This study focuses on landslide susceptibility assessment of the area between Güzelyalı and Lapseki (Çanakkale, Türkiye) by using logistic regression, artificial neural network (ANN) and support vector machine methods. Nine input parameters such as topographic elevation, lithology, slope, land use, aspect, curvature, distance to streams, TWI, and NDVI were selected as the landslide conditioning parameters. The frequency ratio values were also calculated for the parameters and their subclasses and were assigned to express all continuous and categorical input parameters in the same scale for the considered prediction models. In addition, sensitivity (Recall), accuracy, precision, kappa indexes, F 1-score and receiver operating characteristic based on area under curve approach were calculated to assess the performances of the so produced landslide susceptibility maps. Considering all performance indicators, the most successful model was revealed as the map produced by ANN model. Producing such maps, testing their performances and using them into the practice, sustainability can be achieved in regional planning, land use and urban development stages. More importantly, a fundamental step will be taken for future works such as hazard and risk assessments in the region.
This paper proposes a quantification and location damage detection model for plane frames with flexible connections considering simultaneous damage in members and connections. A two-phase method is produced to decrease the computational efforts considerably. The first phase presents proposed damage indicators depending on the residual force vector concept to obtain the expected damaged members and connections separately. The second phase considers damage quantification as a variable into the whale optimization algorithm (WOA) to obtain the optimum damage quantification value of the expected damaged members and connections attained in the first phase. WOA is a recent promising algorithm that has shown excellence in optimizing structural problems. Moreover, the biogeography-based optimization (BBO) is used in the second phase to compare the WOA and BBO algorithms. As it is obvious, the first phase diminishes the search space in the second phase, which in turn leads to a substantial reduction in computational efforts. The model is applied on three plane frame examples with flexible beam-to-column connections considering different damage scenarios. Results have proved the proficiency of the proposed method to accurately detect the quantification and location of damage with minimal computational efforts, and the superiority of WOA in comparison to BBO.
Worldover, seismic design of buildings typically follows a prescriptive approach in which designers conform to a series of prescriptive code requirements in terms of both analysis and design procedures. Even though this prescriptive seismic design approach is time-tested and easily understandable by structural designers. In the recent past, performance-based seismic design has started to gain traction among structural designers. The performance-based seismic design allows designers to set performance objectives and design buildings to meet the targeted performance criteria. Due to its flexible nature, performance-based design has proven extremely useful for critical and lifeline buildings like hospitals and tall buildings. With a focus placed on performance objectives, designers utilizing performance-based seismic design are proficient in designing code exceeding buildings efficiently. Despite these cited benefits, performance-based design is still considered an uncommon practice in structural design, particularly in the developing countries. Hence, the present study aims to provide an overview and framework to practice performance-based seismic design. This work identifies and discusses the key differences between the prescriptive- and performance-based seismic design methods and also addresses the significance, application and implementation of performance-based seismic design of buildings. This paper makes an original contribution to the literature through a critical review of how the performance-based design withholds the opportunity to elevate the role of the structural engineers to which they are informed members of the community, where the structures they create not only perform according to design prescriptions, but also perform according to the needs of the owners, engineers, and society.
Soil liquefaction is a substantial seismic hazard that endangers both human life and infrastructure. This research specifically examines the occurrence of soil liquefaction events in past earthquakes, with a special emphasis on the 1964 Niigata, Japan and 1964 Alaska, USA earthquakes. These occurrences were important achievements in the comprehension of harm caused by liquefaction. Geotechnical engineers often use in-situ experiments, such as the standard penetration test (SPT) to evaluate the likelihood of liquefaction. The attraction for this option arises from the difficulties connected in acquiring undisturbed samples of superior quality, as well as the related expenses. Geotechnical engineering specialists choose the deterministic framework for liquefaction assessment because of its clear mathematical approach and low needs for data, time, and effort. This work emphasises the need of integrating probabilistic and reliability methodologies into the design process of crucial life line structures to enable well-informed risk-based decision-making. The objective of this project is to create models that use deterministic, probabilistic, and reliability-based methods to evaluate the likelihood of soil liquefaction. The work presents a new equation that combines Bayes conditional probability with Genetic Programming (GP). and also in study is to identify the most suitable method for liquefaction analysis based on factor of safety and Performance Fitness Error Metrics (PFEMs), Rank analysis, Gini index, etc. The information provided in study data include soil and seismic characteristics, including the corrected blow count (N1)60cs , fines content (FC), mean grain size ( D50 ), peak horizontal ground surface acceleration ( amax ), earthquake magnitude (M), and CSR7.5 . The parameters are derived from the SPT measurements conducted at many global locations, together with field performance observations (LI) and probability of liquefaction has been assessed through the use of Gini Index (GI). A comparison was made between the novel methodology and the techniques proposed by Juang et al. (J Geotech Geoenviron Eng 128:580–589, 2002), Toprak et al. in: Proc., 7th US–Japan Workshop on Earthquake Resistant Design of Lifeline Facilities and Countermeasures against Liquefaction, Buffalo, 1999), and Idriss and Boulanger (J Soil Dyn End Earthq Eng 26:115–130, 2006) status of case history data using Performance Fitness Error Metrices. The comparison included employing a confusion matrix for binary classification and doing a score analysis based on factor ranking. The proposed model exhibited superior performance, as the outputs of the constructed model increased for all positive factors and decreased for negative indicators.
This study investigated the impacts of treated wastewater (TWW) on concrete mechanical and durability properties, addressing the growing freshwater demand in the concrete production industry amid water scarcity. This research filled a literature gap by employing an accelerated corrosion test using the impressed voltage technique. The investigation involved diverse experiments, including workability, setting time, mortar compressive strength, density, porosity, water absorption, pH value, ultrasonic pulse velocity, and half-cell potential tests. Results indicated negligible deviations compared to the control group. Both concrete groups, mixed with TWW (WT) and mixed and cured with TWW (WW), experienced a minor reduction of less than 10% in compressive and splitting tensile strengths compared to the control group, with slight exceptions. The 7-day compressive strength for the WT and WW groups reached 91.41% and 90.63%, respectively, meeting the ASTM C1602 benchmarks. Notably, after six months of curing, compressive and splitting tensile strengths markedly improved, nearly aligning with the control group. While TWW’s characteristics met the ASTM C1602 criteria for mixing water, with a higher chloride content of 276.7 mg/l compared to the tap water, corrosion results showed higher rates (23.83% for WT and 24.67% for WW groups) compared to the control group, accompanied by an earlier crack appearance and increased rebar mass loss. The TWW utilization for concrete curing minimally affected the results compared to the WT group, suggesting its suitability for curing purposes. Overall, the TWW could be satisfactorily utilized in concrete production, although caution is advisable in aggressive environments where concrete is exposed.
Fiber-reinforced geopolymer concrete (GPC) is a green building material prepared from solid waste. At present, the research on its static and dynamic properties is relatively sufficient, but the mechanism of the effect of fiber on the mechanical properties of GPC after high temperature has not yet formed a perfect system. Therefore, this paper first analyzes the high temperature degradation mechanism of GPC under different material components. Then, the strengthening effect of two kinds of thermal characteristic fibers (steel fiber and polyvinyl alcohol fiber) on the mechanical properties of GPC after high temperature was discussed. Finally, a prediction model of compressive and tensile mechanical properties of GPC after high temperature considering the influence of fiber is proposed. The research results provide a reference for promoting the application of GPC in the field of structural engineering.
In this study, two GIS-based analytical methods, Frequency ratio (FR) and Shannon Entropy (SE), were evaluated for the mapping of the Chamoli region in Uttarakhand, India, for estimating the area's landslide susceptibility. There is a lot of on-going and proposed infrastructure projects in the area due to which, there is a surge in tourism. Thirteen landslide causative factors, namely rainfall, geology, elevation, slope, aspect, curvature, topographic wetness index (TWI), stream power index (SPI), distance to roads, distance to faults/lineaments, distance to river, lithology, annual rainfall, land cover, and geology, are taken into account in this research study. The landslide inventory of 200 landslides was prepared from Bhukosh Portal by Geological Survey of India in point shape file format which are shown as the locations of landslide points. From the findings of this study, two landslide susceptibility maps were created, and they were assessed by using Area Under Curve (AUC) approach of the Receiver Operator Characteristics (ROC) curve by plotting the success rate curve (SRC) and prediction rate curve (PRC). The AUC results showed that the frequency ratio (AUC = 0.923 for SRC and 0.883 for PRC) model is better than the Shannon entropy model (AUC = 0.920 for SRC and 0.877 for PRC) for the predicting landslide susceptibility due to higher AUC values. The maps generated from this research can be extremely useful for the engineers from similar areas for ensuring a safe, disaster resilient infrastructure against a natural disaster like landslides.
The behavior of strip footings under varying soil conditions is a crucial research area in foundation engineering. In this study, the effect of weak layers on the bearing capacity of strip footings resting on a layered soil mass was investigated through small-scale model tests. The experimental program consisted of laboratory tests performed on a rigid strip footing situated on a sand mass foundation. The results indicated that even a thin weak layer can significantly decrease the ultimate bearing capacity of the soil-foundation system. The extent of this effect was shown to be dependent on the thickness of the weak layer. Asymmetrical overburden loading were also found to be a critical factor in achieving the ultimate bearing capacity of the foundations. The primary aim of this research was to evaluate the general behavioral pattern of strip footings resting on sand masses with weak layers and to quantify the effect of various parameters on the ultimate bearing capacity. The comparison of experimental with numerical and analytical analysis results confirmed the similar trends of these two methods under different conditions of the model tests. The results suggest that by increasing the overburden amount, the bearing capacity of the foundation can be improved.
Performance Based Plastic Design (PBPD) method overcomes implicit consideration of inelastic behavior of structural system designed by existing force/strength based approach. The effectiveness of PBPD for various Lateral Load Resisting Systems (LLRS) is ascertained through research in the past decade. However, the validated PBPD designs of these LLRS are still based on elastic design spectra which lead to a conservative estimate of seismic demands. Hence, there is need to develop the inelastic constant ductility response spectra for such structural systems. This paper aims to propose use of inelastic constant ductility response spectra for PBPD of Steel Moment Resisting Frame (SMRF). 20 Large Magnitude Small Radial -distance (LMSR) ground motion records are used to develop these inelastic constant ductility response spectra for various PBPD cases of 3 and 9-storey SAC-steel project buildings. The seismic performance of these PBPD cases using both inelastic constant ductility response spectra, as well as elastic design spectra, are evaluated by carrying out Nonlinear Static Pushover Analysis (NSPA) and Nonlinear Time History Analysis (NTHA). The results show that PBPD cases using inelastic constant ductility response spectra are more efficient and effective in achieving the target ductility and pre-selected yield mechanism as the performance objectives over elastic design spectra.
Scientific and effective evaluation is the true embodiment of the implementation effect of the rural revitalization strategy, which can highlight the problems existing in rural revitalization. Constructing a scientific and reasonable evaluation index system is the premise and foundation of evaluation, which ensures the effective implementation of the rural revitalization strategy. This paper synthesized the national rural revitalization strategic plan, Chongqing rural revitalization strategic action plan, and the existing research results. After combination with the Delphi method, a rural revitalization evaluation index system for Chongqing municipality was constructed, which included a total of six first-level indicators, 14 second-level indicators and 36 third-level indicators. The data of the third agricultural census of Chongqing municipality were then used for the empirical research in which the analytic hierarchy process, the entropy weight method and the comprehensive evaluation model were combined to carry out the empirical analysis of Chongqing municipality. The results showed that the overall level of rural revitalization in Chongqing municipality is not high; there are obvious regional differences in Chongqing municipality in the effectiveness of rural revitalization, and the effect of rural revitalization in Chongqing municipality is uneven. Finally, based on the research results, some practical and feasible recommendations are proposed.
The current investigation focused on evaluating the punching shear strength of flat-edged slabs reinforced with basalt bars. Nine slabs measuring 2000 mm × 1200 mm × 150 mm were tested, each featuring a 250 mm × 250 mm square column positioned at the middle free edge. In Group I, the basalt reinforcement ratio ranged from 1.5 to 2.5 times the balanced reinforcement ratio, with zero eccentricity of applied load. Group II maintained the same reinforcement ratio but introduced an eccentricity ratio of the load to column cross section depth e/h with 0.4 toward and perpendicular to the free edges of the slabs. Group III varied the e/h to 0.8. The study demonstrated the effects of basalt reinforcement ratio, eccentricity of the applied load, mode of failure, strains, and crack patterns for the specimens. Additionally, an equation for the punching shear capacity of flat slabs was proposed. Furthermore, the experimental results and the proposed equation were compared with codes and other equations that researchers use to assess punching shear capacity.
A potential environmentally friendly building material is concrete containing manufactured sand. The utilization of manufactured sand in the production of high-performance concrete (HPC) has been gaining popularity. A commonly used mechanical parameter for the design of HPC structures is the splitting tensile strength. Due to the complexity, expense, and time-consuming nature of conducting tensile testing, numerous researchers are interested in creating a straightforward yet precise way to forecast the value of this feature. This study presents an effective application of machine learning models for predicting the tensile strength of HPC. Five advanced predictive algorithms, namely K-nearest neighbors, Adaptive Boosting (AdaBoost), Random Forest Regressor, Extra Tree Regressor (ETR), and Voting Regressor, were utilized. The performance of the developed ML models was then evaluated using distinct performance indexes. The evaluation shows that the ETR model’s estimated results are closer to the experimental results than the other four models. This suggests that the ETR model accurately estimates split tensile strength. Conversely, Shapley additive explanations (SHAP) offer comprehensive metrics for evaluating both the significance of features and the influence of a variable on a given prediction. It is noteworthy that the SHAP interpretations aligned with the typical tensile behaviour of the concrete, thus reinforcing the causal relationship between the machine learning predictions and the actual outcomes.
An experimental study was carried out on nine reinforced concrete beams with insufficient shear strength to evaluate the shear behavior and performance of beams. One of the beams was not strengthened and designated as the reference beam. Six of them were strengthened in both flexure and shear using two types of composite materials—carbon and glass fiber-reinforced polymer (CFRP and GFRP). Two of them were strengthened with U-wrapped and side-bonded CFRP in only shear. The experimental parameters involve the type of FRP, the number of FRP layers, the clear distance between two adjacent FRP shear strips and the strengthening configuration (completely wrapped, U wrapped and side bonded). The experimental results revealed that (i) applying the FRP sheets for strengthening improved the load carrying and deflection capacities of the tested beams by an average of 143% and 170%, respectively, (ii) strengthening with two layers of CFRP shear strips could transform the failure mode from shear to more ductile flexural failure, (iii) the load-carrying capacity of GFRP-strengthened beams was on the average 34% less than the capacities of those strengthened with CFRP and (iv) using additional CFRP layers for shear strengthening did not provide a proportional increase in strength. The experimental FRP contribution to the shear strength was estimated using equations proposed by various codes and researchers, and the prediction accuracy of the equations was evaluated statistically.
Significant slope destabilisation may become more likely due to the speed at which urbanisation is occurring, as well as the growing necessity for geoengineering initiatives or the growth of the road network. Slope stability analysis is done to lower the risk of landslides and slope failures. The study area, Kalimpong, is well-known for its lush greenery and stunning views and is situated in the Eastern Himalayas. However, it also constantly confronts the risk of landslides because of its rugged topography, potential seismic zone, and heavy monsoon rains. In this study, the results of the factor of safety computed by limit equilibrium (conventional) method have been compared analytically using computational intelligence and machine learning methodologies for both dry and saturated conditions under dynamic loading. Conventional machine learning techniques are combined with seven prediction models. The following algorithms have been chosen for slope stability analysis: support vector machine, k -nearest neighbours, decision tree, random forest, logistic regression, AdaBoost, and gradient boosting. Random cross-validation is used to assess each model's dependability. The stability condition is the result of the random selection of seven parameters: cohesiveness, unit weight, slope height, angle of the slope, internal friction angle, horizontal and vertical pseudo-static coefficient. Moreover, the coefficient of variation method is employed to assess the importance of every indicator in forecasting slope stability. As per the sensitivity analysis, slope stability is primarily affected by cohesiveness. With an average classification accuracy of 0.878, ensembling approach SVM-Boost demonstrates the best prediction abilities among the models tested using multifold cross-validation. The accuracy ratings of SVM and AdaBoost were 0.865 and 0.834, respectively. When combined with SLOPE/W advances, novel SVM-Boost exhibits the highest exactitude, hegemony, and best outcomes in slope stability prediction. Future earthquakes, strong rainfall, and human activity could cause the slope to collapse. The outcome demonstrates machine learning's enormous potential for enhancing slope stability assessments and provides a means of raising the effectiveness and safety of slope management.
Using only activated carbon as a phenol adsorbent is not efficient in a soil system, so a reliable carrier is required to prevent soil distribution issues. This issue needed to be appropriately addressed by the literature. Hence, the present study aims to fill this research gap by using filter geotextile along with granular activated carbon within a continuous column system (GGAC filter) containing sandy soil. Three crucial variables were considered in the experimental program, including the initial concentration of phenol (100–500 mg/L), adsorbent dosage (5–20 gr), and pH (2–12). Generally, results show that the GGAC filter can efficiently adsorb the contaminants, which is in good agreement with the predicting formulations of Thomas, Adams–Bohart, and Yoon–Nelson models. Moreover, statistical results indicate that the maximum adsorption capacity can be obtained at the initial concentration of 458.8 mg/l, the adsorbent dosage of 5.5 g, and the pH of 7. Results also showed that the concentration variable is the most influential parameter.
Travel well-being (TWB) is an important aspect in human life, particularly in travel behavior. This study quantifies TWB using travel, personal and family attributes by employing structural equation modeling (SEM) approach. For this purpose, recent American time use survey (ATUS) dataset consists of 44 parameters was utilized for analysis. The outer weight, Tucker–Lewis index (TLI), and comparative fit index (CFI) were estimated for validation of SEM model performance. Based on results, Whites and Asian people were happy than Black and Latino people. Those who walk or ride their bikes are cheerful. Physically unwell people have lower TWB than healthy people. Compared to their spouses who are present, immigrants are happier. Higher family earnings are associated with happier people than lower family incomes. Those in central cities are the healthiest, followed by those in medium cities, large cities, and small cities. Those who are employed are happier than those who are unemployed. Overall, the findings showed a high correlation between identified human factors and TWB, as well as travel behavior in terms of mode of transportation, purpose of travel, and duration of travel. Furthermore, it was discovered that a person’s physical condition and self-evaluation of living beings had a substantial impact on TWB. Based on study findings, this study addressed potential ways for enhancing TWB when traveling. Research on the human factor of TWB could help decision-makers who create useful strategies to improve passenger experiences. Thus, SEM modeling is a useful approach to identify human factors relationship with TWB for deriving influential factors.
The effect of convergence and divergence ratios of cross sections along a river in a bend of 180° on the changes to bed topography was investigated using an SSIIM model. The results indicated that the changes in convergence and divergence ratios in the channel bend significantly affected the scour depth and sedimentation height. With the increase in the convergence ratio, the deepest scour and highest sedimentation values increased by 4.7 and 2.25 times, respectively, compared to those in the uniform bend. Also, by increasing the divergence ratio, the deepest scour depth and highest sedimentation values decreased by 1.9 and 8 times, respectively, compared to those in the uniform bend.
In recent years, the rapid development of the city has driven the rapid upgrading of the public building industry. While the total number and scale of buildings continue to expand, its high energy consumption and high emissions also bring great pressure to the ecological environment. With the severe ecological environment situation, the formulation of effective emission reduction strategies to promote the low-carbon development of public building industry has become an urgent problem in the current urbanization process. The paper purpose of this article is to reduce building carbon emissions, enhance the actual effectiveness of emission reduction strategies, and achieve green development of public buildings. On the basis of understanding the relevant concepts, characteristics and composition of carbon emissions from public buildings, combined with the development status of carbon emissions from public buildings, this paper proposes emission reduction strategies based on linear programming and fuzzy comprehensive evaluation, and verifies them from the contribution degree, carbon emission intensity and their relationship with economic structure. The experimental results showed that the contribution of the strategy model in this paper in the public environment emission reduction could reach 0.384, which means that the strategy constructed by linear programming (LP) and fuzzy comprehensive evaluation (FCE) could effectively achieve carbon emission reduction (CER) and improve the implementation effect and efficiency of the strategy. In the construction of construction projects, the application of linear programming and fuzzy comprehensive evaluation in the carbon emission and emission reduction strategies of public buildings is of great significance for promoting environmental sustainable development and maintaining economic and ecological balance.
The aim of this paper is to find the proper families and mother wavelets for a successful localization of multiple damages in beams. The post-processing of differences in modal rotations, experimentally measured with shearography, and differences in modal curvatures of aluminium beams are carried out with 14 wavelet families and 84 mother wavelets. The damage identifications show that the best families of wavelets for the post-processing of modal data are the Shannon, frequency B-spline, and complex Morlet families. In order to select the modes that are more sensitive to damage, a novel parameter for the evaluation of the most changed mode is proposed. The boundary effect, which is often found in wavelet-based damage identifications, is also addressed in this paper. The results show that the post-processing of differences in modal curvatures yields better damage identifications than the post-processing of differences in modal rotations.
This work has the goal of experimentally determining the rotational moment of inertia of a wind turbine rotor blade and quantifying the uncertainty of this inertia measurement. Rotational inertia is an important parameter for modeling wind turbine power capacity due to its great influence on powertrain performance. There is a directly proportional relationship between the inertia and rotational power machines. Among commonly used experimental methods, the acceleration–deceleration method was conducted for rotational moment of inertia measurement of a commercial HAWT (Horizontal-Axis Wind Turbines) iSTA Breeze® i-500 model. As recommended by GUM Supplement 1, the Monte Carlo method was used to estimate the measurement uncertainty related to wind turbine rotational inertia. Experimental acceleration–deceleration tests were performed using the motion capture software Tracker to collect angular position data by video capturing the rotational motion of a wind turbine attached to a falling mass, which is rolled to the rotor axis. After appropriate data processing, the experimental uncertainty measure was obtained by the Monte Carlo method. A statistical analysis was implemented to validate the measurement model via comparisons with other methods. The main finding of the present article is the experimental validation, according to ISO-GUM, of the experimental acceleration–deceleration methodology to determine the rotational inertia of equipment, with low uncertainty, without the need to dismantle it, using simple low-cost sensors. The rotational moment of inertia obtained was compared to trifilar pendulum method with a discrepancy between the results of less than 0.7% and a confidence interval of 95.1% .
