The non-destructive thermal characterization of building envelopes relies significantly on various factors such as climate conditions, monitoring devices used, indoor environment, and conditioning systems. In the case of both the temperature-based method (TBM) and heat flux meter (HFM) approaches, U-value is determined considering the ideal condition of steady state. However, it is challenging to accurately define the true thermal condition of buildings when monitoring is affected by inherent uncertainties of the chosen approach and inadequate instrumentation of building envelopes. This paper presents the outcomes of an experimental campaign, that aimed to evaluate the impact of incorrectly positioned exterior sensors, on the precision of U-value measurements. This study simultaneously employed the TBM and HFM approaches. To enhance the accuracy of the results, rigorous outlier detection and statistical analysis were employed on the data collected from three autonomous monitoring systems. The findings of this study revealed that the applied data analysis yielded more satisfactory results for the TBM approach compared to HFM. However, regardless of the approach used, the effectiveness of outlier detection relied heavily on the accuracy of the monitoring systems. When removing an individual outlier, the monitoring systems characterized with higher accuracies provided U-values that were closer to the theoretical values, than less accurate ones., The authors are indebted to the Spanish Ministry of Economy and Competitiveness for the funding provided through the research projects BIA2017-86811-C2-1-R , directed by Jose Turmo, BIA2017-86811-C2-2-R , directed by Jose Antonio Lozano-Galant, and PID2021-126405OB-C32 funded by FEDER funds—A Way to Make Europe and Spanish Ministry of Economy and Competitiveness MICIN/AEI/10.13039/501100011033/. It is also to be noted that funding for this research has been provided to Behnam Mobaraki by Ministerio de Ciencia Innovación y Universidades : grant number: 2018-COB-9092 . Arturo Martínez acknowledges the support provided by the Consejo Nacional de Ciencia y Tecnología (CONACYT) and the Fundación del Instituto Nacional de Bellas Artes (FINBA) given through its doctorate scholarship program., Peer reviewed

Among the multicriteria decision-making (MCDM) methods, the Integrated Value Model for Sustainability Assessment (MIVES) stands out for its ability to quantitatively assess sustainability of civil and construction engineering projects. The integration of this method with building information modeling (BIM) offers a holistic approach to construction project management, this allowing promoting better decision-making, collaboration, and ultimately, more successful and efficient construction projects. While literature explores the link between BIM and MIVES, efforts often lack consistent sustainability assessment automation. Current approaches mainly define MIVES indicators within the BIM model, without achieving a comprehensive evaluation automation. To address this issue, a novel methodology that combines MIVES and BIM is presented to automate the sustainability assessment. The proposed methodology automates the sustainability analysis of BIM digital twin in Revit using quantity tables and internal operators. In addition, a parametric programming tool in Dynamo is developed for direct MIVES and BIM coupling. The approach is validated using a real viaduct to assess sustainability and perform a sensitivity analysis in order to identify those indicators with the most influence on sustainability performance. Critically, this methodology is not limited to the sustainability assessment of bridges as it can be readily adapted to other types of infrastructure. Moreover, it is worth noting that the capabilities of this approach can be significantly augmented by incorporating data from sensors and diverse sources. This inclusion of sensor-derived or alternative data sources enhances the depth and accuracy of the insights drawn from the integrated MCDM-BIM framework., The authors are indebted to the projects PID2021-126405OB-C31, and PID2021-126405OB- C32 funded by MICIN/AEI/10.13039/501100011033/ and FEDER funds A way to make Europe. The financial support of the project F-00528 (PLEC2022-009441) with the title “Optimización topológica para una metodología de construcción de bajo impacto ambiental (Topological optimization for a low environmental impact construction methodology)” is also appreciated.

The development of low-cost structural and environmental sensors has sparked a transformation across numerous fields, offering cost-effective solutions for monitoring infrastructures and buildings. However, the affordability of these solutions often comes at the expense of accuracy. To enhance precision, the LARA (Low-cost Adaptable Reliable Anglemeter) system averaged the measurements of a set of five different accelerometers working as inclinometers. However, it is worth noting that LARA’s sensitivity still falls considerably short of that achieved by other high-accuracy commercial solutions. There are no works presented in the literature to enhance the accuracy, precision, and resolution of low-cost inclinometers using artificial intelligence (AI) tools for measuring structural deformation. To fill these gaps, artificial intelligence (AI) techniques are used to elevate the precision of the LARA system working as an inclinometer. The proposed AI-driven tool uses Multilayer Perceptron (MLP) to glean insight from high-accuracy devices’ responses. The efficacy and practicality of the proposed tools are substantiated through the structural and environmental monitoring of a real steel frame located in Cuenca, Spain., This research was funded by FEDER, grant number PID2021-126405OB-C31 and PID2021-126405OB-C32—A Way to Make Europe and Spanish Ministry of Economy and Competitiveness MICIN/AEI/10.13039/501100011033/, National Natural Science Foundation of China (52278313), and a grant provided by the Polytechnic University of Catalonia with a reference of ALECTORS-2023., Peer Reviewed

A new direct simulation of the cantilever erection method is presented in order to reduce the prestressing operations of stay cables. This new method introduces a direct approach for cable-stayed bridge construction using the cantilever erection method, employing independent finite element models. Prestressing forces are modeled as imposed strains based on the unstressed length concept to ensure the achievement of a specific target state post-construction (objective service stage, OSS). This facilitates faster simulation of the construction process, particularly for steel bridges and the predesign of concrete cable-stayed bridges, and enables direct simulation of construction stages and OSS achievement. The proposed method's efficacy is demonstrated through the analysis of a cable-stayed bridge., The authors are indebted to the projects PID2021-126405OB-C31 and PID2021-126405OB-C32 funded by FEDER funds A way to make Europe and Spanish Ministry of Economy and Competitiveness MICIN/AEI/10.13039/501100011033/ directed by Professor José Turmo. Authors are also indebted to the Secretaria d'Universitats i Recerca de la Generalitat de Catalunya for the funding provided through AGAUR (2020 DI 112).

Structural Health Monitoring (SHM) is crucial for infrastructure safety and integrity. Arduino-based sensors are gaining popularity in low-cost SHM structures. Distributed fiber optic systems (DFOS), such as Distributed Acoustic Sensing (DAS), are employed for accurate SHM despite their high costs, computational demands, and energy consumption. The primary objectives of this work are to compare the accuracy of an accelerometer named LARA (Low-cost Adaptable Reliable Accelerometer (LARA)) that utilizes both Arduino and Raspberry Pi technologies with a DAS system in detecting structural damage and to explore the potential advantages of combining LARA and DAS to create an effective SHM tool. This study is the first to enhance the design of LARA. Subsequently, LARA and DAS were used in a laboratory setting to analyze eigenfrequency changes in a beam model with induced localized damage. Finally, this study evaluated the precision and reliability of LARA and its potential role as a trigger for DAS in detecting localized damage. The findings show that both LARA and DAS can identify changes in the eigenfrequencies of damaged structures with deviations as small as 3.68 %. Consequently, LARA demonstrated its potential as a trigger for DAS, significantly reducing the computational demands while enriching the analysis. This approach offers highly accurate eigenfrequency measurements and enhances the analytical capabilities of DAS by identifying the primary axes of the detected eigenfrequencies., The authors are indebted to the projects PID2021-126405OB-C31 and PID2021–126405OB-C32 funded by FEDER funds A way to make Europe and Spanish Ministry of Economy and Competitiveness MICIN/AEI/10.13039/501100011033/. Dr. Seyedmilad Komarizadehasl is indebted to a grant provided by the Polytechnic University of Catalonia to encourage research among new teaching staff with a reference of ALECTORS-2023., Peer Reviewed

The rapid evolution of digital technologies has revolutionized the architecture, engineering, and construction (AEC) industry, driving the wide-spread adoption of digital twins for structures. These virtual replicas, developed using Building Information Modeling (BIM) methodology, incorporate extensive information databases, proving indispensable for enhancing project management throughout a structure’s entire lifecycle and towards smart city development. As the impact of climate change continues to grow, hazardous weather alerts play a critical role as an early-warning system that notifies stakeholders of imminent threats, thereby influencing decision-making processes in construction projects. Surprisingly, despite its evident value, the integration of alert systems for hazardous weather conditions into BIM is often overlooked. To fill this gap, this paper proposes Robot Process Automation (RPA) protocols to automate the integration of real-time weather parameters into a structure’s BIM models. These very protocols are also used as alert systems, enabling the timely notification of stakeholders in the event of detected hazardous weather conditions. The effectiveness of the proposed methodology is demonstrated through its practical application in enhancing the safety of an actual building in Viña del Mar, Chile., This research was funded by FEDER, grant number PID2021-126405OB-C31 and PID2021- 126405OB-C32 funded by FEDER funds—A Way to Make Europe and Spanish Ministry of Economy and Competitiveness MICIN/AEI/10.13039/501100011033/, Peer Reviewed

In response to the global demand for structural health monitoring (SHM), particularly in the context of expanding structural assets, this paper conducts a thorough exploration of the integration of low-cost sensors in SHM applications. The primary focus is to introduce various low-cost sensors commonly used in SHM applications for bridges, aiming to unveil their full potential as economical alternatives to expensive commercial sensors. This approach not only broadens accessibility, allowing structures with limited SHM resources to benefit but also enhances measurement points for more robust results. The study begins with the introduction of the NodeMCU, serving as the programmable logic controller equipped with a built-in WiFi module. This feature enables IoT functionality for the low-cost sensors under review. The exploration then delves into an array of digital sensors. Systematic ambient tests were conducted to uncover challenges during sensor installation and data acquisition. The paper not only introduces these low-cost electronic devices but also provides practical solutions to overcome identified issues, ensuring their effective utilization for SHM purposes., The authors are indebted to the projects PID2021-126405OB-C31 and PID2021-126405OB-C32 funded by FEDER funds—A Way to Make Europe and Spanish Ministry of Economy, Competitiveness MICIN/AEI/10.13039/501100011033/ and the European Union “NextGenerationEU”/PRTR. Project references: PID2019-108978RB-C32, PID2021-126405OB-C31, PID2021-126405OB-C32 and PLEC2021-007982., Peer Reviewed

The shear deflection effects are traditionally neglected in most structural system identification methods. Unfortunately, this assumption might lead to significant errors in some structures, like deep beams. Although some inverse analysis methods based on the stiffness matrix method, including shear deformation effects, have been presented in the literature, none of these methods are able to deal with actual rotations in their formulations. Recently, the observability techniques, one of the first methods for the inverse analysis of structures, included the shear effects into the system of equations. In this approach, the effects of the shear rotation are neglected. When actual rotations on-site are used to estimate the mechanical properties in the inverse analysis, it can result in serious errors in the observed properties. This characteristic might be especially problematic in structures such as deep beams where only rotations can be measured. To solve this problem and increase the observability techniques’ applicability, this paper proposes a new approach to include the shear rotations into the inverse analysis by observability techniques. This modification is based on the introduction of a new iterative process. To illustrate the applicability and potential of the proposed method, the inverse analysis of several examples of growing complexity is presented., The authors are indebted to the projects PID2021-126405OB-C31 and PID2021-126405OB-C32 funded by FEDER funds—A Way to Make Europe and Spanish Ministry of Economy and Competitiveness MICIN/AEI/10.13039/501100011033/, and the National Natural Science Foundation of China (52278308) directed by Yuan Sun., Peer Reviewed

The concept of observability analysis has garnered substantial attention in the field of structural system identification. Its primary aim is to identify a specific set of structural characteristics, such as Young’s modulus, area, inertia, and possibly their combinations (e.g., flexural or axial stiffness). These characteristics can be uniquely determined when provided with a suitable subset of deflections, forces, and/or moments at the nodes of the structure. This problem is particularly intricate within the realm of structural system identification, mainly due to the presence of nonlinear unknown variables, such as the product of vertical deflection and flexural stiffness, in accordance with modern methodologies. Consequently, the mechanical and geometrical properties of the structure are intricately linked with node deflections and/or rotations. The paper at hand serves a dual purpose: firstly, it introduces the concept of static-state estimation, especially tailored for the identification of structural systems; and secondly, it presents a novel observability analysis method grounded in static-state estimation principles, designed to overcome the aforementioned challenges. Computational experiments shed light on the algorithm’s potential for practical structural system identification applications, demonstrating significant advantages over the existing state-of-the-art methods found in the literature. It is noteworthy that these advantages could potentially be further amplified by addressing the static-state estimation principles problem, which constitutes a subject for future research. Solving this problem would help address the additional challenge of developing efficient techniques that can accommodate redundancy and uncertainty when estimating the current state of the structure., The authors would like to express their gratitude for thefunding provided by the following projects: ProjectPID2021-126405OB-C31: “Development of low-cost mod-ular sensors for use in the structural identifcation of bridgessubjected to quasi-static loads.” Project PID2021-126405OB-C32: “Alarm system for bridge management systems withBIM digital twins using artifcial intelligence.” Tese projectshave been generously funded by MICIN (Ministry of Scienceand Innovation), AEI (State Research Agency), and the “Away to make Europe” FEDER Funds. Dr. Mínguez’s researchwas supported by project “SENSEI: Smart watEr NetworkSusing artifcial intEllIgence” with code CNS2022-135472,funded by MCIN/AEI/10.13039/501100011033 and by theEuropean Union Next Generation EU/PRTR, and R&Dproject “Algorithms for Stochastic Optimization UsingData-driven and Learning Analysis (ASTRAL)” with codePID2023-151013NB-I00, funded by MCIN/AEI/10.13039/501100011033 and the European Union Next GenerationEU/PRTR. Finally, scholarship funding was obtained fromL’Agència de Gestió d’Ajuts Universitaris i de Recerca(AGAUR) through the “Ajuts de suport a departaments iunitats de recerca universitaris per a la contractació depersonal investigador predoctoral en formació (FI SDUR2022).”, Peer Reviewed

Among the multicriteria decision-making (MCDM) methods, the Integrated Value Model for Sustainability Assessment (MIVES) stands out for its ability to quantitatively assess sustainability of civil and construction engineering projects. The integration of this method with building information modeling (BIM) offers a holistic approach to construction project management, this allowing promoting better decision-making, collaboration, and ultimately, more successful and efficient construction projects. While literature explores the link between BIM and MIVES, efforts often lack consistent sustainability assessment automation. Current approaches mainly define MIVES indicators within the BIM model, without achieving a comprehensive evaluation automation. To address this issue, a novel methodology that combines MIVES and BIM is presented to automate the sustainability assessment. The proposed methodology automates the sustainability analysis of BIM digital twin in Revit using quantity tables and internal operators. In addition, a parametric programming tool in Dynamo is developed for direct MIVES and BIM coupling. The approach is validated using a real viaduct to assess sustainability and perform a sensitivity analysis in order to identify those indicators with the most influence on sustainability performance. Critically, this methodology is not limited to the sustainability assessment of bridges as it can be readily adapted to other types of infrastructure. Moreover, it is worth noting that the capabilities of this approach can be significantly augmented by incorporating data from sensors and diverse sources. This inclusion of sensor-derived or alternative data sources enhances the depth and accuracy of the insights drawn from the integrated MCDM-BIM framework., The authors are indebted to the projects PID2021-126405OB-C31, and PID2021-126405OB- C32 funded by MICIN/AEI/10.13039/501100011033/ and FEDER funds A way to make Europe. The financial support of the project F-00528 (PLEC2022-009441) with the title “Optimización topológica para una metodología de construcción de bajo impacto ambiental (Topological optimization for a low environmental impact construction methodology)” is also appreciated., Postprint (published version)

In response to the global demand for structural health monitoring (SHM), particularly in the context of expanding structural assets, this paper conducts a thorough exploration of the integration of low-cost sensors in SHM applications. The primary focus is to introduce various low-cost sensors commonly used in SHM applications for bridges, aiming to unveil their full potential as economical alternatives to expensive commercial sensors. This approach not only broadens accessibility, allowing structures with limited SHM resources to benefit but also enhances measurement points for more robust results. The study begins with the introduction of the NodeMCU, serving as the programmable logic controller equipped with a built-in WiFi module. This feature enables IoT functionality for the low-cost sensors under review. The exploration then delves into an array of digital sensors. Systematic ambient tests were conducted to uncover challenges during sensor installation and data acquisition. The paper not only introduces these low-cost electronic devices but also provides practical solutions to overcome identified issues, ensuring their effective utilization for SHM purposes., The authors are indebted to the projects PID2021-126405OB-C31 and PID2021-126405OB-C32 funded by FEDER funds—A Way to Make Europe and Spanish Ministry of Economy, Competitiveness MICIN/AEI/10.13039/501100011033/ and the European Union “NextGenerationEU”/PRTR. Project references: PID2019-108978RB-C32, PID2021-126405OB-C31, PID2021-126405OB-C32 and PLEC2021-007982., Peer Reviewed, Postprint (published version)

The development of low-cost structural and environmental sensors has sparked a transformation across numerous fields, offering cost-effective solutions for monitoring infrastructures and buildings. However, the affordability of these solutions often comes at the expense of accuracy. To enhance precision, the LARA (Low-cost Adaptable Reliable Anglemeter) system averaged the measurements of a set of five different accelerometers working as inclinometers. However, it is worth noting that LARA’s sensitivity still falls considerably short of that achieved by other high-accuracy commercial solutions. There are no works presented in the literature to enhance the accuracy, precision, and resolution of low-cost inclinometers using artificial intelligence (AI) tools for measuring structural deformation. To fill these gaps, artificial intelligence (AI) techniques are used to elevate the precision of the LARA system working as an inclinometer. The proposed AI-driven tool uses Multilayer Perceptron (MLP) to glean insight from high-accuracy devices’ responses. The efficacy and practicality of the proposed tools are substantiated through the structural and environmental monitoring of a real steel frame located in Cuenca, Spain., This research was funded by FEDER, grant number PID2021-126405OB-C31 and PID2021-126405OB-C32—A Way to Make Europe and Spanish Ministry of Economy and Competitiveness MICIN/AEI/10.13039/501100011033/, National Natural Science Foundation of China (52278313), and a grant provided by the Polytechnic University of Catalonia with a reference of ALECTORS-2023., Peer Reviewed, Postprint (published version)

The shear deflection effects are traditionally neglected in most structural system identification methods. Unfortunately, this assumption might lead to significant errors in some structures, like deep beams. Although some inverse analysis methods based on the stiffness matrix method, including shear deformation effects, have been presented in the literature, none of these methods are able to deal with actual rotations in their formulations. Recently, the observability techniques, one of the first methods for the inverse analysis of structures, included the shear effects into the system of equations. In this approach, the effects of the shear rotation are neglected. When actual rotations on-site are used to estimate the mechanical properties in the inverse analysis, it can result in serious errors in the observed properties. This characteristic might be especially problematic in structures such as deep beams where only rotations can be measured. To solve this problem and increase the observability techniques’ applicability, this paper proposes a new approach to include the shear rotations into the inverse analysis by observability techniques. This modification is based on the introduction of a new iterative process. To illustrate the applicability and potential of the proposed method, the inverse analysis of several examples of growing complexity is presented., The authors are indebted to the projects PID2021-126405OB-C31 and PID2021-126405OB-C32 funded by FEDER funds—A Way to Make Europe and Spanish Ministry of Economy and Competitiveness MICIN/AEI/10.13039/501100011033/, and the National Natural Science Foundation of China (52278308) directed by Yuan Sun., Peer Reviewed, Postprint (published version)

The concept of observability analysis has garnered substantial attention in the field of structural system identification. Its primary aim is to identify a specific set of structural characteristics, such as Young’s modulus, area, inertia, and possibly their combinations (e.g., flexural or axial stiffness). These characteristics can be uniquely determined when provided with a suitable subset of deflections, forces, and/or moments at the nodes of the structure. This problem is particularly intricate within the realm of structural system identification, mainly due to the presence of nonlinear unknown variables, such as the product of vertical deflection and flexural stiffness, in accordance with modern methodologies. Consequently, the mechanical and geometrical properties of the structure are intricately linked with node deflections and/or rotations. The paper at hand serves a dual purpose: firstly, it introduces the concept of static-state estimation, especially tailored for the identification of structural systems; and secondly, it presents a novel observability analysis method grounded in static-state estimation principles, designed to overcome the aforementioned challenges. Computational experiments shed light on the algorithm’s potential for practical structural system identification applications, demonstrating significant advantages over the existing state-of-the-art methods found in the literature. It is noteworthy that these advantages could potentially be further amplified by addressing the static-state estimation principles problem, which constitutes a subject for future research. Solving this problem would help address the additional challenge of developing efficient techniques that can accommodate redundancy and uncertainty when estimating the current state of the structure., The authors would like to express their gratitude for thefunding provided by the following projects: ProjectPID2021-126405OB-C31: “Development of low-cost mod-ular sensors for use in the structural identifcation of bridgessubjected to quasi-static loads.” Project PID2021-126405OB-C32: “Alarm system for bridge management systems withBIM digital twins using artifcial intelligence.” Tese projectshave been generously funded by MICIN (Ministry of Scienceand Innovation), AEI (State Research Agency), and the “Away to make Europe” FEDER Funds. Dr. Mínguez’s researchwas supported by project “SENSEI: Smart watEr NetworkSusing artifcial intEllIgence” with code CNS2022-135472,funded by MCIN/AEI/10.13039/501100011033 and by theEuropean Union Next Generation EU/PRTR, and R&Dproject “Algorithms for Stochastic Optimization UsingData-driven and Learning Analysis (ASTRAL)” with codePID2023-151013NB-I00, funded by MCIN/AEI/10.13039/501100011033 and the European Union Next GenerationEU/PRTR. Finally, scholarship funding was obtained fromL’Agència de Gestió d’Ajuts Universitaris i de Recerca(AGAUR) through the “Ajuts de suport a departaments iunitats de recerca universitaris per a la contractació depersonal investigador predoctoral en formació (FI SDUR2022).”, Peer Reviewed, Postprint (published version)

Structural Health Monitoring (SHM) is crucial for infrastructure safety and integrity. Arduino-based sensors are gaining popularity in low-cost SHM structures. Distributed fiber optic systems (DFOS), such as Distributed Acoustic Sensing (DAS), are employed for accurate SHM despite their high costs, computational demands, and energy consumption. The primary objectives of this work are to compare the accuracy of an accelerometer named LARA (Low-cost Adaptable Reliable Accelerometer (LARA)) that utilizes both Arduino and Raspberry Pi technologies with a DAS system in detecting structural damage and to explore the potential advantages of combining LARA and DAS to create an effective SHM tool. This study is the first to enhance the design of LARA. Subsequently, LARA and DAS were used in a laboratory setting to analyze eigenfrequency changes in a beam model with induced localized damage. Finally, this study evaluated the precision and reliability of LARA and its potential role as a trigger for DAS in detecting localized damage. The findings show that both LARA and DAS can identify changes in the eigenfrequencies of damaged structures with deviations as small as 3.68 %. Consequently, LARA demonstrated its potential as a trigger for DAS, significantly reducing the computational demands while enriching the analysis. This approach offers highly accurate eigenfrequency measurements and enhances the analytical capabilities of DAS by identifying the primary axes of the detected eigenfrequencies., The authors are indebted to the projects PID2021-126405OB-C31 and PID2021–126405OB-C32 funded by FEDER funds A way to make Europe and Spanish Ministry of Economy and Competitiveness MICIN/AEI/10.13039/501100011033/. Dr. Seyedmilad Komarizadehasl is indebted to a grant provided by the Polytechnic University of Catalonia to encourage research among new teaching staff with a reference of ALECTORS-2023., Peer Reviewed, Postprint (author's final draft)

The rapid evolution of digital technologies has revolutionized the architecture, engineering, and construction (AEC) industry, driving the wide-spread adoption of digital twins for structures. These virtual replicas, developed using Building Information Modeling (BIM) methodology, incorporate extensive information databases, proving indispensable for enhancing project management throughout a structure’s entire lifecycle and towards smart city development. As the impact of climate change continues to grow, hazardous weather alerts play a critical role as an early-warning system that notifies stakeholders of imminent threats, thereby influencing decision-making processes in construction projects. Surprisingly, despite its evident value, the integration of alert systems for hazardous weather conditions into BIM is often overlooked. To fill this gap, this paper proposes Robot Process Automation (RPA) protocols to automate the integration of real-time weather parameters into a structure’s BIM models. These very protocols are also used as alert systems, enabling the timely notification of stakeholders in the event of detected hazardous weather conditions. The effectiveness of the proposed methodology is demonstrated through its practical application in enhancing the safety of an actual building in Viña del Mar, Chile., This research was funded by FEDER, grant number PID2021-126405OB-C31 and PID2021- 126405OB-C32 funded by FEDER funds—A Way to Make Europe and Spanish Ministry of Economy and Competitiveness MICIN/AEI/10.13039/501100011033/, Peer Reviewed, Postprint (published version)

Underground mines are considered one of the riskiest facilities for human activities due to numerous accidents and geotechnical failures recorded worldwide over the last century, which have resulted in unsafe labor conditions, poor health outcomes, injuries, and fatalities. One significant cause of these accidents is the inadequate or nonexistent capacity for the real-time monitoring of safety conditions in underground mines. In this context, new emerging technologies linked to the Industry 4.0 paradigm, such as sensors, the Internet of Things (IoT), and LoRaWAN (Long Range Wide Area Network) wireless connectivity, are being implemented for planning the efficient, safe, and sustainable performance of underground mine labor environments. This paper studies the implementation of an ecosystem composed of IoT sensors and LoRa wireless connectivity in a data-acquisition system, which eliminates the need for expensive cabling and manual monitoring in mining operations. Laying cables in an underground mine necessitates cable support and protection against issues, such as machinery operations, vehicle movements, mine operator activities, and groundwater intrusion. As the underground mine expands, additional sensors typically require costly cable installations unless wireless connectivity is employed. The results of this review indicate that an IoT LoRaWAN-based wireless sensor network (WSN) provides real-time data under complex conditions, effectively transmitting data through physical barriers. This network presents an attractive low-cost solution with reliable, simple, scalable, secure, and competitive characteristics compared to cable installations and manually collected readings, which are more sporadic and prone to human error. Reliable data on the behavior of the underground mine enhances productivity by improving key performance indicators (KPIs), minimizing accident risks, and promoting sustainable environmental conditions for mine operators. Finally, the adoption of IoT sensors and LoRaWAN wireless connectivity technologies provides information of the underground mine in real-time, which supports better decisions by the mining industry managers, by ensuring compliance with safety regulations, improving the productive performance, and fostering a roadmap towards more environmentally friendly labor conditions., The authors are indebted to the projects PID2021-126405OB-C31 and PID2021–126405OBC32 funded by FEDER funds A way to make Europe and Spanish Ministry of Economy and Competitiveness MICIN/AEI/10.13039/501100011033/. The financial support of the Research Department of the Catholic University of Temuco and the Civil Engineering Department of the University of Castilla-La Mancha is also appreciated., Peer Reviewed, Postprint (published version)

A new direct simulation of the cantilever erection method is presented in order to reduce the prestressing operations of stay cables. This new method introduces a direct approach for cable-stayed bridge construction using the cantilever erection method, employing independent finite element models. Prestressing forces are modeled as imposed strains based on the unstressed length concept to ensure the achievement of a specific target state post-construction (objective service stage, OSS). This facilitates faster simulation of the construction process, particularly for steel bridges and the predesign of concrete cable-stayed bridges, and enables direct simulation of construction stages and OSS achievement. The proposed method's efficacy is demonstrated through the analysis of a cable-stayed bridge., The authors are indebted to the projects PID2021-126405OB-C31 and PID2021-126405OB-C32 funded by FEDER funds A way to make Europe and Spanish Ministry of Economy and Competitiveness MICIN/AEI/10.13039/501100011033/ directed by Professor José Turmo. Authors are also indebted to the Secretaria d'Universitats i Recerca de la Generalitat de Catalunya for the funding provided through AGAUR (2020 DI 112)., Postprint (published version)

The development of low-cost structural and environmental sensors has revolutionized monitoring practices across numerous fields, enabling cost-effective solutions for infrastructure and building health assessment. However, a critical challenge associated with these sensors is their long-term durability and reliability. Surprisingly, despite the significant interest in these low-cost devices, the literature does not present any solutions for ensuring their long-term performance. To address this gap, this study proposes an innovative artificial intelligence-based approach for evaluating the long-term performance of low-cost inclinometers using a low-cost adaptable reliable anglemeter. This method automatically compares the inclinations of actual onsite measurements with predicted values under real environmental conditions. Over time, if the discrepancies between both measurements surpass a predefined statistical threshold, it may signal potential inaccuracies in the low-cost inclinometer, thereby suggesting the need for recalibration or presence of structural anomalies. The effectiveness and applicability of the proposed tool were demonstrated through a long-term study conducted on a real steel frame in Spain., This work was supported by the projects PID2021-126405OB-C31 and PID2021-126405OB-C32 funded by FEDER funds through the program “A Way to Make Europe” and by the Spanish Ministry of Economy and Competitiveness MICIN/AEI/10.13039/501100011033/. It also received support from the National Natural Science Foundation of China (Grant No. 52278313)., Peer Reviewed, Postprint (author's final draft)