Affordability, Long-term warranty, scalability, as well as continuous decline in the LCOE (levelized cost of electricity) of PV (Photovoltaic) in many nations, are largely responsible for the current enormous up thrust in global installation of solar PV modules, at residential roof-top as well as utility-scale systems. Also, as the world’s energy portfolio evolves toward cleaner energy sources, PV deployment is anticipated to maintain this increasing trend. Despite this, these PV modules are subjected to a broad range of climatic conditions in outdoor application, regardless of the PV module material/type and technology used. These modules are frequently subjected to high chemical, photochemical, and thermomechanical stress because of the reason that these PV modules are exposed to extreme environmental conditions. In addition to manufacturing flaws, these circumstances have a greater impact on the aging rate, flaws, and degradation of PV modules. As a result, different investigations on PV dependability and degradation mechanisms have been conducted recently. These studies not only shed light on how the performance of PV modules deteriorates with time, but most importantly, they provide useful input for future advancements in PV technology and performance forecasting for more accurate financial modeling. Due to this, it is crucial to quickly and accurately identify and classify the degradation modes and mechanisms caused by manufacturing flaws and environmental factors in order to reduce the likelihood of failure and the risks that go along with it. Visual examination, EPM (Electrical-Parameter-Measurements), imaging techniques, and latest data-driven techniques have all been suggested and used in the literature to assess or describe the deterioration signatures and mechanisms/pathways of PV modules. This report offers a critical analysis of recent research that examined the performance reliability and deterioration of solar PV systems. The objective is to identify important topics, advance the state of the art, and offer well-considered suggestions for upcoming research, especially in the field of data-driven analytics. Data-driven analytical methods, such as DL (Deep Learning) ML (Machine Learning) models, have astounding computational abilities to process large amounts of data, with diverse features, and with minimal computation time. This contrasts with visual inspection and statistical approaches, which are more time-intensive and require significant human experience. They can therefore be used to evaluate the performance of a module in manufacturing, field deployments, and lab settings. DL and ML can observe erratic patterns and draw useful conclusions in the classification, diagnosis, and prediction of PV performance degradation signatures thanks to the enormous size of PV module installations, particularly in systems for utility scales, and the large datasets produced in terms of features from imaging and EPM data. In terms of methodology, datasets, characterization techniques, accelerated testing procedures, feature extraction mechanisms, classification procedures, analysis and comparison and critical analysis of solar PV deterioration models. Lastly, we briefly describe any potential research gaps and briefly list some suggestions for additional research.

The recent failure of buildings because of punching shear has alerted researchers to assess the reliability of the punching shear design models. However, most of the current research studies focus on model uncertainty compared to experimentally measured strength, while very limited studies consider the variability of the basic variables included in the model and the experimental measurements. This paper discusses the reliability of FRP-reinforced concrete slabs’ existing punching shear models. First, more than 180 specimens were gathered. Second, available design codes and simplified models were selected and used in the calculation. Third, several reliability methods were conducted; therefore, three methods were implemented, including the mean-value first-order second moment (MVFOSM) method, the first-order second moment (FOSM) method, and the second-order reliability method (SORM). A comparison between the three methods showed that the reliability index calculated using the FOSM is quite similar to that using SORM. However, FOSM is simpler than SORM. Finally, the reliability and sensitivity of the existing strength models were assessed. At the same design point, the reliability index varied significantly. For example, the most reliable was the JSCE, with a reliability index value of 4.78, while the Elgendy-a was the least reliable, with a reliability index of 1.03. The model accuracy is the most significant parameter compared to other parameters, where the sensitivity factor varied between 67% and 80%. On the other hand, the column dimension and flexure reinforcement are the least significant parameters compared to other parameters where the sensitivity factor was 0.4% and 0.3%, respectively.

Compressed stabilized earth blocks (CSEBs) offer a cheaper and environmentally sustainable alternative to traditional building materials for construction. In addition to addressing waste disposal difficulties, the inclusion of waste additives may improve the characteristics of compressed earth blocks (CEBs). This article attempts to outline the findings of researchers who have utilized the various manufacturing processes and investigated the influence of binders and fibers on the properties of CEBs. A systematic search of Web of Science and Scopus electronic databases for works on soil blocks published between 2012 and 2022 yielded 445 articles, while reports, case studies, conference papers, and non-English articles were omitted. Keywords such as “Soil blocks”, “Earth bricks”, and others were used to identify eligible studies. This study has been segmented into five sections, including a descriptive examination of articles and authors who have investigated soil blocks, a comparative analysis based on their manufacturing processes, and physical, mechanical, and durability aspects of the CSEBs, which were analyzed to determine the impact of additives. The PRISMA 2020 standards were followed in the evaluation of each record, which resulted in the identification of 61 articles that were pertinent to the study’s objective. The comparative analysis of the articles reveals that the binders were more significant in improving the compressive strength, cyclic wetting-drying and erosion (durability) aspects of the soil blocks, while fibers were effective in enhancing their flexural and thermal performance. The literature review indicates that if the minimum permissible limits are met, waste materials have the potential to partially replace the soil. In addition, this study suggests establishing standardized manufacturing norms and testing protocols to ascertain the quality and safety of CSEBs used in construction. However, this study is constrained by the limited databases used, governed by keywords, electronic resources and timeframe that could be used as research avenues in the future.

Terrorist bombing-induced casualties are not only related to immediate fatalities but also to structural deterioration, damage, or even collapse that might occur and may lead to tremendous loss of life. Efficient assessment of blast-induced structural damage following explosion events is becoming a growing problem in modern societies. An attempt based on machine learning is made in this study to anticipate structures’ responses and the associated structural damage to reinforced concrete (RC) buildings exposed to extremely short-duration explosive loads. A program is developed to generate a set of analytically derived data for nonlinear building models subjected to explosive loads. Common machine learning models and Python libraries were utilized during the development of our program implementation to learn from a dataset. The latter has different features or input parameters, such as the amount of explosive charge, the distance from the building, fundamental period, and the building’s mass and rigidity, as well as the soil type. Our database is thus used, along with our regression-and-classification based implementations, to generate an output index that estimates and categorizes the state of damage based on the several most-important parameters of the explosion exposure. In the input database, the state of damage, based on the values of captured damage indices, is classified into one of four cases. Our code efficiently predicts those cases using a model that learns from the database. The prediction rates of the presented model reach an overall high accuracy. Therefore, the proposed model provides an accurate prediction of the level of structural damage by using the computed damage indices.