Beam–column connections (joints) are one of the most critical elements which govern the overall seismic behavior of reinforced concrete (RC) structures. Especially in buildings designed according to previous generation codes, joints are often encountered with insufficient transverse reinforcement detailing, or even with no stirrups, leading to brittle failure. Therefore, externally bonded composite materials may be applied, due to the ease of application, low specific weight and corrosion-free properties. The present work assesses the seismic performance of insufficiently reinforced large-scale T beam–column connections with large and heavily reinforced beams. The joints receive externally bonded NSM X-shaped composite ropes with improved versatile continuous detailing. The columns are subjected to low normalized axial load, while the free end of the beam is subjected to transverse displacement reversals. Different failure criteria are investigated, based on the beam free-end transverse load, as well as on the joint region shear deformations, to critically assess the structural performance of the subsystem. The experimental investigation concludes that cyclic loading has a detrimental effect on the performance of the joint. Absence of an internal steel stirrup leads to earlier deterioration of the joint. The unstrengthened specimens disintegrate at 2% drift, which corresponds to 34 mm beam-end displacement, and shear deformation of the joint equal to 30 × 10⁻⁴ rad. The composite strengthening, increases the structural performance of the joint up to 4% drift which corresponds to 68 mm of beam-end displacement and shear deformation of the joint equal to 10 × 10⁻⁴ rad. The investigated cases of inadequate existing transverse reinforcement in the joint and light external FRP strengthening provide a unique insight into the required retrofits to achieve different levels of post-yielding displacement ductility under seismic loading at 2%, 3% and 4% drift. It allows for future analytical refinements toward reliable redesign analytical models.

The brittle failure of unreinforced masonry (URM) walls when subjected to in-plane loads present low shear strength remains a critical issue. The investigation presented in this paper touches on the retrofitting of URM structures with textile-reinforced mortar (TRM), which enables shifting the shear failure mode from a brittle to a pseudo-ductile mode. Despite many guidelines for applying composite materials for retrofitting and predicting the performance of strengthened structures, the application of TRM systems in masonry walls is not extensively described. A thorough retrospect of the literature is presented, containing research results relating to different masonry walls, e.g., bricks, cement, and stone blocks strengthened with TRM jackets and subjected to diagonal compression loads. The critical issue of this study is the failure mode of the retrofitted masonry walls. Available prediction models are presented, and their predictions are compared to the experimental results based on their failure modes. The novelty of this study is the more accurate failure mode prediction of reinforced masonry with TRM and also of the shear strength with the proposed model, Thomoglou et al., 2020, at an optimal level compared to existing regulations and models. The novel prediction model estimates the shear failure mode of the strengthened wall while considering the contribution of all components, e.g., block, render mortar, strengthening textile, and cementitious matrix, by modifying the expressions of the Eurocode 8 provisions. The results have shown that the proposed model presents an optimum accuracy in predicting the failure mode of all different masonry walls strengthened with various TRM jackets and could be taken into account in the regulations for reliable forecasting.

One of the most critical aspects in the seismic behavior or reinforced concrete (RC) structures pertains to beam–column joints. Modern seismic design codes dictate that, if failure is to occur, then this should be the ductile yielding of the beam and not brittle shear failure of the joint, which can lead to sudden collapse and loss of human lives. To this end, it is imperative to be able to predict the failure mode of RC joints for a large number of structures in a building stock. In this research effort, various ensemble machine learning algorithms were employed to develop novel, robust classification models. A dataset comprising 486 measurements from real experiments was utilized. The performance of the employed classifiers was assessed using Precision, Recall, F1-Score, and overall Accuracy indices. N-fold cross-validation was employed to enhance generalization. Moreover, the obtained models were compared to the available engineering ones currently adopted by many international organizations and researchers. The novel ensemble models introduced in this research were proven to perform much better by improving the obtained accuracy by 12–18%. The obtained metrics also presented small variability among the examined failure modes, indicating unbiased models. Overall, the results indicate that the proposed methodologies can be confidently employed for the prediction of the failure mode of RC joints.

This study examines Phase B of the GREENERGY project focusing on the seismic performance and structural health monitoring of a renovated single-story RC frame with brick masonry infills that received significant strategic structural interventions. The columns were confined with basalt fiber ropes (FR, 4 mm thickness, two layers) in critical regions, the vertical interfaces between infill and concrete were filled with polyurethane PM forming PUFJ (PolyUrethane Flexible Joints), and glass fiber mesh embedded in polyurethane PS was applied as FRPU (Fiber Reinforced PolyUrethane) jacket on the infills. Further, greenery renovations included the attachment of five double-stack concrete planters (each weighing 153 kg) with different support-anchoring configurations and of eight steel frame constructions (40 kg/m²) simulating vertical living walls (VLW) with eight different connection methods. The specimen was subjected to progressively increasing earthquake excitation based on the Thessaloniki 1978 earthquake record with peak ground acceleration ranging from EQ0.07 g to EQ1.40 g. Comprehensive instrumentation included twelve accelerometers, eight draw wire sensors, twenty-two strain gauges, and a network of sixty-one PZTs utilizing the EMI (Electromechanical Impedance) technique. Results demonstrated that the structure sustained extremely high displacement drift levels of 2.62% at EQ1.40 g while maintaining structural integrity and avoiding collapse. The PUFJ and FRPU systems maintained their integrity throughout all excitations, with limited FRPU fracture only locally at extreme crushing zones of two opposite bottom bricks. Columns’ longitudinal reinforcement entered yielding and strain hardening at top and bottom critical regions provided the FR confinement. VLW frames exhibited equally remarkably resilient performance, avoiding collapse despite local anchor degradation in some investigated cases. The planter performance varied significantly, yet avoiding overturning in all cases. Steel rod anchored planter demonstrated superior performance while simply supported configurations on polyurethane pads exhibited significant rocking and base sliding displacement of ±4 cm at maximum intensity. PZT structural health monitoring (SHM) sensors successfully tracked damage progression. RMSD indices of PZT recordings provided quantifiable damage assessment. Elevated RMSD values corresponded well to visually observed local damages while lower RMSD values in columns 1 and 2 compared with columns 3 and 4 suggested that basalt rope wrapping together with PUFJ and FRPU jacketed infills in two directions could restrict concrete core disintegration more effectively. The experiments validate the advanced structural interventions and vertical forest renovations, ensuring human life protection during successive extreme EQ excitations of deficient existing building stock.

One of the most critical components of reinforced concrete structures are beam–column joint systems, which greatly affect the overall behavior of a structure during a major seismic event. According to modern design codes, if the system fails, it should fail due to the flexural yielding of the beam and not due to the shear failure of the joint. Thus, a reliable tool is required for the prediction of the failure mode of the joints in a preexisting population of structures. In the present paper, a novel methodology for the derivation of analytical equations for this task is presented. The formulation is based on SHapley Additive exPlanations values, which are commonly employed as an explainability tool in machine learning. Instead, in the present paper, they were also utilized as a transformed target variable to which the analytical curves were fitted, which approximated the predictions of an underlying machine learning model. A dataset comprising 478 experimental results was utilized and the eXtreme Gradient Boosting algorithm was initially fitted. This achieved an overall accuracy of ≈$84\%$. The derived analytical equations achieved an accuracy of ≈$78\%$. The corresponding metrics of precision, recall, and the F1-score ranged from ≈$76\%$ to ≈$80\%$ and were close across the two modes, indicating an unbiased model.

Existing Mediterranean reinforced concrete buildings with masonry infills exhibit critical seismic vulnerabilities, yet real-time damage detection capabilities remain limited. This study validates a novel dense piezoelectric transducer (PZT) network concept for early damage detection in deficient RC structures under progressive seismic loading. A three-dimensional single-story RC frame with brick infills, representative of pre-Eurocode Mediterranean construction (non-ductile detailing, inadequate transverse reinforcement), was tested at serviceability limit states (SLSs) (Phase A) using a dynamic pushover approach with the 1978 Thessaloniki earthquake record, progressively scaled from EQ0.1g to EQ1.1g within the GREENERGY vertical forest renovation project. The specimen featured 48 PZTs using electromechanical impedance (EMI) methodology, 12 accelerometers, 8 displacement sensors, and 20 strain gauges. Progressive infill deterioration initiated at EQ0.5g while steel reinforcement remained elastic (max 2350 μstrain < 2890 μstrain yield). Maximum inter-story drift reached 11.37‰ with negligible residual drift (0.204‰). The PZT network, analyzed through Root Mean Square Deviation (RMSD), successfully detected internal cracking and infill-frame debonding before visible manifestation, validating its early warning capability. Floor acceleration amplification increased from 1.26 to 1.57, quantifying structural stiffness degradation. These SLS results provide critical baseline data enabling the Phase B implementation of sustainable vertical forest retrofitting strategies for aging Mediterranean building stock.