Designing tunnel lining in anhydritic claystones: intensity and distribution of swelling forces

The measured swelling pressures against tunnel linings range between a fraction of one MPa and 6–7 MPa. A strong spatial heterogeneity is often observed. The paper integrates these considerations into a procedure to design tunnel linings in anhydritic formations. Three-dimensional effects and proper consideration of heterogeneity is shown to be consistent with monitoring data of lining reinforcement stresses. The calculation methodology is illustrated in the case of the Lilla tunnel lining, which was monitored for more than 6 years. The described procedure leads to a rational design away from the conservatism of the assumption of uniform pressures against lining and two-dimensional modelling of tunnel cross-section.The Spanish railway administration (ADIF) kindly provided the monitoring data of AVE Lilla tunnel analysed here. The authors are grateful for the support received from Prof. Gonzalo Ramos for some of the structural calculations reported and to Eng. Francesc Cervera for his continuous collaboration and help regarding the A27 Motorway case. The Grants PID2021-122733OB-I00 and RTI2018-094226-J-I00 funded by the Spanish MCIN/AEI/10.13039/501100011033 and by “ERDF (FEDER) A way of making Europe” Projects, and, also, the former financial support of Spanish Public Works Ministry are gratefully acknowledged. Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature.Peer ReviewedPostprint (published version

IoT software infrastructure for Energy Management and Simulation in Smart Cities

This paper presents an IoT software infrastructure that enables energy management and simulation of new control policies in a city district. The proposed platform enables the interoperability and the correlation of (near-)real-time building energy profiles with environmental data from sensors as well as building and grid models. In a smart city context, this platform fulfills i) the integration of heterogeneous data sources at building and district level, and ii) the simulation of novel energy policies at district level aimed at the optimization of the energy usage accounting also for its impact on building comfort. The platform has been deployed in a real world district and a novel control policy for the heating distribution network has been developed and tested. Results are presented and discussed in the paper

Análisis del comportamiento del revestimiento circular del túnel de Lilla

El túnel de Lilla sufrió importantes deformaciones de expansión y presiones de hinchamiento desde su construcción debido a la precipitación de cristales de yeso en discontinuidades del macizo rocoso. Las expansiones afectaron gravemente el túnel y condujeron a construir una sección circular con un revestimiento de hormigón de alta resistencia altamente armado. En la tesina se analizará el comportamiento del revestimiento definitivo del túnel de Lilla

Análisis del comportamiento del revestimiento circular del túnel de Lilla

El túnel de Lilla sufrió importantes deformaciones de expansión y presiones de hinchamiento desde su construcción debido a la precipitación de cristales de yeso en discontinuidades del macizo rocoso. Las expansiones afectaron gravemente el túnel y condujeron a construir una sección circular con un revestimiento de hormigón de alta resistencia altamente armado. En la tesina se analizará el comportamiento del revestimiento definitivo del túnel de Lilla

Análisis del comportamiento del revestimiento circular del túnel de Lilla

El túnel de Lilla sufrió importantes deformaciones de expansión y presiones de hinchamiento desde su construcción debido a la precipitación de cristales de yeso en discontinuidades del macizo rocoso. Las expansiones afectaron gravemente el túnel y condujeron a construir una sección circular con un revestimiento de hormigón de alta resistencia altamente armado. En la tesina se analizará el comportamiento del revestimiento definitivo del túnel de Lilla

Combining BIM, GIS, and IoT to Foster Energy Management and Simulation in Smart Cities

This chapter presents a novel distributed software infrastructure to enable energy management and simulation of novel control strategies in smart cities. In this context, the following heterogeneous information, describing the different entities in a city, needs to be taken into account to form a unified district information model: internet-of-things (IoT) devices, building information model, system information model, and georeferenced information system. IoT devices are crucial to monitor in (near-) real-time both building energy trends and environmental data. Thus, the proposed solution fulfills the integration and interoperability of such data sources providing also a correlation among them. Such correlation is the key feature to unlock management and simulation of novel energy policies aimed at optimizing the energy usage accounting also for its impact on building comfort. The platform has been deployed in a real-world district and a novel control policy for the heating distribution network has been developed and tested. Finally, experimental results are presented and discusse

LLM-PBC: Logic Learning Machine-Based Explainable Rules Accurately Stratify the Genetic Risk of Primary Biliary Cholangitis

Background: The application of Machine Learning (ML) to genetic individual-level data represents a foreseeable advancement for the field, which is still in its infancy. Here, we aimed to evaluate the feasibility and accuracy of an ML-based model for disease risk prediction applied to Primary Biliary Cholangitis (PBC). Methods: Genome-wide significant variants identified in subjects of European ancestry in the recently released second international meta-analysis of GWAS in PBC were used as input data. Quality-checked, individual genomic data from two Italian cohorts were used. The ML included the following steps: import of genotype and phenotype data, genetic variant selection, supervised classification of PBC by genotype, generation of “if-then” rules for disease prediction by logic learning machine (LLM), and model validation in a different cohort. Results: The training cohort included 1345 individuals: 444 were PBC cases and 901 were healthy controls. After pre-processing, 41,899 variants entered the analysis. Several configurations of parameters related to feature selection were simulated. The best LLM model reached an Accuracy of 71.7%, a Matthews correlation coefficient of 0.29, a Youden’s value of 0.21, a Sensitivity of 0.28, a Specificity of 0.93, a Positive Predictive Value of 0.66, and a Negative Predictive Value of 0.72. Thirty-eight rules were generated. The rule with the highest covering (19.14) included the following genes: RIN3, KANSL1, TIMMDC1, TNPO3. The validation cohort included 834 individuals: 255 cases and 579 controls. By applying the ruleset derived in the training cohort, the Area under the Curve of the model was 0.73. Conclusions: This study represents the first illustration of an ML model applied to common variants associated with PBC. Our approach is computationally feasible, leverages individual-level data to generate intelligible rules, and can be used for disease prediction in at-risk individuals

LLM-PBC: Logic Learning Machine-Based Explainable Rules Accurately Stratify the Genetic Risk of Primary Biliary Cholangitis

Background: The application of Machine Learning (ML) to genetic individual-level data represents a foreseeable advancement for the field, which is still in its infancy. Here, we aimed to evaluate the feasibility and accuracy of an ML-based model for disease risk prediction applied to Primary Biliary Cholangitis (PBC). Methods: Genome-wide significant variants identified in subjects of European ancestry in the recently released second international meta-analysis of GWAS in PBC were used as input data. Quality-checked, individual genomic data from two Italian cohorts were used. The ML included the following steps: import of genotype and phenotype data, genetic variant selection, supervised classification of PBC by genotype, generation of “if-then” rules for disease prediction by logic learning machine (LLM), and model validation in a different cohort. Results: The training cohort included 1345 individuals: 444 were PBC cases and 901 were healthy controls. After pre-processing, 41,899 variants entered the analysis. Several configurations of parameters related to feature selection were simulated. The best LLM model reached an Accuracy of 71.7%, a Matthews correlation coefficient of 0.29, a Youden’s value of 0.21, a Sensitivity of 0.28, a Specificity of 0.93, a Positive Predictive Value of 0.66, and a Negative Predictive Value of 0.72. Thirty-eight rules were generated. The rule with the highest covering (19.14) included the following genes: RIN3, KANSL1, TIMMDC1, TNPO3. The validation cohort included 834 individuals: 255 cases and 579 controls. By applying the ruleset derived in the training cohort, the Area under the Curve of the model was 0.73. Conclusions: This study represents the first illustration of an ML model applied to common variants associated with PBC. Our approach is computationally feasible, leverages individual-level data to generate intelligible rules, and can be used for disease prediction in at-risk individuals

Machine learning in primary biliary cholangitis: A novel approach for risk stratification

Background & Aims Machine learning (ML) provides new approaches for prognostication through the identification of novel subgroups of patients. We explored whether ML could support disease sub-phenotyping and risk stratification in primary biliary cholangitis (PBC). Methods ML was applied to an international dataset of PBC patients. The dataset was split into a derivation cohort (training set) and a validation cohort (validation set), and key clinical features were analysed. The outcome was a composite of liver-related death or liver transplantation. ML and standard survival analysis were performed. Results The training set was composed of 11,819 subjects, while the validation set was composed of 1,069 subjects. ML identified four clusters of patients characterized by different phenotypes and long-term prognosis. Cluster 1 (n = 3566) included patients with excellent prognosis, whereas Cluster 2 (n = 3966) consisted of individuals at worse prognosis differing from Cluster 1 only for albumin levels around the limit of normal. Cluster 3 (n = 2379) included young patients with florid cholestasis and Cluster 4 (n = 1908) comprised advanced cases. Further sub-analyses on the dynamics of albumin within the normal range revealed that ursodeoxycholic acid-induced increase of albumin >1.2 x lower limit of normal (LLN) is associated with improved transplant-free survival. Conclusions Unsupervised ML identified four novel groups of PBC patients with different phenotypes and prognosis and highlighted subtle variations of albumin within the normal range. Therapy-induced increase of albumin >1.2 x LLN should be considered a treatment goal