In this paper, we delve into the evolving landscape of vibration-based structural damage detection (SDD) methodologies, emphasizing the pivotal role civil structures play in society's wellbeing and progress. While the significance of monitoring the resilience, durability, and overall health of these structures remains paramount, the methodology employed is continually evolving. Our focus encompasses not just the transformation brought by the advent of artificial intelligence but also the nuanced challenges and future directions that emerge from this integration. We shed light on the inherent nonlinearities civil engineering structures face, the limitations of current validation metrics, and the conundrums introduced by inverse analysis. Highlighting machine learning's (ML) transformative role, we discuss how techniques such as artificial neural networks and support vector machine's have expanded the SDD's scope. Deep learning's (DL) contributions, especially the innovative capabilities of convolutional neural network in raw data feature extraction, are elaborated upon, juxtaposed with the potential pitfalls, like data overfitting. We propose future avenues for the field, such as blending undamaged real-world data with simulated damage scenarios and a tilt towards unsupervised algorithms. By synthesizing these insights, our review offers an updated perspective on the amalgamation of traditional SDD techniques with ML and DL, underlining their potential in fostering more robust civil infrastructures.

Additional energy demand is needed to accomplish the mega-projects of the Belt & Road Initiative (BRI). As energy consumption is one of the prime determinants of environmental degradation, the present study investigates the impact of energy inequalities on environmental degradation along with financial development. The entropy approach is applied to quantify the three energy consumption inequalities; average, between, and total energy consumption inequality respectively. The energy consumption inequality of BRI economies follows an uprising temporal trend. The estimates reveal that East Asia and South Asia have the highest and lowest energy consumption inequality among the BRI regions. Within regions, it is found that Central Asia has the lowest, and East Asia has the highest energy inequality among the BRI regions, respectively. Based on bootstrapping, the generalized least square (GLS) is applied to quantify the impact of energy consumption inequalities on environmental degradation along financial development. The energy inequalities have a statistically positive impact on environmental degradation in BRI regions, East Asia, Central Asia, the Middle East and North African region (MENA), and Southeast Asia respectively. In contrast, South Asian economies are sustaining environmental quality despite the energy consumption inequalities. Financial development also has a significantly major impact on environmental degradation in BRI, and its regions except for Central Asia, and MENA.

This study focuses on using various machine learning (ML) models to evaluate the shear behaviors of ultra-high-performance concrete (UHPC) beams reinforced with glass fiber-reinforced polymer (GFRP) bars. The main objective of the study is to predict the shear strength of UHPC beams reinforced with GFRP bars using ML models. We use four different ML models: support vector machine (SVM), artificial neural network (ANN), random forest (R.F.), and extreme gradient boosting (XGBoost). The experimental database used in the study is acquired from various literature sources and comprises 54 test observations with 11 input features. These input features are likely parameters related to the composition, geometry, and properties of the UHPC beams and GFRP bars. To ensure the ML models' generalizability and scalability, random search methods are utilized to tune the hyperparameters of the algorithms. This tuning process helps improve the performance of the models when predicting the shear strength. The study uses the ACI318M-14 and Eurocode 2 standard building codes to predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams. The ML models' predictions are compared to the results obtained from these building code standards. According to the findings, the XGBoost model demonstrates the highest predictive test performance among the investigated ML models. The study employs the SHAP (SHapley Additive exPlanations) analysis to assess the significance of each input parameter in the ML models' predictive capabilities. A Taylor diagram is used to statistically compare the accuracy of the ML models. This study concludes that ML models, particularly XGBoost, can effectively predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams.

Structural materials sustainability is gaining popularity across the globe at present. Reusing natural resources, building, demolition debris, and solid waste are the most apparent tools to make construction more environmentally friendly. Traditional concrete is believed to be less durable, stronger, environmentally friendly, and socially and commercially feasible than industrial waste concrete. The evolution of non-destructive testing (NDT) across time has not been investigated in depth by researchers. An experimental study was carried out to propose the use of non-destructive mechanisms that would enable us to assess concrete’s compressive strength without causing destruction. Varying quantities of industrial waste (coal bottom ash (CBA) and waste glass sludge (WGS)) were incorporated to cast concrete prisms (150 mm × 150 mm × 150 mm). The results obtained helped us to establish relationships between the compressive strength of concrete and the Schmidt hammer rebound value, as well as the ultrasonic pulse velocities. Microstructural analysis showed that incorporating 10% of CBA and WGS improved the porosity of concrete specimens, which shows the applicability of these industrial wastes as partial cement replacements. Scanning electron microscopy (SEM) showed traces of calcium alumino-silicate hydrate (C-A-S-H), portlandite and C-S-H, which indicates the binder characteristics of CBA and WGS. The concept of the response surface approach (RSM) for optimizing cement and industrial waste substitution was validated by the polynomial work expectation. The model was statistically significant when the fluctuation of ANOVA was analyzed using a p value with a significance level of 0.05. The study results show that the usage of 15% CBA and 10% WGS as a cementitious additive and cement replacement has the potential to increase the strength of concrete significantly.

Sustainable development in structural materials is currently getting attention all around the world. Solid waste, building and demolition waste, natural resources, and their reuse are the most obvious strategies for achieving sustainability in the construction industry. Solid waste human hair fiber (HHF) with a diameter of 70 µm and a length of 30–40 mm is used as a fiber, having a dosage of 0%, 1%, 1.5%, 2%, 3%, 4%, and 5%, while silica fume (SF) with a dosage of 0%, 5%, 10%, 15%, 20%, 25%, and 30% is used as a cement substitute. A drop of 50 mm to 75 mm slump was witnessed for the water–cement ratio used in the M20 mix design of concrete. The concrete’s mechanical properties, such as compressive, split tensile, and flexural strength, were determined after 28 days of water curing. The concept of the response surface methodology (RSM) for optimizing human hair fiber concrete (HHFC) and SF substitution was used, which was validated by the polynomial work expectation. The model is statistically significant when the fluctuation of the analysis of variance (ANOVA) is analyzed using a p-value with a significance level of 0.05. The test results showed that the use of 2% human hair as fiber and 15% SF as a cementitious additive or cement replacement considerably improved the strength of concrete. The compressive, flexural, and split tensile strengths of HHFC improved by 14%, 8%, and 7%, respectively, which shows the significance of human hair and the partial replacement of cement with SF. Moreover, SEM analysis was carried out to study the microstructure of the concrete matrix.

Due to the complexity of underground environmental conditions and operational incidents, advanced and accurate monitoring of the underground metro shield tunnel structures is crucial for maintenance and the prevention of mishaps. In the past few decades, numerous deep learning-based damage identification studies have been conducted on aboveground civil infrastructure. However, a few studies have been conducted for underground metro shield tunnels. This paper presents a deep learning-based damage identification study for underground metro shield tunnels. Based on previous experimental studies, a numerical model of a metro tunnel was utilized, and the vibration data obtained from the model under a moving load analysis was used for the evaluation. An existing deep auto-encoder (DAE) that can support deep neural networks was utilized to detect structural damage accurately by incorporating raw vibration signals. The dynamic analysis of a metro tunnel FEM model was conducted with different severity levels of the damage at different locations and elements on the structure. In addition, root mean square (RMS) was used to locate the damage at the different locations in the model. The results were compared under different schemes of white noise, varying levels of damage, and an intact state. To test the applicability of the proposed framework on a small dataset, the approach was also utilized to investigate the damage in a simply supported beam and compared with two deep learning-based methods (SVM and LSTM). The results show that the proposed DAE-based framework is feasible and efficient for the damage identification, damage size evaluation, and damage localization of the underground metro shield tunnel and a simply supported beam with comparison of two deep models.