Machine learning approach to the safety assessment of a prestressed concrete railway bridge

Early structural anomalies identification allows to hold maintenance activities that avoid loss of both economic resources and human life. This is extremely important for crucial infrastructures like railway bridges. This paper illustrates the structural health monitoring approach applied to a simply supported prestressed concrete railway bridge. In the framework of long-term monitoring, both static quantities (displacements, strains, and rotations) and environmental measurements (temperatures) have been recorded. Machine learning techniques, Extreme Gradient boosting machine and Multi-Layer Perceptron, have been exploited to build regression correlation models associated with the undamaged structural condition after adequate pre-processing operations. In this way, alarm thresholds based on the expected residuals between the predicted structural quantities and the measured ones, have been defined. The thresholds turned out to be able to catch early-stage anomalies not pointed out by traditional damage thresholds based on the design values. The proposed damage index is chosen as the moving median of the residuals, allowing a significant reduction of false alarms. The used correlation models and the obtained results represent a starting point for the generalization of this approach to the bridges belonging to the same static typology

Una metodologia speditiva per la valutazione della pericolosit\ue0 alluvionale associata ad infrastrutture di trasporto estese

In caso di eventi alluvionali, i danni strutturali e/o le interruzioni di servizio delle infrastrutture lineari di trasporto (ad esempio ferrovie, autostrade, ecc.) sono responsabili di una parte considerevole delle perdite economiche totali, dirette e indirette, specialmente nei Paesi pi\uf9 sviluppati. Partendo da questa considerazione, la memoria, che descrive la metodologia messa a punto in un recente Contratto di ricerca tra Rete Ferroviaria Italiana S.p.A. e il Dipartimento di Ingegneria Civile, Chimica, Ambientale e dei Materiali dell\u2019Universit\ue0 di Bologna (RFI-DICAM, 2019), si concentra sulle grandi reti di infrastrutture di trasporto (in particolare la rete ferroviaria) e propone una metodologia speditiva per (1) identificare le porzioni di tratte della rete infrastrutturale esposte a pericolosit\ue0 per alluvioni fluviali e (2) raggruppare tali porzioni di tratte in classi diverse a seconda del tipo di fenomeno alluvionale a cui sono esposte, del meccanismo di danneggiamento e della gravit\ue0 dello scenario di allagamento attesi (ad es., sormonto del rilevato, instabilit\ue0 dello stesso, dovuta a infiltrazione o sifonamento, possibile collisione di treni o veicoli contro detriti fluviali, ecc.). L\u2019identificazione di queste sottotratte, nonch\ue9 la stima delle principali grandezze che quantificano l\u2019intensit\ue0 dell\u2019evento (ad es. tiranti, velocit\ue0 dell\u2019acqua, durata dell\u2019alluvione, ecc.), in linea di principio, potrebbero basarsi sui risultati di simulazioni idrodinamiche; tuttavia, nel caso di reti di trasporto di notevole estensione, tali simulazioni sono al pi\uf9 disponibili per limitate porzioni del territorio. Nel caso in cui si desideri pervenire a una valutazione complessiva, o comunque ad ampia scala spaziale, \ue8 conveniente ricorrere all\u2019applicazione di algoritmi e indici geomorfologici di rapida elaborazione basati sui DTM (Digital Terrain Model) come, nel nostro caso, il Geomorphic Flood Index (GFI). In quest\u2019ultimo caso, come evidenziato in letteratura e anche nella presente memoria, si identificano, con accettabile precisione e affidabilit\ue0, le aree soggette a pericolosit\ue0 alluvionale a partire dalla caratterizzazione geomorfologica del bacino fluviale. La struttura metodologica proposta \ue8 adatta a studi su larga scala poich\ue9 si basa principalmente su semplici analisi di DTM e richiede, in aggiunta, per la calibrazione, soltanto una mappatura dell\u2019area allagabile (relativa a eventi reali o anche a simulazioni idrauliche di scenario) riferita ad una porzione, di estensione anche contenuta, del territorio di interesse. Nella memoria, accanto all\u2019approccio GFI utilizzato per l\u2019identificazione delle tratte inondabili, viene anche implementato un approccio geomorfologico utile a identificare le potenziali sorgenti di colate detritiche, il cui percorso pu\uf2 colpire l\u2019infrastruttura. La procedura \ue8 stata testata in alcuni casi di studio reali in Italia e i risultati sono stati confrontati con le testimonianze di eventi alluvionali verificatesi lungo le linee ferroviarie che hanno subito rallentamenti o interruzioni del traffico. I risultati mostrano le potenzialit\ue0 della metodologia, che pu\uf2 essere facilmente adattata a diversi casi di studio e rispondere a requisiti specifici. La procedura proposta rappresenta uno strumento robusto, applicabile anche su scala nazionale, per supportare i processi decisionali di identificazione delle misure di gestione e di mitigazione del rischio di alluvione associato a grandi reti di trasporto

A bioluminescent mouse model of proliferation to highlight early stages of pancreatic cancer: A suitable tool for preclinical studies

Transgenic mouse models designed to recapitulate genetic and pathologic aspects of cancer are useful to study early stages of disease as well as its progression. Among several, two of the most sophisticated models for pancreatic ductal adenocarcinoma (PDAC) are the LSL-KrasG12D/+;Pdx-1-Cre (KC) and LSL-KrasG12D/+;LSL-Trp53R172H/+;Pdx-1-Cre (KPC) mice, in which the Cre-recombinase regulated by a pancreas-specific promoter activates the expression of oncogenic Kras alone or in combination with a mutant p53, respectively. Non-invasive in vivo imaging offers a novel approach to preclinical studies introducing the possibility to investigate biological events in the spatio/temporal dimension. We recently developed a mouse model, MITO-Luc, engineered to express the luciferase reporter gene in cells undergoing active proliferation. In this model, proliferation events can be visualized non-invasively by bioluminescence imaging (BLI) in every body district in vivo. Here, we describe the development and characterization of MITO-Luc-KC- and -KPC mice. In these mice we have now the opportunity to follow PDAC evolution in the living animal in a time frame process. Moreover, by relating in vivo and ex vivo BLI and histopathological data we provide evidence that these mice could represents a suitable tool for pancreatic cancer preclinical studies. Our data also suggest that aberrant proliferation events take place early in pancreatic carcinogenesis, before tumour appearance

A bioluminescent mouse model of proliferation to highlight early stages of pancreatic cancer. A suitable tool for preclinical studies

Transgenic mouse models designed to recapitulate genetic and pathologic aspects of cancer are usefulto study early stages of disease as well as its progression. Among several, two of the most sophis-ticated models for pancreatic ductal adenocarcinoma (PDAC) are the LSL-KrasG12D/+;Pdx-1-Cre (KC)and LSL-KrasG12D/+;LSL-Trp53R172H/+;Pdx-1-Cre (KPC) mice, in which the Cre-recombinase regulated by apancreas-specific promoter activates the expression of oncogenic Kras alone or in combination witha mutant p53, respectively. Non-invasive in vivo imaging offers a novel approach to preclinical stud-ies introducing the possibility to investigate biological events in the spatio/temporal dimension. Werecently developed a mouse model, MITO-Luc, engineered to express the luciferase reporter gene in cellsundergoing active proliferation. In this model, proliferation events can be visualized non-invasively bybioluminescence imaging (BLI) in every body district in vivo. Here, we describe the development andcharacterization of MITO-Luc-KC- and -KPC mice. In these mice we have now the opportunity to followPDAC evolution in the living animal in a time frame process. Moreover, by relating in vivo and ex vivoBLI and histopathological data we provide evidence that these mice could represents a suitable tool forpancreatic cancer preclinical studies. Our data also suggest that aberrant proliferation events take placeearly in pancreatic carcinogenesis, before tumour appearance

Experimental Analysis of Hot-Mix Asphalt (HMA) Mixtures with Reclaimed Asphalt Pavement (RAP) in Railway Sub-Ballast

Environmental safeguards promote innovative construction technologies for sustainable pavements. On these premises, this study investigated four hot mix asphalt (HMA) mixtures—i.e., A, B, C, and D—for the railway sub-ballast layer with 0%, 10%, 20%, and 30% reclaimed asphalt pavement (RAP) by total aggregate mass and a rejuvenator additive, varying the bitumen content between 3.5% and 5.0%. Both Marshall and gyratory compactor design methods have been performed, matching the stability, indirect tensile strength, and volumetric properties of each mixture. Dynamic stiffness and fatigue resistance tests provided mechanical performances. Laboratory results highlighted that the RAP and the rejuvenator additive increase the mechanical properties of the mixtures. In addition, the comparative analysis of production costs revealed up to 20% savings as the RAP content increased, and the life cycle impact analysis (LCIA) proved a reduction of the environmental impacts (up to 2% for resource use-fossils, up to 7% for climate change, and up to 13% for water use). The experimental results confirm that HMA containing RAP has mechanical performances higher than the reference mixture with only virgin raw materials. These findings could contribute to waste management and reduce the environmental and economic costs, since the use of RAP in the sub-ballast is not, so far, provided in the Italian specifications for railway construction

Experimental Analysis of Hot-Mix Asphalt (HMA) Mixtures with Reclaimed Asphalt Pavement (RAP) in Railway Sub-Ballast

Environmental safeguards promote innovative construction technologies for sustainable pavements. On these premises, this study investigated four hot mix asphalt (HMA) mixtures—i.e., A, B, C, and D—for the railway sub-ballast layer with 0%, 10%, 20%, and 30% reclaimed asphalt pavement (RAP) by total aggregate mass and a rejuvenator additive, varying the bitumen content between 3.5% and 5.0%. Both Marshall and gyratory compactor design methods have been performed, matching the stability, indirect tensile strength, and volumetric properties of each mixture. Dynamic stiffness and fatigue resistance tests provided mechanical performances. Laboratory results highlighted that the RAP and the rejuvenator additive increase the mechanical properties of the mixtures. In addition, the comparative analysis of production costs revealed up to 20% savings as the RAP content increased, and the life cycle impact analysis (LCIA) proved a reduction of the environmental impacts (up to 2% for resource use-fossils, up to 7% for climate change, and up to 13% for water use). The experimental results confirm that HMA containing RAP has mechanical performances higher than the reference mixture with only virgin raw materials. These findings could contribute to waste management and reduce the environmental and economic costs, since the use of RAP in the sub-ballast is not, so far, provided in the Italian specifications for railway construction