Civil Engineering for Multi-Hazard Risk Reduction

Citizens aspire for good quality spaces and availability of amenities close to their homes in the neighborhood. They are thus encouraged to walk to the market places, parks, and other places of daily visit, which keeps them healthy. This paradigm shift of walkable localities in the planning of the cities as a global approach, calls for an investigation of whether there is adequacy in terms of amenities and whether they are within the walkable distance from homes. In this context, the “15-minute city” as defined by Carlos Moreno was an ideal urban area where most human needs and aspirations were located within a walkable distance of 15 min. This was used to evaluate the city of Hassan in Karnataka, India. By employment of a conceptual radius of 400 m, the pedestrian shed was defined as a locality, in the over laid model of the 15-min city for the context of Hassan. Seven such localities were defined to constitute a neighborhood. The 15-min city plan as defined by Carlos Moreno was overlaid on the existing plan of Hassan and analyzed with the assumption that the historical medium-sized city of Hassan may have an underlying structure of a walkable city damaged by recent developments. Restoring and guiding the urban transformations in accordance with a clearly defined structure of neighborhood localities was proposed which enhanced the quality of life of the citizens. The proposed structure also defined the areas for future proposed amenities at the neighborhood level.

This paper discusses the prediction of noise pollution during Deepawali festival of Delhi City using machine learning (ML) algorithms. The spatial noise pollution data of four locations of Delhi, namely Lajpat Nagar, Mayur Vihar-II, Kamla Nagar, and Pitam Pura were collected from the Central Pollution Control Board (CPCB). Seven regression models were used on the Python platform. Algorithms were run using Google Colab. As the data obtained were very little, additional two random data were generated and used in the analysis. It was found that among all models, Quantile Regression is a superior one in the prediction of noise level in the present study as compared to other ML models. It is observed that coefficient of determination with Quantile Regression is 0.792 for original data, 0.803 for 150 random data, and 0.801 for 300 random data. However, at other locations, the suitability of a particular regression model can be determined and recommended.

The availability of clean water is essential for public health and various industrial processes. However, sustaining the water quality is a challenge, because of the ever-increasing demand for water and the effect of human activities on water resources. Because of this, water quality prediction using the machine learning techniques is an emerging field of research that helps in forecasting water quality accurately. In this paper, a machine learning-based technique is proposed to predict quality of the water, using historical water quality data. Based on water quality index, usage of the water is being classified into three distinct categories, namely, drinking, agriculture and industrial. Then, performance of the model is evaluated for optimization using linear regression model, random forest, decision tree model and XG boost regression to enhance the accuracy of the model. The quality of the water can be determined based on the water quality parameters such as pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), nitrate, coliform and temperature. It helps in monitoring and managing the quality of water in real time, identifying potential risks and developing strategies to mitigate them. This experimental result proves that the proposed approach achieves high accuracy in predicting water quality and can be used as a reliable tool for water quality management. Water quality prediction using machine learning has the potential to improve the accuracy and efficiency of water resource management, leading to better environmental and public health outcomes.

Many human activities have been the main contributors to surface water contamination in recent years. However, the western Godavari delta region of Andhra Pradesh only seldom permits assessing the water quality's natural state due to the uneven distribution of industrial operations and agricultural farms. This study uses artificial neural networks (ANNs) to estimate surface waters’ water quality index (WQI) between 2014 and 2022. A prediction like this can reduce computing time, labour, and the chance of calculating errors. The ANN results demonstrate that convergent plots perform better when the coefficient of determination (R2) values is more significant. Electrical conductivity (EC) and total dissolved solids (TDS) are the most significant parameters in predicting WQI in the surface waters of the Godavari delta region, according to a sensitivity analysis used to demonstrate the significance of each parameter in the ANN's modelling process. The method described in this paper offers a practical and effective alternative to WQI assessment and prediction, particularly when compared to WQI calculation methods that require time-consuming calculations and multiple sub-index calculations for every single value, or range of standards, of the part water quality variables.

Risk is an integral part of the construction industry. Even a well-experienced person can be met with an accident due to the inherent dangers associated with construction sites. Any construction area needs to be secured against theft and illegal activities. To ensure the safety of the construction site, it is necessary to patrol the area, so that any illegal activities can be reported and corrective action can be taken against them. As such sites are usually open and exposed to multiple heavy machinery, these sites are prone to accidents which may end up in the loss of life of construction workers. Patrolling of such sites by humans is not safe and demands for human less patrolling. In addition to this, another concern is in Z patrolling the construction site situated in remote locations. Without suitable resources, security cannot be arranged. IOT-Based Patrolling bots with intelligent sensors, embedded systems, autonomous control mechanisms, and mobile applications would be a groundbreaking innovation in advanced security and surveillance technology. So, here we propose an IOT-Based Patrolling robot using Raspberry PI. This system uses cameras and microphones mounted on the robotic vehicle for monitoring any premises. The robotic vehicle follows IR-based path for patrolling the assigned area and stops at particular points if any sounds are detected. It monitors the entire area and detects any unauthorized or illegal activity. It captures the activity and sends images of the situation immediately to the administrator. Thus, we put forward a patrolling robot with advanced sensors that operates vigorously and monitors unsecure areas and saves the life of construction site workers.

The use of IoT-enabled robots in the investigation and maintenance of underground tunnels has gained significant attention in recent years. These robots, equipped with sensors and cameras, offer numerous advantages, including improved safety, increased efficiency, and cost reduction. This research explores the benefits of employing IoT-enabled robots for tunnel analysis and maintenance, while addressing the challenges associated with their implementation. The study focuses on the use of LIDAR technology for perception, mapping, and navigation of the robots in unstructured underground environments. The autonomous navigation system encompasses perception, map building, location calculation, planning, control, and obstacle avoidance. The research highlights the importance of autonomous systems in assisting humans in various domains and reviews past and present research using various sensors and software. The proposed work presents a comprehensive approach that combines hardware, software, and experimental methodologies to investigate the utilization of IoT-enabled robots for safe and efficient tunnel operations. The research includes the development of customized software applications, integration of advanced sensor technologies, communication infrastructure, and experimental setups to simulate underground tunnel environments. Data collection, analysis, evaluation, and validation are conducted to assess the performance and effectiveness of the IoT-enabled robots (Yan et al. in Int J Distrib Sens Netw 15(5), 2019 [1]). Additionally, the integration of IoT-enabled robots in civil engineering applications is discussed, including construction site monitoring, automated inspection and maintenance, smart infrastructure monitoring, autonomous construction and excavation, hazardous environment exploration, geotechnical monitoring, and disaster response and recovery (Valente et al. in 2020 Global IoT summit (GIoTS). IEEE, pp 1–6, 2020 [2]). The results demonstrate the visualization of surrounding objects using LIDAR data and showcase point clouds captured in cave and tunnel environments. Overall, this research demonstrates the potential of IoT-enabled robots to revolutionize underground tunnel inspection and maintenance, offering improved safety, efficiency, and quality control in civil engineering projects.

This paper presents a pipeline that utilizes a TorchScript model to implement event-based detection in challenging traffic scenarios, aligning with the theme of Advances in Intelligent Transport Systems (ITS) for Sustainable Mobility. The pipeline incorporates intuitive interfaces for visualizing event streams in both 2D and 3D, enhancing the understanding of traffic dynamics. It also integrates adaptive rate control and optical flow estimation techniques to improve detection and tracking capabilities. The model demonstrates promising results in accurately detecting and tracking vehicles and pedestrians. To validate the model’s performance, we analyze the spatio-temporal distribution of events using histogram difference computation and exponential-decay time surface analysis, which provide valuable visual insights. By effectively utilizing event-based sensing and incorporating innovative techniques, this research contributes to the advancement of vision-based traffic monitoring for sustainable mobility within the field of Intelligent Transport Systems.

In many applications, including measuring physical activity, understanding sign language, and controlling full-body gestures, human position estimation from video is essential. This has the potential to be utilized for activity recognition in civil work. By accurately tracking human body posture from video, the technology can identify and classify different tasks and actions being performed by workers in construction, manufacturing, or other industries. This information can be used to monitor worker productivity, optimize workflow, and identify potential safety hazards. The proposed project is a machine learning (ML) solution for high-fidelity body posture tracking, employing current open source research that also drives the Machine Learning Pose Detection Application programming interface to infer predefined 3D landmarks and background segmentation mask on the entire body from RGB (Red, Green, Blue) video frames. The suggested method in this project achieves real-time performance on the majority of modern mobile phones, desktops/laptops, Python, and even the web, in contrast to current state-of-the-art methodologies, which rely mostly on strong desktop environments for inference.

Smart irrigation systems are essential to detect the existing moisture content of soil, which regulates and controls the water supply to irrigation. The present study focuses on the on-board installation of soil moisture sensor with Arduino UNO platform to measure the moisture content of soil samples, which will facilitate in releasing of irrigation water. The present experimental study uses five uniform (poorly graded) soil samples of size d50 = 850, 600, 425, 300, and 150 µm and a non-uniform (well-graded) soil sample of d50 = 325 µm. A fourth order polynomial is fitted between the sensor reading and degree of saturation, which is related to second order polynomial between the degree of saturation and moisture content of the soil. The sensor readings are used to estimate the existing moisture content of the soil sample through the degree of saturation of the soil through these polynomials. A satisfactory similarity is found between degree of saturation and versus normalized sensor readings for all the cases of uniform and no uniform soil. Further, power equation is developed between the sensor reading and the moisture content of the soil with an R2 value of 0.96. In addition, three machine learning prediction models ANN, KNN, and SVM were employed and compared. It is found that artificial neural network predicted the moisture content better than other predictors having prediction accuracy with R2 = 0.981 for training and 0.985 for validation indicating as a good predictor as compared to KNN and SVM.

Soil being a heterogeneous medium, predicting the stability of soil slope is a complex engineering problem due to the involvement of multiple effective attributes of soil in geotechnical behaviour. However, as comprehension of soil variability improves, deterministic methods have been replaced by probabilistic ones. This paper examines the application of two soft computing techniques, Group Method of Data Handling (GMDH) and Random Forests Classifier (RFC), to the study of reliability analysis of clayey soil slope stability. In addition, the applicability of GMDH and RFC in predicting stability of Soil Slope based on distinct soil attributes was evaluated, and model performance was evaluated using various fitness parameters such as RMSE, LMI, Bias Factor, etc. The results indicate that the GMDH model outperformed all fitness parameters, suggesting that the GMDH approach can be used as a reliable soft computing method for addressing non-linear problems, such as the stability of soil slope.

This research aims to develop a hybrid artificial intelligence model to predict the residual strength required to resist soil movement after post-liquefaction. The model is trained using available case history and experimental data, with a focus on soil parameters such as standard penetration test, cone penetration test resistance, percentage fine, void ratio, relative density, and pore water pressure. Detailed statistical analysis of the model is conducted using previous case histories to assess its accuracy. The practical implications of this research lie in the challenge of having to extrapolate beyond available data for flow failures and lateral spreading after liquefaction. By providing a reliable prediction model for residual strength, this paper offers a valuable tool for geotechnical engineers and practitioners to assess the stability of soil and mitigate risks associated with soil movement after post-liquefaction.

An optical measurement method called Digital Image Correlation (DIC) enables the accurate tracking of a surface or object's motion and deformation across time. To examine the behavior of materials and structures under varied loading circumstances, the approach is widely used in materials science, engineering, and biomechanics. In DIC, a high-speed camera is used to take a sequence of digital photographs of the item or surface of interest at various moments in time. The displacement and deformation of the item are then calculated from the photos using the pixel contrast of corresponding location on the photographs. In geotechnical engineering, DIC technique is recently gaining popularity. In the present study, a brief theory of DIC is presented. Further, the application of the DIC in failure mechanism of footing on sandy soil is examined. The developed failure wedge beneath the footing is compared with the theatrical concept. It was found that the DIC technique can successfully depict the actual failure mechanism of footing on slope. The future scope of this technique is also discussed in the paper.

In the present study, design of the steel truss of the existing geodesic dome is carried out by using the codebook IS 800:2007 (code of practice for general construction in steel). The dynamic analysis is also carried out by using response spectrum method (RSM) as per IS 1893(Part 1):2016 (the criteria for earthquake resistant design of structures). The MAT foundation is designed for geodesic dome models for 30 m diameter. To incorporate the soil-structure interaction (SSI), the standard soil mechanical properties are obtained by soil test. Finite element analysis (FEA) of the geodesic dome is carried out using SAP 2000 V22. The artificial neural network (ANN) model is developed by using MATLAB 2021Ra.119 and soil spring values are calculated by varying the standard penetration number (SPT), shear wave velocity, density, and Poisson’s ratio for different soil conditions. ANN model is developed with four inputs as soil spring values and four outputs as base shear values for zone II, III, IV, and V using the Levenberg–Marquardt algorithm. It is observed from the study that the ANN model can predict the dynamic characteristics and seismic response of the soil-structure system with 98% accuracy compared to the results obtained by FEA.

The overlap between gas and oil pipeline network safety distances and urban areas poses significant risks and potential hazards to public safety and the environment. In the case of the city of Mohammedia, Morocco, where there is a significant concentration of industrial activities, the risk of accidents related to gas and oil pipelines is high due to the proximity of urban areas to the pipelines and its closeness to the oil port, which is considered the largest in Morocco in terms of importing petroleum materials. The problem is determining whether the existing safety distance regulations for gas and oil pipelines are adequate to ensure the safety of the population and the environment in the urban areas of Mohammedia. Therefore, the main problem of this subject is how to assess the adequacy of the existing safety distance regulations for gas and oil pipelines in the urban areas of Mohammedia and develop effective measures to mitigate the risks associated with the overlap of the safety distance of pipelines with the city's urban areas. This article addresses this issue by measuring the safety distance to determine whether an explosion, leak, or fire outbreak will reach the civilian area.

Tunnelling is amongst the most demanding infrastructure projects done in civil engineering industry. In addition to its severity of work, the construction being done in underground conditions makes it highly vulnerable to schedule delays, cost overruns, etc. Making risk analysis has been a very important aspect in tunnel construction. Due to the complexity of the tunnelling industry, each tunnelling project has numerous risk factors affecting the flow of construction work. The long list of factors is initially curtailed using Relative Importance Index (RII) then these risk variables are further prioritised based on the relative importance of expert opinion by using Analytic Hierarchy Process (AHP) approach. A literature analysis and real-life case studies in tunnel construction yielded 98 risk indicators. The list was then reduced to 75 by removing elements that were repetitive or overlapped. The remaining elements were grouped into clusters and rated using two methods: the Relative Importance Index and the Analytical Hierarchical Process. Tunnel projects are complicated and expensive infrastructure projects; hence the aim of this research is to improve current risk analysis practices by offering a more accurate framework for risk analysis and prioritisation.

In every construction project, risk is a very common event. Whereas it is more significant in road construction projects as it involves multiple activities in several stages and spread over a wider geographic area, therefore it may be unpredictable. Due to these risks, budgets are stretched excessively, which results in project cost overruns. The purpose of this research is to identify the risk variables that cause cost overruns in projects for developing roadways in India. Several risks are identified through the use of a comprehensive literature review and on-field expert advice. To get a construction professional’s perspective on risk factors contributing to cost overruns, the questionnaire survey is developed. RII (Relative Importance Index) technique is used to prioritize risk factors from the pool causing cost overrun in road construction. By gathering all the responses from a questionnaire survey with experts working in road construction, this work attempts to find the most important risk factors for road construction and prioritize them accordingly. By identifying important risk factors in the early stages of the project, it would be possible to reduce complexity throughout the development of roads. It is expected that the framework will assist researchers and decision-makers in ranking the risk factors and focusing on the most critical ones to mitigate risks and prevent delays and cost escalations, ensuring the successful completion of road construction projects in India.

Flood prediction is a critical aspect of disaster management and requires accurate forecasting techniques to mitigate the potential risks and impacts. In this study, a flood prediction model is developed and built using machine learning algorithms. The objective is to develop a robust and reliable system that can forecast the occurrence and severity of floods in a specific region. The proposed model utilizes historical data on rainfall (in millimeters) to train the machine learning algorithms, such as decision tree, random forest, K-nearest neighbors (KNN), and logistic regression algorithms to build predictive models. These algorithms are known for their capability to handle diverse data patterns and provide accurate predictions. The dataset used for training and evaluation is sourced from the region of Kerala, India, which experiences frequent flood occurrences. The data is preprocessed, including cleaning, handling missing values, and converting categorical variables, to ensure the quality and compatibility of input features. Experimental results demonstrate the effectiveness of the developed models in flood prediction. The decision tree algorithm provides interpretability and identifies significant variables influencing flood occurrence. The KNN algorithm shows promising results in capturing local patterns and neighbors’ influence. Random forest leverages ensemble learning to enhance prediction accuracy, while logistic regression estimates the probability of flood events. The proposed flood prediction models offer valuable insights for early warning systems, disaster response planning, and resource allocation. The integration of machine learning algorithms enhances the accuracy and reliability of flood prediction, facilitating proactive measures to mitigate the potential risks associated with flooding.

Greenhouse is the place where the plants are grown in a controlled manner. Monitoring the greenhouse environment is necessary to maintain the optimal conditions for plant growth. Using wireless sensor networks, the monitoring of the greenhouse environment can be not only simplified but can also contribute to increase in the production efficiency. Monitoring and control sensors are used for different purposes in agriculture. Smart greenhouse farming using Internet of Things (IoT) are deployed in rural area to benefit the farmers by automatic monitoring and controlling the plant environment. Thus, IoT devices and technologies are deployed in smart greenhouse that mainly focused on enhancing greenhouse farming's effectiveness and efficiency. The proposed system uses various IoT devices for automatically monitoring the parameters that affect plant growth, such as temperature, humidity, soil moisture, water pH and to perform particular actions like turning on the irrigation based on the threshold parameters that are set. The measured parameters are transmitted via GSM (Global System for Mobile communication) module to the ThingSpeak IoT cloud platform where the data is stored. These stored data are displayed and analyzed using ThingSpeak's built-in MATLAB features. In addition, the system incorporates the plant disease detection using image processing techniques, which is crucial for early detection and prevention of crop loss. A website has been developed where all the parameters, which are measured, are displayed and analysis performed in ThingSpeak cloud can be seen by the user.

We are in the Big Data era, the quantity of geospatial data gathered or stored using remotely sensed satellite imagery for land-use and land-cover (LULC) mapping and secondary geospatial data files grow. Innovative cloud computing, deep learning methods, and machine learning have also recently been developed. Deep learning algorithms got a lot of interest because of their enhanced performance in separation, category, and supplementary machine algorithm applications. Land use and land cover (LULC) are key elements of a broad range of ecological uses in remote sensing. Land-use changes occur on the geographical and spatial scale owing to the precision, development capabilities, flexibility, uncertainties, structure, and ability to incorporate existing patterns. As a result, the high performance of LULC modelling necessitates the use of a broad range of pattern modes in remote sensing, including dynamical, statistic, and deep learning models. Advances in remote sensing technology, and hence the rapidly increasing amount of timely data accessible on a worldwide scale, open new possibilities for a variety of applications. This article provides a summary of the basic ML and DL ideas that apply to the LULC is explained, including their pros and cons. To address the difficult issue of identifying changes in LULC, the application of deep learning to land usage, and this review has clarified both their advantages and disadvantages as researched by various scholars.

Earthquake is one of the most devastating natural calamities known to man. Earthquakes can affect lives in unimaginable ways and predicting them well in time is one of the most important things when it comes to earthquake damage reduction. Many approaches are used to predict earthquakes, and machine learning can aid in early and timely prediction of earthquakes and thus reducing any damage. This paper provides a comprehensive and comparative analysis of various classical machine learning algorithms in the prediction of earthquakes using a large textual dataset that holds information about all the historical earthquakes. This dataset holds many vital data and serves as the basis for training and evaluating various machine learning models. Different regression models including a random forest regressor, decision tree regressor, linear regressor, and a regression artificial neural network (ANN) were used for earthquake prediction. Machine learning architectures help in capturing the relationship between the independent and dependent variables, whereas the ANN captures more complex patterns in the data. Unlike linear regressor, decision tree and random forest regressors capture non-linear relationships between dependent and independent variables. A substantial amount of experimentation and evaluation of the models were conducted to compare and contrast the model performance based on relevant performance metrics. This research offers insights into application of several machine learning models in earthquake prediction and their real-world applicability.

Ichamati River flows through Pabna city and has been transformed into a narrow canal. Except for monsoon, water flows are reduced drastically and dry in summer. Presently, the river is mostly occupied by dumped waste, and this waste has clogged the Ichamati River by blocking the possibility of water flow. There needs excavation for removing the wastes from the river, but also, a sustainable solid waste management system is the prerequisite for proper management of the Ichamati River to ensure a robust river environment for the future. The objective of the study was to explore the present solid waste dumping scenarios along the Ichamati riverbank of Pabna city and to propose a possible waste management plan. The methodological framework with participatory rural appraisal toll consists of extensive field visits, focus group discussions (FGD), key informant interviews (KII), and then, GIS analysis for a suitable solid waste management plan. The major issues in this area are inadequate waste management, disposal, and collection system and a lack of awareness of the public regarding riverside waste management. The reduce, reuse, and recycle (3R) is a very excellent option for Pabna as practiced by some poor families. To overcome the issues, this study found that low-cost regular waste collection systems by the municipality from door to door and relocating the dumping station from the riverbank can make the solid waste management system in the riverside sustainable.

Dams are counterfeit lakes made to hold water for a specific reason. In the present study, the flood plain zone, at the downstream finish of KRISHNARAJASAGARA Repository close to Mandya, has been planned, involving DEM in Bend GIS in the wake of choosing yield area in Smack. This paper gives an outline of the utilization of 2-D water-powered model, Hydrologic Designing Center—Waterway Investigation Framework (HEC-RAS) for the examination of dam break of Krishnarajasagara dam situated at Mandya locale, Karnataka. Inundation maps were created in ARC-GIS by employing the IDW interpolation method and the water surface elevations obtained from HEC-RAS for the various scenarios listed above. Later, the IDW map was shown on Google Earth to identify flooding areas. Further, the model is utilized to provide an indication of the flood. Level sensors are used to determine the surface’s position within a container that holds both liquids and powdered solids. In this paper, we examine the plan of a water level sensor gadget that can recognize and control the degree of water in a certain water tank or a comparative water stockpiling framework. The framework detects how much water, first and foremost, accessible in the tank by the level finder part and afterward changes the condition of the water siphon in understanding to the water level data.

Climate risk management in agriculture is a critical concern for sustainable production and livelihood. This research is an attempt to monitor climate hazards with respect to rainfall variability and its patterns in Bharathapuzha basin (BPB), Palakkad, the rice bowl of Kerala. It was noted that there are no research works published on the recent rainfall trends in this basin. Where there are fewer monitoring stations, satellite remote sensing provides better picture of rainfall distribution of a region. Climate Hazard Group Infrared Precipitation with Station Data (CHIRPS) is one of the latest high-resolution quasi-global satellite-based rainfall datasets used for rainfall measurements. CHIRPS data is used for getting an overall rainfall distribution in the basin from 1989 to 2020. Results shows that CHIRPS has captured the spatial pattern and seasonality of monsoon, the eastern side of the basin is comparatively dry with 700 mm of annual rainfall and west portion of the basin gets 2600 mm of annual rainfall. Precipitation concentration index (PCI) is used to evaluate seasonal precipitation changes and heterogeneity of monthly rainfall within the basin. PCI value is less than 10 in winter; hence, the precipitation is uniformly distributed throughout the basin; however, PCI is over 40 in July, June and October months representing a significant irregular rainfall distribution throughout the basin. Through this research, weather early warning is distributed to the rice farmers and the initial survey revealed that dissemination has helped the rice farmers to a great extent for risk reduction. Small and marginal rice farmers had an opinion that extreme rainfall events such as floods and droughts is posing havoc in rice production in the recent past. The drought in the year, 2016, and floods of 2018, 2019 and 2021 created destruction to human life and crop production, and traditional varieties are reported to be more resilient to hazards. Adopting a combination of technology solutions such as ICT-based weather early warning and nature-based solutions aids to lessen carbon foot prints and severe negative impacts. Spatiotemporal rainfall variability analysis supports not only farmers or agriculturists, but hydrologists, geologists, engineers working on surface or groundwater irrigation, and policymakers to manage available water resources efficiently.

This paper offers a comprehensive investigation of crucial meteorological variables [rainfall (P), surface temperature (T), and relative humidity (RH)] for Maharashtra (307,690 km2 area), a dry-arid state in the western Indian subcontinent, encompassing four meteorological subdivisions: Konkan and Madhya Maharashtra (west) and Marathwada and Vidarbha (east). The central hypothesis posits that Maharashtra is rapidly becoming an emerging warming state. To examine this hypothesis, long-term hydroclimatic time series (1980–2020) data for P, T, and RH were derived from the ECMWF-ERA5 dataset and analyzed using non-parametric Mann–Kendall and Sen's Slope methods at α = 0.05 significance level. Pearson correlation coefficient (PCC) was applied for time series and scatter plot interpretation. Anomalies were identified by comparing data from 2011 to 2020 to the baseline (1981–2020). The results showed significant and positive trends in temperature (T) and rainfall (P) across Maharashtra and its subdivisions. Relative humidity (RH) had an insignificant but positive trend. The highest correlation was between RH and P, followed by T and P, with the weakest association between T and RH. Konkan had the highest RH and P values, while Vidarbha experienced the highest temperatures. Temperature anomalies ranged from 0.19 to 1.29 °C in Maharashtra, with the most significant anomaly in Marathwada (0.62–1.77 °C) and Vidarbha (0.67–1.56 °C). RH and P anomaly values decreased with rising temperatures, especially during summer, winter, and in the eastern region, potentially leading to hotter summers and less cool winters. In conclusion, the findings provide robust evidence of Maharashtra's emergence as a warming state, particularly during the recent decade (2011–2020).

To enhance the mobility of the military forces to the impregnable areas and also to provide a temporary access for rescue force to the disaster area when the existing landline of communication system is damaged or destroyed, lightweight portable bridges are very much useful. Nowadays, fiber-reinforced polymer (FRP) composite becomes an integral part of the construction industry. But for most of the cases, use of FRP is restricted in combinations of FRP and conventional materials or in retrofitting areas. The relative lightness and high strength-to-weight ratio makes it possible to enhance the portability of the bridges made by FRP only. The use of such materials for major load-bearing members is increasingly being promoted because of its superior material properties like high resistance to fatigue, corrosion free, enhanced durability, huge heat resistance, lower maintenance and life-cycle costs, etc. Present study includes the modeling and simulation analysis of FRP bridge deck slab subjected to moving load of heaviest battle tank currently available in India (T-90S Bhishma) by using ANSYS 19.2 FEA software. ANSYS 19.2 FEA software is selected to simulate the proposed design model bridge in the ANSYS Composite PrepPost (ACP). This research examines the performance of a FRP bridge deck in terms of modal and transient analysis. Glass-fiber reinforced polymer (GFRP) is being used as a primary material for this bridge deck.

Fly ash, a byproduct of burning coal, is usually used as a supplementary cementitious constituent in the creation of concrete. It has been discovered that using fly ash in a concrete has a number of advantages, including improved workability, decreased permeability, and greater strength. Using image processing techniques, this study seeks to regulate the ideal amount of fly ash in the concrete mix in respect to crack propagation. The study entails analyzing concrete cylinders samples in the lab with dimensions of 150 mm × 300 mm that contain different amounts of fly ash—5%, 15%, 25%, 30%, 40%, and 50% by weight of cement. A total of 120 concrete cylinders were prepared, and compressive tests were performed. The photographs of cracks under proper lighting conditions were taken to analyze using image processing. Different parameters were used for study: Otsu threshold value, size of area of interest of crack, and fly ash content. The Otsu threshold image segmentation was utilized as a binary thresholding method to detect the crack from the images. The parameters varied according to the histogram of each crack images. The segmentation was done by minimizing the variance on each class. With these analyzed parameters, the results signified that the percentage of fly ash content was achieved to be within 25–30% of cementitious materials. This optimum percentage fall in the category of “low crack class” as defined in this paper.

This paper examines the most common structural defect in concrete is surface cracking. Building inspections are carried out to assess the stiffness and tensile strength of a building. Crack detection is a crucial step in the inspection process since it helps locate cracks and assess the building’s condition. With the use of TensorFlow, several deep learning models, including VGG19, VGG16, and MobileNetV2, have been improved to recognize surface cracks. The files contain 40,000 photos of various concrete surfaces, both with and without cracks, each with a size of 227 by 227 pixels and an RGB color channel. One of the most cutting-edge vision model architectures, VGG16 is a Convolution Neural Network (CNN) with an accuracy of 99.62%. Dense Convolutional Network (DenseNet) is a deep network architecture used in deep learning (DL). 99.51% test accuracy can be attained by dividing the weights of the features collected from deeper layers among several inputs present in the same dense block and transition layers. The VGG19 architecture and VGG16, which have been tested with an accuracy of 99.62%, share a lot of similarities. MobilenetV2 has a 99.81% accuracy rate.

Nowadays, space limitations on the expansion of urban infrastructure necessitate the incorporation of various architectural complexities, such as soft storeys or floating columns, at numerous storey levels and positions in tall buildings. The presence of floating columns in the structure causes a discontinuity in the load transfer path, which is fatal during earthquakes. The stage-wise design of tall buildings, which deviates from the conventional approach, gives realistic results and is thus more appealing to designers. This paper employs ETABS v20 to deal with analyses on two multi-storey reinforced concrete buildings of varying bay widths. Acted upon by wall loads and self-weight of members, a relative analysis assessment of the full-frame model followed by a stage-wise examination of the model yields a conclusive response to the query on how the use of M40 and M60 concrete grades in building frame members can be used to assess the effects on their structural responses, thereby improving the performance of a building during its service life. The results of the study indicate that with addition of more storeys, the cumulative weight at every floor by construction stage analysis (CSA) increases; and compared to columns, beams are more susceptible to sequential effects.

RCC buildings located in hilly areas with irregular configuration need special attention while carrying out seismic analysis when compared with the RCC structures in level surface. A study on the seismic behaviour of step back building configuration and step back setback building configurations was carried out. To reduce the effect of soft storey developed in step back building and step back and setback buildings, bracings of different types were provided at ground floor level and analysed, as bracings function as horizontal load resisting system. This study was aimed to measure structural responses in terms of total storey displacement and inter-storey drift ratio. From the studies, it was concluded that chevron bracings performed well under seismic conditions in the case of step back setback buildings.

Earthquake is a natural phenomenon that occurs below the ground surface at some depth which causes vibration at the ground surface (Pradeep et al. in Analysis of infilled steel frames subjected to lateral loading, 2022 [1]). Earthquake zones are divided into four zones zone-II, zone-III, zone-IV, and zone-V. Structures which are located in zone-II will be least affected by earthquake since the magnitude of earthquake in zone-II will be less compared to other zones, and the structures located in zone-V will be affected more since the magnitude of earthquake in zone-V will be more compared to other zones. Hence, the structures which are designed without considering the seismic loading conditions may suffer damages or collapse due to earthquake (Binu et al. in Int J Eng Res Technol (IJERT), 2022 [5]). Steel structures have been widely used nowadays due to some of its advantages over the RCC structures (Pradeep et al. in Analysis of infilled steel frames subjected to lateral loading, 2022 [1]). The infill walls have been used in the structures in order to minimize the effects of earthquake (Sanjay et al. in Int Res J Eng Technol 9, 2022 [4]). In the present study, an attempt is made to determine and compare the base shear and story displacement of steel bare framed model, steel framed model with burnt brick masonry infill, and steel framed model with concrete block masonry infill for a multistoried commercial complex by using linear static analysis (equivalent static analysis) and linear dynamic analysis (response spectrum analysis) methods in E-tabs software. The results show that the base shear is more for steel framed model with infill compared to steel bare framed model, and the story displacement is more for steel bare framed model compared to steel framed model with infill. The present study gives the seismic performance of steel structure with and without infill subjected to seismic loading which helps to assess the damage caused due to earthquake.

Conventional construction of structures consumes more time and effort but still lacks quality depending on the workmanship and generates large amounts of construction cost due to formwork. The technology of 3D printing of concrete structures has eliminated the estimated cost of formwork and scaffolding. The speed of construction has also increased several times. This is of great use in disaster shelter construction which takes about 2–3 h and may have a lifespan of up to 6 years. In the recent past, 3D printed buildings have been tested only for inertial forces originated from building loads itself. The performance of a 3D printed scaled-down building model subjected to seismic loads needs to be understood. The present study discusses about the analysis and testing of a 3D printed scaled-down model subjected to earthquake loads using the Shake Table Study. A five-storey prototype building has been scaled down using similitude laws to a scale of 1:30 and 3D printed using Polylactic Acid (PLA) material. The concept of IoT (Internet of Things) has been used in the form of ADXL335 accelerometer, NodeMCU, and Arduino UNO to measure acceleration values at different levels of the model subjected to 2001 Bhuj Earthquake loads. The recorded values are, in turn, transferred to a public cloud (ThingSpeak) which makes data handling simpler. The results obtained for the scaled model are extrapolated to comprehend the response of the prototype model in terms of displacement and acceleration. Results of the study indicate that displacement of the top storey is more compared to lower floors both in the scaled and prototype model. Furthermore, the amplification of acceleration values is observed. Hence, the concept of IoT can be effectively used in model studies to understand the real-time behaviour of 3D printed structures.

Characterization of the laminated rubber bearing is done experimentally. Linear and nonlinear parameters are identified from the numerical method. The hysteretic behavior of the laminated rubber bearing is determined by cyclic shear and vertical stiffness test. Result shows that the shear test with increased axial loads resulted in a marginal reduction up to 7% in shear stiffness of the laminated rubber bearing. The bearing is modeled with equivalent linearization and nonlinear methods on secondary structure with the properties obtained from the test. The analysis results show that the response of base-isolated secondary structure is significantly affected by hysteretic properties of the bearing. Though actual prediction of the response can be obtained considering the area under the hysteretic loop, equivalent linearization can be done for narrowly damped laminated rubber bearings for conservative results. Numerical study carried out on five-story moment-resisting steel frame. The isolated single degree of freedom (SDOF) secondary system is kept on different floors of the primary system to analyze the acceleration, displacement, and shear forces. In a result, laminated bearing well intercepted the earthquake excitations having predominant frequencies less than 2 Hz from its base. For earthquake excitations, the response has been reduced up to 51%. But the sinusoidal excitation with frequency 3 Hz was unable to perform satisfactorily and amplified the acceleration response instead of reduction. Based on the numerical and experimental study, it can be concluded that the laminated rubber bearings are effective for lower frequency excitations.

The objective of the experiment was to examine the impact of high temperatures on the compressive strength of concrete. Self-compacting concrete represents a significant advancement in the construction industry, owing to its ease of use and practicality. The utilization of waste material in concrete minimizes the environmental issues and waste management-related issues. In general, sugarcane bagasse (SCBA) is used as a fuel to fire furnaces in the same sugar mill, yielding approximately 8–29% ashes containing a large quantity of un-burnt materials, silicon, aluminum, iron, and calcium oxides; the ash thus becomes an individual waste and creates disposal issues. The main objective of this project is to study the consequences of partial cement replacement with slag cement by-product ash (SCBA) and fly ash at different percentages (0%, 10%, 15%, and 20%). Two forty-nine cubes of 150 mm size of design mix concrete viz. M30 was cast. After 28 days curing, later cubes are exposed to various temperatures ranging from 600, 700, 800, 900, and 1000 °C, with various time periods 30, 60, 90, and 120 min. After being heated to the necessary temperatures in the range of (600, 700, 800, 900, and 1000 °C) in a muffle furnace, the samples were cooled at ambient temperature for 24 h while air drying, and then, the cube compressive strength test was performed on them. Based on the findings of this study, self-compacting concrete with a 15% replacement of fly ash and SCBA demonstrated superior performance in terms of compressive strength when compared to normal self-compacting concrete at room temperature.

The current study aims to analyze a multi-storey RCC building model subjected to lateral loads induced by Zone V earthquake. In this method, a G+14 storey building is modeled using ETABS v.20, the model is acted upon by a peak acceleration of an earthquake, and analysis is done by Response Spectrum Method. The building models considered incorporate lateral stiffness systems such as X-Bracings, Inverted V-Bracings (concentric), and K-Bracings (eccentric) placed at various locations in the building models to resist lateral loads. The positions, type of bracings, and their efficiency in resisting the earthquake loads are studied considering seismic parameters such as storey displacement, base shear, overturning moment, storey stiffness, and time period. The most efficient lateral system and its position, based on models performance is observed. From the studies, it is found that the optimal way to place the bracings is at the center like a braced core system. The second-best position would be to place them at the middle periphery of the building. Eccentric bracings perform the best compared to any other type of bracings.

A rise in concern is observed to determine capacity of RC Framed structure to resist seismic forces, fire effect, along with gravity loads. The analysis of structure by considering fire effect requires consideration of thermal degradation and material non-linearity. In general, a finite element program is adopted to conduct structural fire analysis. However, application of finite element software is time consuming. Hence, in the present work, post-fire performance of structure to seismic forces is determined using ETAB software. The post-fire properties of structural elements are obtained using Wickström’s empirical relationships. In the present work, RC building frame is designed as per Indian standard guidelines using linear static method by adopting gravity load, wind load, and seismic load. Further, the pushover analysis is carried out to RC framed structure by adopting displacement controlled methods until peak roof displacement is reached. Later, RC members are subjected to standard fire using Wickström’s empirical relationship to obtain temperature isotherms. Further, 500 °C isotherm is used to obtain reduced cross section of the RC Frame members. Various fire spread scenarios were considered to determine the criticality of fire and seismic effect. It is observed that fire on the ground floor will greatly affect the structural performance of seismic loads.

An accurate and real-time time sensing of the parameters plays a significant role in Structural Health Monitoring systems (SHM) as they are prone to numerous environmental conditions. This aspect can be addressed using Fiber Bragg Grating sensors. The fiber Bragg grating sensor is only desired as they are immune to Electromagnetic radiation. The fiber Bragg grating sensors and the Internet of Things are the key enablers for online real-time sensing. In this paper, FBG was bound to the base plate of the suspension bridge structure. A thermal loading was applied, and the strain was determined using the change in the wavelength of Bragg grating through the FBG interrogator. The Interrogator was connected to the gateway through which the real-time value of the strain can be measured from a long distance. The experimental setup is demonstrated in the paper.

Base isolation is a technique that has been used to protect structures from the damaging effect of earthquakes. The addition of isolators at the base improves the building structures’ adaptability. The majority of the fundamental isolation solutions created over time only offer “partial” isolation. “Partial” in the sense that the structure's flexibility and energy dissipation mechanisms, along with the incorporation of base isolation devices, are mostly responsible for reducing the force transferred and the ensuing responsive motions. U-shaped hybrid (steel plus rubber) dampers dissipate energy through elasto-plastic deformation and control earthquake ground motion. The aim of this research is to assess the effectiveness of a U-shaped hybrid system in reducing base shear and storey drift. In this research, the effectiveness of a U-shaped damper is compared to that of a lead rubber bearing (LRB) isolator by attaching it to a five-storey building. Using SAP2000 software, nonlinear time history analysis was carried out. This research found that the use of a U-shaped hybrid isolator was beneficial in minimizing structural responses, which may result in less damage.

The rapid advancement of the Internet of Things (IoT) has revolutionized data acquisition, enabling the collection of vast amounts of real-time information from various sensors and devices. This paper introduces an IoT-based data acquisition system that focuses on capturing Strain Data and Temperature of a bridge infrastructure efficiently, thereby enabling structural health monitoring of bridges. The system architecture integrates a NodeMCU, including a strain gauge sensor and temperature sensor, interconnected through a robust communication infrastructure. Sway analysis, enabled by the strain gauge sensor, allows for monitoring the lateral movement and stability of the bridge, providing valuable information for assessing its dynamic behavior. Additionally, the system facilitates strain determination at different points of the bridge, allowing engineers to understand force distribution and identify potential weak spots. Leveraging standard protocols such as MQTT and Restful, the system facilitates seamless data transmission between devices and central data processing on the web using ThingSpeak. Key features of the system include real-time data monitoring for the structural health assessment of bridges. The continuous monitoring of structural health parameters, such as strain and temperature, enhances safety, optimizes resource allocation, and extends the lifespan of critical bridge infrastructure. The system's scalability allows for the integration of a large number of devices and the handling of high-volume data streams, enabling comprehensive structural health monitoring across multiple bridges. Experimental trials conducted in a real-world setting demonstrate the system's effectiveness in capturing accurate and reliable data for structural health analysis of bridges. Results indicate significant improvements in data acquisition speed, accuracy, and cost-effectiveness compared to traditional methods, making it a promising solution for structural health monitoring of bridges. The integration of data analytics and visualization tools enables meaningful presentation and analysis of the collected data, supporting informed decision-making processes for bridge maintenance and safety. This IoT-based data acquisition system holds immense potential across various domains, including industrial automation, healthcare, and infrastructure management, offering a proactive approach to bridge maintenance and safety. It enables enhanced monitoring, resource optimization, and informed decision-making for maintaining the integrity and longevity of bridge infrastructures. In conclusion, this paper highlights the innovative IoT-based data acquisition system for structural health monitoring of bridges, showcasing its architecture, features, and benefits. By continuously capturing strain data and temperature variations, conducting sway analysis, and ensuring real-time monitoring, the system empowers civil engineers and infrastructure managers to proactively manage and maintain critical bridge infrastructure efficiently and effectively. With its scalability and cost-effectiveness, this IoT-based solution represents a significant advancement in the field of structural engineering and promises a safer and more sustainable future for bridge infrastructure worldwide.