Digital Twin for Civil Engineering Systems: An Exploratory Review for Distributed Sensing Updating.

We live in an environment of ever-growing demand for transport networks, which also have ageing infrastructure. However, it is not feasible to replace all the infrastructural assets that have surpassed their service lives. The commonly established alternative is increasing their durability by means of Structural Health Monitoring (SHM)-based maintenance and serviceability. Amongst the multitude of approaches to SHM, the Digital Twin model is gaining increasing attention. This model is a digital reconstruction (the Digital Twin) of a real-life asset (the Physical Twin) that, in contrast to other digital models, is frequently and automatically updated using data sampled by a sensor network deployed on the latter. This tool can provide infrastructure managers with functionalities to monitor and optimize their asset stock and to make informed and data-based decisions, in the context of day-to-day operative conditions and after extreme events. These data not only include sensor data, but also include regularly revalidated structural reliability indices formulated on the grounds of the frequently updated Digital Twin model. The technology can be even pushed as far as performing structural behavioral predictions and automatically compensating for them. The present exploratory review covers the key Digital Twin aspects-its usefulness, modus operandi, application, etc.-and proves the suitability of Distributed Sensing as its network sensor component.

Porosity of a Fast-Setting Mortar with Crystallization Admixture and Effect of a SA-PA Modification.

Air permeability measurements according to the Hagen-Poiseuille equation, Scanning Electron Microscopy (SEM) and mercury intrusion porosimetry (MIP) tests were conducted on samples of cementitious mortar at different curing times to study the correlation between the increased crystallization and their microstructure. The mortar samples were prepared with a commercial fast-setting premix containing calcium silicates and quartz. The average permeability coefficient (K) was 2.96 × 10-15 m2 after 3 days and decreased to 3.07 × 10-17 m2 after about one month. The continuous C-S-H nucleation in the capillary pores of the cement mortar changes their shape and improves the mortar's impermeability. The SEM images showed the development of crystals that refine the pore size distribution of the cement paste, with more of the smallest pores, and fewer of the largest, as demonstrated by the MIP measurements. Adding a superabsorbent polyacrylate (SA-PA) in the amount of 0.5% wt of dry powder, without adding any extra water, makes a mortar less fluid but not faster-setting. Twenty-four hours after mixing and casting, it is still plastic and, with time, the pore size distribution differs from that of standard mortar. Over time in air, permeability remains high, but in water it could be low due to swelling of SA-PA residues.

Structural Health Monitoring Based on Acoustic Emissions: Validation on a Prestressed Concrete Bridge Tested to Failure.

The increasing number of bridges approaching their design life has prompted researchers and operators to develop innovative structural health monitoring (SHM) techniques. An acoustic emissions (AE) method is a passive SHM approach based on the detection of elastic waves in structural components generated by damages, such as the initiation and propagation of cracks in concrete and the failure of steel wires. In this paper, we discuss the effectiveness of AE techniques by analyzing records acquired during a load test on a full-size prestressed concrete bridge span. The bridge is a 1968 structure currently decommissioned but perfectly representative, by type, age, and deterioration state of similar bridges in operation on the Italian highway network. It underwent a sequence of loading and unloading cycles with a progressively increasing load up to failure. We analyzed the AE signals recorded during the load test and examined how far their features (number of hits, amplitude, signal strength, and peak frequency) allow us to detect, quantify, and classify damages. We conclude that AE can be successfully used in permanent monitoring to provide information on the cracking state and the maximum load withstood. They can also be used as a non-destructive technique to recognize whether a structural member is cracked. Finally, we noticed that AE allow classifying different types of damage, although further experiments are needed to establish and validate a robust classification procedure.