Recent years have witnessed a burgeoning interest in sustainable, eco-friendly, and cost-effective construction materials for civil engineering projects. Soilcrete, an innovative blend of soil and cement, has gained significant acclaim for its versatility and effectiveness. It serves not only as grout for soil stabilization in corrosive environments like landfills and coastal regions but also as a reliable material for constructing structural elements. Understanding the mechanical properties of soilcrete is crucial, yet traditional laboratory tests are prohibitively expensive, time-consuming, and often imprecise. Machine learning (ML) algorithms present a superior alternative, offering efficiency and accuracy. This research focuses on the application of the adaptive neuro-fuzzy inference system (ANFIS) algorithm to predict the uniaxial compressive strength (UCS) of soilcrete. A total of 300 soilcrete specimens, crafted from two types of soil (clay and limestone) and enhanced with metakaolin as a pozzolanic additive, were meticulously prepared and tested. The dataset was divided, with 80% used for training and 20% for testing the model. Eight parameters were identified as key determinants of soilcrete's UCS: soil type, metakaolin content, superplasticizer content, shrinkage, water-to-binder ratio, binder type, ultrasonic velocity, and density. The analysis demonstrated that the ANFIS algorithm could predict the UCS of soilcrete with remarkable accuracy. By combining laboratory results with ANFIS model predictions, the study identified the optimal conditions for maximizing soilcrete's UCS: 11% metakaolin content, a 0.45 water-to-binder ratio, and 1% superplasticizer content.

Selecting the optimal intensity measure (IM) is essential for accurately assessing the seismic performance of the submarine shield tunnels in the layered liquefiable seabed. However, current research relies on simplistic ranking or filtering methods that neglect the different contributions of each evaluation criterion on IM's overall performance. To address this, this study begins by developing a numerical simulation method for nonlinear dynamic analysis, considering joint deformation, ocean environmental loads, and soil liquefaction, which is validated by experimental and theoretical methods. Subsequently, a fuzzy multiple criteria decision-making (FMCDM) method based on fuzzy probabilistic seismic demand models (FPSDM) is proposed, which integrates the fuzzy analytical hierarchical process (FAHP) for calculating weights and the fuzzy technique for order preference by similarity to ideal solution (FTOPSIS) for ranking IM alternatives. Finally, tunnel damage is classified into four states employing joint opening as the index for measuring damage, then the seismic fragility analysis is conducted. The results indicate that the optimal IM of a submarine shield tunnel situated in layered liquefiable seabed is sustained maximum velocity (SMV). Furthermore, the comparison between the fragility curves established using SMV and peak ground acceleration (PGA) reveals PGA, a frequently employed IM, notably undervaluing the seismic hazard.

One of the fundamental challenges in municipal waste management is ensuring the long-term stability of landfills. Slope instability in these structures can lead to irreparable consequences, including environmental pollution, infrastructure destruction, and public health threats. Therefore, accurate assessment and prediction of slope behavior in these structures is of particular importance. In this study, a new method based on fuzzy logic has been proposed to assess and predict slope stability in landfills. Fuzzy logic, as a powerful tool in modeling complex and uncertain systems, allows for more accurate analysis of landfill behavior. In order to conduct the present study, various steps were taken, including data collection, modeling with fuzzy logic, determining model coefficients, and model validation. In the first step, field data were collected from various excavations such as slope geometry, soil mechanical properties, groundwater level, loading due to waste, etc. Then, a fuzzy logic model was developed to analyze slope stability. In this model, input parameters such as slope, elevation, soil type, and groundwater level were described as linguistic variables and converted into fuzzy numbers using membership functions, and finally, fuzzy rules were defined to express the relationships between inputs and output (slope stability or instability). In the next step, field and laboratory data were used to determine the coefficients of the fuzzy model, and using optimization methods, the model parameters were adjusted in such a way that the model output was consistent with the observed data. In the last step, the developed model was validated using new data. For this purpose, the model results were compared with the observed data and the accuracy and reliability of the model were evaluated. The results show that the developed model will be able to predict the probability of slope instability with high accuracy and identify areas with high risk of instability in landfills. Also, it was determined that by analyzing the sensitivity of the model, important factors that affect slope stability can be identified. According to the model results, appropriate solutions can be provided to improve slope stability, such as changing the slope angle, soil reinforcement, and drainage. As a general conclusion, based on the observations of the present study, it can be said that using fuzzy logic in analyzing the slope stability of landfills is a new and efficient approach that can significantly contribute to improving urban waste management and reducing risks from slope instability.

The selection of representative ground motion intensity measure (IM) and structural engineering demand parameter (EDP) is the crucial prerequisite for evaluating structural seismic performance within the performance-based earthquake engineering (PBEE) framework. This study focuses on this crucial step in developing the probabilistic seismic demand model for two-story and three-span subway stations exposed to transverse seismic loadings in three different ground conditions. The equivalent linearization approach is used to simulate the shear modulus degradation and the increase in damping characteristics of the soil under seismic excitation. Nonlinear fiber beam-column elements are adopted to characterize the nonlinear hysteretic degradation of the subway station structure during seismic events. A total of 21 far-field ground motions are selected from the PEER strong ground motion database. Nonlinear incremental dynamic analyses (IDAs) are conducted to evaluate the seismic response of the subway station. A suite of 23 ground motion IMs is evaluated using the criteria of correlation, efficiency, practicality, and proficiency. Then, a multi-level fuzzy evaluation method is employed to integrate these evaluation criteria and determine the optimal ground motion IMs in different ground conditions. The peak ground acceleration and sustained maximum acceleration are demonstrated to be the optimal ground motion IM candidates for shallowly buried rectangular underground structures in site classes I, II, and III, while the root-mean-square displacement and compound displacement are found to be not suitable for this purpose.

Soil and water conservation structures are vital for environmental resilience but present maintenance challenges due to their wide distribution and remote locations. To tackle these issues, a method using unmanned aerial vehicles (UAVs) combined with 360 degree photography was developed. UAVs captured images that were processed into panoramic and 3D models, enabling precise inspections of structural damage. These models were integrated into the disaster environment review and update (DER&U) rating system, enhanced by a fuzzy inference classification mechanism for improved damage estimation. Additionally, a management platform was created to boost overall efficiency and provide decision-making support for relevant authorities. The UAV-assisted inspection method demonstrated promising results, though certain limitations were also noted.

This study investigated the mechanical properties of rammed earth (RE) stabilized with cement or lime and reinforced with straw. Specifically, the compressive and tensile strengths of 15 different mix designs were analyzed, including unstabilized RE, RE stabilized with lime or cement (at 4 % and 8 % by weight of soil), and RE reinforced with straw (at 0.5 % and 1.0 % by weight of soil), along with various combinations of stabilized and unstabilized RE reinforced with straw. Mechanical properties were further assessed through ultrasonic testing and scanning electron microscopy (SEM). Additionally, a data-driven fuzzy logic model was developed to estimate the mechanical properties of RE, addressing a key gap in the application of fuzzy logic to RE construction. The results showed that stabilizing RE with cement and lime increased its 28-day dry compressive strength by 365% to 640% and 109% to 237%, respectively. The addition of straw generally reduced compressive strength. The stress-strain curves indicated that the elastic modulus of RE stabilized with cement and lime increased by up to 350% and 11 %, respectively. The 28-day dry tensile strength of the samples ranged from 0.17 to 0.56 MPa. Furthermore, the addition of stabilizers improved tensile strength by approximately 88 % to 224 %, while straw enhanced the tensile strength of unstabilized RE by about 35 %. Ultrasonic and SEM analyses provided valuable insights into the mechanical properties of RE. Additionally, the fuzzy logic model proved useful, yielding satisfactory results in predicting the properties of RE, particularly when using the centroid defuzzification method. The study concluded that RE materials when properly cured and effectively stabilized with cement, lime, and straw, can achieve acceptable mechanical properties and offer sustainable benefits.

Landslides are one of the most significant natural geological hazards, capable of causing extensive damage to lives, infrastructure, and property. These events are often triggered by specific geological and environmental conditions that can be monitored utilizing advanced technologies such as Wireless Sensor Networks (WSNs). This study introduces a novel itinerary planning approach for WSNs, employing the Fuzzy Logic-based Particle Swarm Optimization (FLPSO) technique, which integrates Fuzzy Logic and Particle Swarm Optimization methodologies. The primary objective of this approach is to minimize the energy consumption in large-scale WSNs, thereby enhancing their efficiency for landslide detection systems. The proposed method improves on traditional network grouping methods by optimizing energy usage across sensor nodes. A case study was conducted in Shiradi village, Mangalore, India, an area characterized by high annual rainfall and changing climatic patterns. Over a year, data was collected and analyzed to evaluate the system's potential for accurate landslide hazard predictions. The soil suction stress was calculated using laboratory tests, incorporating various geotechnical and unsaturated soil parameters specific to the study area. The experimental results demonstrated that energy-efficient nodes not only have a longer operational lifespan and greater adaptability to environmental changes, but also exhibit superior performance compared to current methods, with improvements of 14.15% in Packet Delivery Ratio (PDR), 11.15% in Energy Delay Product (EDP), 10.15% in Packet Loss Ratio (PLR), 22.1% in task delay, and 20.1% in throughput.

The Chinese economy is one of the largest and most dynamic economies in the world. Over the past few decades, China has experienced rapid economic growth from agrarian to industrial powerhouse fueled by manufacturing, exports, and services. However, this rapid growth has also brought about challenges, including environmental issues like water contamination. The indulgence of cadmium metal in regular used water can cause serious health issues, including kidney damage and cancer. Many strategies have been implemented for treatment of water contamination. The main focus of this research is to introduce a novel methodology for treatment of cadmium contaminated water problem in China. This study seeks to demonstrate the multi-criteria group decision-making ability based on the outranking relations within the confines of a contemporary, well- organized and extremely flexible model of spherical fuzzy rough numbers. Spherical fuzzy rough numbers, amalgamation of rough numbers with traditional spherical fuzzy numbers, make the use of membership, nonmembership and neutral membership degrees along with the manipulation of the subjectivity and reliance on objective uncertainties. The combination of spherical fuzzy rough numbers with an outranking multi-criteria group decision making technique, Elimination and Choice Expressing Reality, integrates spherical fuzzy logic to handle uncertainty and imprecision in multi-criteria decision-making. This approach captures degrees of uncertainty and hesitancy with spherical fuzzy numbers, improving the handling of imprecise information. The working mechanism involves generation of outranking relations among alternatives by comparing predominant and subdominant options, calculating score degrees, concordance and discordance sets, and incorporating subjective spherical fuzzy rough criteria weights. Unlike traditional methods that use crisp or conventional fuzzy numbers, this technique provides a more reliable and flexible evaluation by integrating rough set theory for better handling of imprecision and uncertainty. Finally, an outranking graph is drawn that points from the supreme option to inferior one. The legitimacy of the proposed technique is, then, testified by making its comparison with other existing techniques.

This paper provides a comprehensive analysis of the undrained failure envelope for embedded foundations in anisotropic clays. Using the AUS failure criterion as the soil strength model, the study examines how the anisotropic strength (re) and embedment depth (D/B) affect the behavior of the footing under combined loading conditions. Failure envelopes are assessed via two-dimensional finite element limit analysis (2D FELA) in both 2D and 3D spaces. This research highlights the failure mechanisms of embedded foundations, offering valuable insights into the engineering design of footings in anisotropic clays subjected to combined loads (V, H, M). Furthermore, this study introduces an advanced soft-computing approach by creating a machine learning model that leverages the adaptive neuro-fuzzy inference system (ANFIS) integrated with the particle swarm optimization (PSO) algorithm to predict the failure envelope of embedded footings, highlighting the novelty and original of this study. The optimised ANFIS model has been validated and demonstrates a strong correlation with the numerical FELA results, offering engineers a valuable tool for determining the failure envelope of embedded foundations in anisotropic clay under different loading scenarios (V, H, M).

Due to the complex and multi-dimensional nature of droughts, it is not possible to assess droughtinduced damage and its consequences for various social, economic, and environmental aspects of societies by relying only on a univariate index such as precipitation-based drought indices. The present study aimed to develop a practical and scientific framework based on hazard, vulnerability (social, economic, and environmental), and coping capacity to generate a drought risk map for the hot and dry climate regions of Iran. Accordingly, the Drought Hazard Index (DHI), Drought Vulnerability Index (DVI), and Drought Coping Capacity Index (DCCI) were derived from the Standardized Precipitation Evapotranspiration Index (SPEI), 16 social, economic and environmental variables and three social, economic variables, respectively. The layers of all variables of the three indices in the GIS were provided, and they were combined in the form of an equation to produce a drought hazard map of central and southeastern Iran. The results indicate that the counties most and least vulnerable to drought were located in the southeast and west of the case study area, respectively. A number of large households, long distances from provincial centers, and soil erosion were the most important social, economic, and environmental factors making the southeast of the case study (including south of Sistan and Baluchestan and south of Kerman provinces) most vulnerable to drought. Due to their high drought coping capacity, counties located in the west of the case study (west of Kerman and south of Yazd provinces) were least vulnerable to drought. Extended support for low-income households by charitable organizations, tertiary education, and most importantly, a variety of jobs and career opportunities were the most important factors in reducing vulnerability in this part of Iran. Furthermore, our methodology by taking social, economic, and environmental dimensions into account as risk, vulnerability, and coping capacity indices can be far more efficient than the methods considering only risk and vulnerability factors.