens (i.e. bituminous binders containing 25%wt. of recycled products) and asphalt mixtures. In order to create more sustainable asphalt mixes, also recycled aggregates are used for partial replacement of virgin natural aggregates in the aggregate skeleton. The experimental program encompassed five successive steps: (i) the evaluation of physicochemical properties of R and REOB, (ii) the definition of the optimal extenders by the development of a new protocol and their characterizations, (iii) the realization and investigation of the chemo-rheological responses of the extended bitumens at different boundary conditions, (iv) the assessment of the effectiveness of analytical method to predict the rheological parameters of extended bitumens and, finally, (v) the analysis of the mechanical performances of the corresponding asphalt mixtures. A standard 50/70 penetration grade bitumen was chosen as a reference material and the main constituent of the innovative bituminous products. The results of this study underlined the importance of material characterization. The incorporation of R-REOB extenders strongly affects the chemo-rheological responses of the resulting extended bitumens and asphalt mixtures overall the boundary conditions. While the presence of R and the consequent formation of a polymer network improves the elasticity of the final products, especially at high test temperatures; the addition of REOB, softens the bituminous binders and asphalt mixes increasing their response at low test temperatures. Nonetheless, the use of recycled products increased the susceptibility of bituminous material under damaging conditions, which would need further investigations.

Fiber beam-columns models with flexure-shear interaction for nonlinear analysis of reinforced concrete structures

The aim of this study was to develop a model capable to capture the different contributions which characterize the nonlinear behaviour of reinforced concrete structures. In particular, especially for non slender structures, the contribution to the nonlinear deformation due to bending may be not sufficient to determine the structural response. Two different models characterized by a fibre beam-column element are here proposed. These models can reproduce the flexure-shear interaction in the nonlinear range, with the purpose to improve the analysis in shear-critical structures. The first element discussed is based on flexibility formulation which is associated with the Modified Compression Field Theory as material constitutive law. The other model described in this thesis is based on a three-field variational formulation which is associated with a 3D generalized plastic-damage model as constitutive relationship. The first model proposed in this thesis was developed trying to combine a fibre beamcolumn element based on the flexibility formulation with the MCFT theory as constitutive relationship. The flexibility formulation, in fact, seems to be particularly effective for analysis in the nonlinear field. Just the coupling between the fibre element to model the structure and the shear panel to model the individual fibres allows to describe the nonlinear response associated to flexure and shear, and especially their interaction in the nonlinear field. The model was implemented in an original matlab® computer code, for describing the response of generic structures. The simulations carried out allowed to verify the field of working of the model. Comparisons with available experimental results related to reinforced concrete shears wall were performed in order to validate the model. These results are characterized by the peculiarity of distinguishing the different contributions due to flexure and shear separately. The presented simulations were carried out, in particular, for monotonic loading. The model was tested also through numerical comparisons with other computer programs. Finally it was applied for performing a numerical study on the influence of the nonlinear shear response for non slender reinforced concrete (RC) members. Another approach to the problem has been studied during a period of research at the University of California Berkeley. The beam formulation follows the assumptions of the Timoshenko shear beam theory for the displacement field, and uses a three-field variational formulation in the derivation of the element response. A generalized plasticity model is implemented for structural steel and a 3D plastic-damage model is used for the simulation of concrete. The transverse normal stress is used to satisfy the transverse equilibrium equations of at each control section, this criterion is also used for the condensation of degrees of freedom from the 3D constitutive material to a beam element. In this thesis is presented the beam formulation and the constitutive relationships, different analysis and comparisons are still carrying out between the two model presented.

Vulnerable users' protection with advanced recycling paving materials. Design and characterisation of rubber-based impact-absorbing pavement materials for bike lanes and sidewalks

Our cities are constantly evolving, and the necessity to improve the condition and safety of the urban infrastructures is fundamental. However, on the roads, the specific needs of cyclists and pedestrians are often neglected. The Vulnerable Road Users (VRUs), among whom cyclists and pedestrians are, rarely benefit from the most innovative safety measures. Inspired by playgrounds and aiming to reduce VRUs injuries, the Impact-Absorbing Pavements (IAP) developed as novel sidewalks, and bike lanes surface layers may help decrease injuries, fatalities, and the related societal costs. To achieve this goal, the End-of-Life Tyres (ELTs) crumb rubber (CR) is used as a primary resource, bringing its elastic properties into the surface layer. The thesis is divided into five main chapters. The first concerns the formulation and the definition of a feasible mix. The second explores the mechanical and environmental properties in detail, and the ageing effect is also assessed. The third describes the modelling of the material to simulate accidents and measure the injury reduction, especially on the head. The fourth chapter is reserved for the field trial. The last gives some perspectives on the research and proposes a way to optimize and improve the data and results collected during the doctoral research. It was observed that the specimens made with cold protocol have noticeable performances and reduce the overall carbon footprint impact of this material. The material modelling and the accident simulation proved the performance of the IAP against head injuries, and the field trial confirmed the good results obtained in the laboratory for the cold-made material. Finally, the outcomes of this thesis opened many prospective to the IAP development, such as the use of a plant-based binder or recycled aggregates and gave a positive prospect of an innovative material to the urban road infrastructures.

Exploring the biofilm of Streptococcus agalactiae to identify virulence factors

Streptococcus agalactiae, also known as Group B Streptococcus (GBS) is the primary colonizer of the anogenital mucosa of up to 40% of healthy women and an important cause of invasive neonatal infections worldwide. Among the 10 known capsular serotypes, GBS type III accounts for 30-76% of the cases of neonatal meningitis. Biofilms are dense aggregates of surface-adherent microorganisms embedded in an exopolysaccharide matrix. Centers for Disease Control and Prevention estimate that 65% of human bacterial infections involve biofilms (Post et al., 2004). In recent years, the ability of GBS to form biofilm attracted attention for its possible role in fitness and/or virulence. Here, a new in vitro biofilm formation protocol was developed to guarantee more stringent conditions, to better discriminate between strong-, low- and non- biofilm forming strains and reduce ambiguous data interpretation. This protocol was applied to screen the in vitro biofilm formation ability of more than 350 GBS clinical isolates from pregnant women and neonatal infections belonging to different serotype, in relation to media composition and pH. The results showed the enhancement of GBS biofilm formation in acidic condition and identified a subset of isolates belonging to serotypes III and V that forms strong biofilms in these conditions. Interestingly, the best biofilm formers belonged to the serotype III hypervirulent clone ST-17.It was also found that pH 5.0 induces down-regulation of the capsule but that this reduction is not enough by itself to ensure biofilm formation. Moreover, the ability of proteinase K to strongly inhibit biofilm formation and to disaggregate mature biofilms suggested that proteins play an essential role in promoting GBS biofilm formation and contribute to the biofilm structural stability. Finally, a set of proteins potentially expressed during the GBS in vitro biofilm formation were identified by mass spectrometry.

Evaluation of alpha-synuclein RT-QuIC for an early and differential diagnosis of Lewy body disease

Synucleinopathies are a group of neurodegenerative diseases characterized by tissue deposition of insoluble aggregates of the protein α-synuclein. Currently, the clinical diagnosis of these diseases, including Parkinson’s disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA), is very challenging, especially at an early disease stage, due to the heterogeneous and often non-specific clinical manifestations. Therefore, identifying specific biomarkers to aid the diagnosis and improve the clinical management of patients with these disorders represents a primary goal in the field. Pursuing this aim, we applied the α-Syn Real-Time Quaking-Induced Conversion (RT-QuIC), an ultrasensitive technique able to detect minute amounts of amyloidogenic proteins, to a large cohort of 953 CSF samples from clinically well-characterized (“clinical” group), or neuropathologically verified (“NP” group) patients with parkinsonism or dementia. Of significance, we also studied patients with prodromal synucleinopathies (“prodromal” group), such as pure autonomic failure (PAF) (n = 28), isolated REM sleep behavior disorder (iRBD) (n = 18), and mild cognitive impairment due to probable Lewy body (LB) disease (MCI-LB) (n = 81). Our findings show that α-syn RT-QuIC can accurately detect α-Syn seeding activity across the whole spectrum of LB-related disorders (LBD), exhibiting a mean sensitivity of 95.2% in the “clinical” and “NP” group, while ranging between 89.3% (PAF) and 100% (RBD) in the “prodromal group”. Moreover, we observed 95.1% sensitivity and 96.6% specificity in the distinction between MCI-LB patients and cognitively unimpaired controls, demonstrating the solid diagnostic potential of α-Syn RT-QuIC in the early phase of the disease. Finally, 13.3% of MCI-AD patients also had a positive test, suggesting an underlying LB co-pathology. This work demonstrated that α-Syn RT-QuIC is an efficient assay for accurate and early diagnosis of LBD, which should be implemented for clinical management and recruitment for clinical trials in memory clinics.

Skin as a promising biomatrix for the early detection of misfolded proteins by RT-QuIC in neurodegenerative diseases

Real-Time Quaking-Induced Conversion (RT-QuIC) is an ultrasensitive assay capable of detecting pathological aggregates of misfolded proteins in biospecimens. In recent years, efforts have been made to find a more feasible and convenient biomatrix as an alternative to CSF, and skin biopsy may be a suitable candidate. This project aimed to evaluate the diagnostic performance of skin RT-QuIC in 3 different cohorts of patients: 1. Creutzfeldt-Jakob disease (CJD), 2. Lewy body disease (LBD), and 3. Isolated REM sleep behavior disorder (iRBD). We studied 71 punch skin samples of 35 patients with CJD, including five assessed in vitam, using 2 two different substrates: Bank vole 23-230 (Bv23-230) and Syrian hamster 23-231 (Ha23-231) recombinant prion protein. Skin prion RT-QuIC showed a 100% specificity with both substrates and a higher sensitivity with the Bv23-230 than Ha23-231 (87.5% vs. 65.6%, respectively). Forty-one patients underwent both lumbar puncture (LB) and skin biopsy; CSF and skin RT-QuIC showed a high level of concordance (38/41, 92.7%). Then, we analyzed samples taken in vitam (n=69) or postmortem (n=49) from patients with Parkinson’s disease (PD), dementia with Lewy bodies (DLB), incidental Lewy body pathology, and neurological controls. Skin α-syn RT-QuIC distinguished LBD patients with an overall accuracy of 94.1% in the two cohorts (sensitivity, 89.2%; specificity, 96.3%). Seventy-nine patients underwent both CSF and skin α-syn RT-QuIC, and the two assays yielded similar diagnostic accuracy (skin, 97.5%; CSF, 98.7%). Finally, we studied 91 iRBD patients and 41 control. In the skin, RT-QuIC showed a sensitivity of 76.9%, specificity of 97.6%, and 82.0% accuracy. 128 participants (88 patients plus 40 controls) underwent both CSF and skin RT-QuIC. The two protocols showed 99.2% of concordance. These works confirmed that skin punch biopsies might represent a valid and convenient alternative to CSF analysis for an early diagnosis of prion diseases and LB-related pathologies.

Efficient and low-computational predictive models for spectral sensors

Spectral sensors are a wide class of devices that are extremely useful for detecting essential information of the environment and materials with high degree of selectivity. Recently, they have achieved high degrees of integration and low implementation cost to be suited for fast, small, and non-invasive monitoring systems. However, the useful information is hidden in spectra and it is difficult to decode. So, mathematical algorithms are needed to infer the value of the variables of interest from the acquired data. Between the different families of predictive modeling, Principal Component Analysis and the techniques stemmed from it can provide very good performances, as well as small computational and memory requirements. For these reasons, they allow the implementation of the prediction even in embedded and autonomous devices. In this thesis, I will present 4 practical applications of these algorithms to the prediction of different variables: moisture of soil, moisture of concrete, freshness of anchovies/sardines, and concentration of gasses. In all of these cases, the workflow will be the same. Initially, an acquisition campaign was performed to acquire both spectra and the variables of interest from samples. Then these data are used as input for the creation of the prediction models, to solve both classification and regression problems. From these models, an array of calibration coefficients is derived and used for the implementation of the prediction in an embedded system. The presented results will show that this workflow was successfully applied to very different scientific fields, obtaining autonomous and non-invasive devices able to predict the value of physical parameters of choice from new spectral acquisitions.

High friction and acoustic surface for pavements: development of artificial engineered aggregates from recycled powders

There is a constant need to improve the infrastructure's quality and build new infrastructure with better designs. The risk of accidents and noise can be reduced by improving the surface properties of the pavement. The amount of raw material used in road construction is worrisome, as it is finite and due the waste produced. Environmentally-friendly roads construction, recycling might be the main way. Projects must be more environmentally-friendly, safer, and quieter. Is it possible to develop a safer, quieter and environmentally-friendly pavement surfaces? The hypothesis is: is it possible to create an Artificial Engineered Aggregate (AEA) using waste materials and providing it with a specific shape that can help to reduce the noise and increase the friction? The thesis presents the development of an AEA and its application as a partial replacement in microsurfacing samples. The 1st introduces the topic and provides the aim and objectives of the thesis. The 2nd chapter – presents a pavement solution to noise and friction review. The 3rd chapter - developing a mix design for a geopolymer mortar that used basalt powder. The 4th chapter is presented the physical-mechanical evaluation of the AEA. The 5th chapter evaluates the use of this aggregate in microsurfacing regarding the texture parameters. The 6th chapter, those parameter are used as an input to SPERoN® model, simulating their noise behavior of these solutions. The findings from this thesis are presented as partial conclusions in each chapter, to be closed in a final chapter. The main findings are: the DoE provided the tool to select the appropriate geopolymer mortar mix design; AEA had interesting results regarding the physical-mechanical tests; AEA in partial replacement of the natural aggregates in microsurfacing mixture proved feasible. The texture parameters and noise levels obtained in AEA samples demonstrate that it can serve as a HIFASP