The enduring resilience of Roman infrastructure, exemplified by the Tiberius Bridge in Rimini—completed in the 1st century CE and remaining structurally sound after over two millennia—has long drawn scholarly attention. This study re-evaluates Roman construction methodologies with a particular focus on opus caementicium (Roman concrete) encased within durable permanent facings such as opus quadratum, opus incertum, and opus latericium. Central to this longevity was the use of pozzolanic binders, which underwent prolonged hydration reactions, enabling continued strength development over extended timescales—markedly contrasting with contemporary hydraulic cements engineered for rapid early-age strength gain. A comparative analysis is conducted between ancient Roman materials and modern high-performance cementitious composites, including High-Performance Concrete (HPC), Ultra-High Performance Concrete (UHPC), and Engineered Cementitious Composites (ECC). Contemporary practices are frequently guided by design codes such as Eurocode, which, while structurally robust, tend to prioritize short-term performance metrics. To bridge this gap, a hybrid construction strategy is proposed wherein additive manufacturing is employed to produce permanent structural formworks that mimic the load-bearing and protective functions of Roman facings. This approach enables the use of modern slow-maturing binders within digitally fabricated enclosures, thus integrating ancient durability principles into automated, scalable workflows. By reconciling historical construction insights with advances in modern materials science and digital fabrication, a new paradigm is introduced for designing infrastructure with service lives far exceeding the conventional 50–100 year design horizon. The implications of such an approach extend to both sustainability and resilience, offering a technically viable and historically informed route toward ultra-durable infrastructure in the face of evolving environmental and operational challenges.

The fundamental mechanical properties and constrained recovery behavior of two domestically produced Fe-Mn-Si shape memory alloys (SMAs) (Fe-16.86Mn-4.5Si-10.3Cr-5.29Ni-0.08C and Fe-17.6Mn-4.5Si-3.22Cr-2.96Ni-0.28C-1.45V) were investigated with specific reference to their potential application in bridge strengthening. Uniaxial tensile tests, differential scanning calorimetry (DSC), and thermal expansion measurements were conducted to determine the elastic modulus, transformation stress, transformation temperatures, and thermal expansion characteristics. The alloy containing vanadium exhibited a higher elastic modulus and a higher transformation stress than the vanadium-free alloy. In addition, the presence of vanadium significantly reduced the width of the transformation temperature interval, which is advantageous for temperature control during practical activation. Constrained recovery tests showed that the recovery stress increased with increasing activation temperature and reached a maximum at a pre-strain of approximately 6%. The level of pre-applied stress had only a minor effect on the final recovery stress, indicating a stable and controllable recovery behavior under engineering conditions. These results provide both experimental data and a mechanical basis for the use of domestically produced Fe-Mn-Si shape memory alloys in the active strengthening of civil engineering structures.

The expensive energy prices and sustainability goals are driving the precision manufacturing facilities to stop their periodic energy reporting to full-time, machine-level reporting that can provide insights into where energy is used, anticipate future-demand and observe unusual behavior in the CNC machining and digital fabrication processes. This paper creates a real-time smart power dashboard, which combines power measurement and production-aware processing to facilitate actionable energy governance on the shop floor. This workflow coordinates time-stamped power data (and optional machine context), cleanses and rebuilds windows of features, and uses a multi-model forecasting layer (autoregressive integrated moving average, additive time-series decomposition, gradient-boosted regression, and long short-term memory (LSTM)) to make short-horizon predictions. A dual protocol based on standardized deviation monitoring and isolation-based outlier detectors detect abnormal consumption with energy windows being clustered into repeatable profiles using clustering to facilitate benchmarking across machines and shifts. The prototype testing demonstrates that the forecasting layer has a best mean absolute percentage error (MAPE) of 8.9, the clustering operation has a conspicuous separation with a silhouette score of 0.742 and the anomaly detection has a precision of 95.7 and a false positive of 2.8 at minimal computing power. Such findings show that the dashboard, as suggested, can be used to provide reliable forecasting, interpretable profiling and low noise alerting that can be used in real-time monitoring. The strategy offers deployable analytics structure that converts raw power streams into decision-ready data and facilitates undertakable efficiency steps by means of energy per job, peak-load exposure, and share of non-productive energy indicators.