Recent Advances in Civil Engineering for Sustainable Communities

The present work deals with investigating the effect of marble sludge powder on partially replacing cement in concrete. Various properties of fibre-reinforced concrete were examined experimentally with fresh concrete and hardened concrete. Two water binder ratios were selected, such as 0.35 and 0.40, and percentage replacements of 0, 5, 10, 15, 20, and 25% of marble sludge powder (MSP) and 0.5% of polypropylene 3S fibre were selected to find out the mechanical properties of FRC. The samples were tested after curing for the period of 7, 14, 28, and 56 days for the mechanical properties. Initially, the tests conducted in this work are compressive strength. Finally, an artificial neural network (ANN) was utilized in the process of developing a prediction model for compressive strength. For ANN, we plotted the experimentally determined compressive strength against the regression analysis strength after 56 days. Based on the experimental results, marble sludge powder was found to lessen the environmental impression of concrete and be economically advantageous. Using a feed-forward back-propagation neural network consisting of 8 input neurons, 2 and 1 neurons of hidden and output, respectively, which implies reliable mechanical strength, were introduced in this study. From the results, it was found that the mechanical properties of concrete were enhanced when dry marble sludge powder was incorporated up to 15% as a replacement.

Oil palm shells (OPS) can be utilized as a sustainable substitute for natural coarse aggregates in the making of concrete. Due to its various advantages in concrete manufacturing, including environmental sustainability, lower density, good insulating qualities, and lower cost. The use of appropriate additives, as well as proper design and mix proportions, can help to optimize the mechanical characteristics of concrete containing OPS. To optimize mix design, anticipate mechanical characteristics either an exhaustive set of experiments or soft computing techniques are required. To that objective, various soft computing techniques were used in this study. Firstly, a correlation matrix between various features of sustainable concrete was established. Machine learning (ML) models were developed for predicting the compressive strength (CS) of OPS-based concrete composite. Various ML models such as decision tree (DT) was developed as a conventional machine learning (CML) model, whereas Random Forest (RF), AdaBoost (AdB), and Gradient Boosting (GB) were developed as ensemble machine learning (EML) models. Hyperparameter tuning was also performed to enhance each model’s performance. As a result, all developed models predicted the CS of concrete containing OPS effectively. Models were examined using performance evaluation methods, and it was found that the GB model fared the best in both training and testing phases, with the lowest RMSE and MAE of 0.428 and 0.341, respectively, with higher R2 as 0.998. Simultaneously, the RF's predicted performance for this data was determined to be inferior, with RMSE and MAE of 2.096 and 1.578, respectively, and lower R2 value as 0.953.

Extensive research is currently focused on the concept of bio-concrete, with impressive performance outcomes. This innovative technique involves introducing bacteria into concrete to facilitate self-healing of cracks. The significant objective of the current study was to evaluate the strength-related aspects of bacterial concrete and provide evidence on the existence of bacteria in concrete specimens, which influences its properties. Two types of bacteria from the Bacillus family, Bacillus pasteurii and Bacillus cereus, were examined. The results revealed that concrete infused with B. cereus exhibited superior compressive, flexural, and split tensile strength compared to B. pasteurii bacterial concrete, with improvements of 6.1, 7.15, and 5.11%, respectively, after 28 days. The inclusion of bacteria and the utilization of ground granulated blast-furnace slag (GGBS) as a cement supplement make the concrete environmentally friendly and cost-effective and contribute to reduced CO2 emissions. Consequently, bacterial concrete significantly enhances the strength and durability of concrete structures, promoting a sustainable and eco-friendly future.

Cement manufacture is a major source of pollution in the environment as it contributes to 5–7% of total CO2 emissions globally. Quantity of cement in concrete manufacturing can be reduced by using alternative pozzolanic materials known as supplementary cementitious materials (SCMs). SCMs include a wide range of materials, such as fly ash, slag, metakaolin, silica fume, nanosilica, and other materials that are rich in silica and alumina. These materials are added to concrete mixtures in order to influence various properties of the concrete; one of its important properties is porosity as porosity can have a significant impact on the durability and strength of the concrete. To study the influence of various SCMs on the porosity, either an exhaustive set of experiments or soft computing techniques are needed. This paper presents the use of soft computing techniques as ensemble machine learning (EML) models to predict the values of porosity with differing proportions of SCMs in the concrete mix. Random forest (RF), AdaBoost (AdB), and gradient boosting (GB) were the EML models that were developed in this study. Gradient boosting was shown to be the best predictor of porosity, while the random forest model was found to be subpar after the models were examined under model efficiency parameters. For training, the coefficient of correlation (R2), mean absolute error (MAE), and root-mean-squared error (RMSE) were determined to be 0.995, 0.279, and 0.0.341 for GB, respectively, and for random forest, they were 0.979, 0.383, and 0.677, respectively.

Currently, the concrete sector is experiencing a massive problem in adapting to the concept of sustainable development since the manufacturing of ordinary Portland cement (OPC) emits about 8% of CO2, which is responsible for global warming. Thus, to reduce cement consumption, researchers are modifying conventional cement concrete by using various supplementary cementitious materials (SCMs). Among various SCMs available, fly ash (FlA) has been the most popularly used SCM. The use of FlA in the concrete industry not only promotes sustainable development by reducing cement consumption but also solves the problem associated with the disposal of FlA. In this study, various ensemble machine learning (EML) models such as random forest, AdaBoost, and gradient boosting have been developed to predict the compressive strength (CS) and ultrasonic pulse velocity (UPV) of FlA-based concrete. Database needed to develop the various models to predict the desired outputs was obtained by the experiments performed. In order to enhance the efficiency of the models, hyperparameter tuning was being done. All the developed models were able to predict the CS and UPV of FlA-based concrete. Comparison between the various models has been done on the basis of the model efficiency parameters and found that the gradient boosting was to be the best predictor whereas random forest to be the substandard.

Advancements in machine learning and their algorithm have been used in recent times to calculate the strength and robustness of concrete. The algorithm used for the prognosis of the concrete properties and their relationship with other constituents from the data has added ease to the prediction of strength. This study attempts to use advanced machine algorithms and categorization feasibility based on accuracy and implementation. This study is done by using gradient boosting, XGBoost, CATBoost, LightGBM, Random Forest Regressor, and AdaBoost Regressor to predict strength. This study categorizes the proportional analysis of advanced machine learning algorithms and earlier used algorithms for strength prediction and their effectiveness based on accuracy and the data features. Feature engineering shows the importance of feature variables and their relationships using a different algorithm and filters out the less important features. The insights of using such concepts bring numerous possibilities for reducing the errors for better predictions. This study can demonstrate different possibilities for making the infrastructure sustainable and predictable by studying the mechanical properties and other factors affecting the compressive strength of concrete using Machine Learning. The outcome of the different regressor models in this paper showcases that the performance of CATBOOST with MAE error of 2.69 is better compared to other algorithms utilized for prognosis of cement concrete.

The paper briefs about the developments in the construction field with the effective incorporation of different kinds of nanomaterials and their effects on the microstructure, strength and durability characteristics of concrete. The present work gives the detailed procedure for synthesizing the Titanium Dioxide Nanoparticle and strength as well as durability behavior of CM 1:3 is ascertained by partially replacing cement with TiO2 Nanoparticle in varying percentages ranging from 0 to 5 by weight. For better results, TiO2 Nanoparticles were synthesized in the laboratory by precipitation method and were characterized for size, morphology and elemental composition. The characterized SEM results indicated non-uniformity; the Scherrer formula indicated the average size of the Nanoparticle to be 9.61 nm using XRD details. Other results indicate that the cement mortar has shown better compressive strength values and improved resistance against alkalinity and acidity for 3% of TiO2 for 3, 7 and 28 days of curing.

This research paper presents a study on predicting the compressive strength (CS) of Limestone and Calcined Clay-based concrete composites using machine learning algorithms. Limestone and Calcined Clay are promising materials to replace Ordinary Portland Cement, with the potential benefits of significantly reduced carbon dioxide emissions and lower production costs. In this study, three Ensemble Machine Learning (EML) models, Gradient Boosting, Random Forest, and AdaBoost, were employed to develop predictive models for the compressive strength of the concrete composite. The models were trained using 80% of the data and tested with the remaining data. The results showed that the developed models effectively predicted the compressive strength of concrete composite with high accuracy and consistency. The findings of this research can provide valuable insights into the development of sustainable construction materials and the use of machine learning techniques in predicting the strength of concrete composites. The assessment of model efficiency revealed that the Gradient Boosting model emerged as the optimal choice for achieving accurate CS predictions, demonstrating a superior Correlation Coefficient (R2) alongside diminished values of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The Random Forest model was deemed inferior with lower R2 and higher RMSE and MAE values.

For long, fly ash is the most sought-after material in the construction industry. It is used as cementitious material as a supplement (SCM) to ordinary Portland cement (OPC) concrete as a replacement for cement. Recently, its usage as a geopolymer cement while developing geopolymer concrete (GPC) mixes has been well documented in bibliography. Apart from this, fly ash has found wide utility in every other realm of civil engineering as a material to reckon with. Fly ashes have been traditionally classified into two groups Group-C and Group-F as per ASTM. BIS is classified as Class F, Class C and Class P. These categorizations are hinged on chemical constituents and their contribution as a constituent material in concrete matrix. This paper presents the application of unsupervised machine learning techniques namely, k-means, k-medoids and fuzzy c-means algorithm have been applied to classify fly ashes on a data set of 400 instances. The chemical composition like SiO2, Al2O3, Fe2O3, CaO and LOI have been considered as fly ash attributes. The data instances are clustered optimally into three groups. K-means and k-medoids algorithms were able to cluster with minimal overlapping with similar number of instances of each group. These optimal numbers of clusters were found using elbow method. However, fuzzy c-means algorithm grouped differently by attaching a fly ash data instance to an appropriate group with higher degree of belongingness of each data instance to a particular cluster. From the results obtained, the clustering rendered by fuzzy logic approach proved to be optimal as it placed the instances in groups affixing them with degree of belongingness. Based on this clustering, group characteristics are also elicited.

The work explores the experimental investigation of Bacillus paramycoides bacteria with the concentration of 105 cells/ml in cement mortar cubes. Eggshell powder (ESP) was utilized as a cement replacement in three different percentages of 5, 10 and 15%, and jute fibre was used with a constant dose of 0.5% in volume fractions to evaluate the strength and durability properties of cement mortar. In this work, ESP was used as a nutrient source and jute fibre as a carrier material to intensify the healing efficiency of bacteria concrete. Eight mixtures were prepared and divided into three series: control (no bacteria, ESP and fibre), with and without bacteria including the eggshell shell powder and fibre. For all mixtures, mortar cube specimens were prepared for 7, 14 and 28 days to determine the compressive strength (CS) and healing efficiency. Durability properties such as sorptivity were studied for uncracked and pre-cracked specimens at 28 days under full-wet curing. Based on the test results, notable strength improvement and rate of crack healing were observed for all fibrous mixtures with bacteria and ESP than control mixes. Microstructure study such as Scanning Electron Microscopic (SEM) analysis and X-ray diffraction (XRD) analysis was investigated to evaluate the elements present in different mixtures.

To accommodate essential utility services such as HVAC systems, electric lines for electricity and computer network, plumbing for sanitary lines and water supply, etc., transverse openings are provided on the RC beams. These openings become a source of potential weakness. Thus, it is important to understand the behaviour of the beam imposed with openings and the effects on its strength and serviceability. Circular, square or nearly square holes may qualify as small openings if their depth is not more than 40% of the total depth of the beam. As a result, the design and analysis of a beam with small openings may proceed similarly to that of a solid beam. By the inclusion of openings, non-uniformity of the section is caused, which results in interruptions or disturbances in the usual stress flow and also early cracking in the vicinity of the opening. To regulate width of cracks and avoid potential premature failure of the beam, special reinforcement, around the periphery of the opening, should be provided in adequate amount. Retrofitting can be adopted to gain the beam’s strength and serviceability. One of the potential materials for fixing damaged infrastructure is carbon fibre reinforced polymer (CFRP) sheet. Nine beams were cast and 3 beams were considered as control beams, among which 3 beams (BH) were pre-cut beams of circular small openings (ratio of diameter of openings to depth of beam is less than 50%) with special reinforcement given around the opening. Remaining beams were post-cut for same diameter and repaired using CFRP at the hole circumference and beam surface. All the beams were tested under two-point loading, and the results were drawn in terms of ultimate load, cracking load, shear pattern, and load versus deflection parameter. Based on the experimental investigation conducted on reinforced concrete (RC) beams, control beams (BM) underwent maximum deflection when subjected to static loading test. Beams designed for openings, BH show least deflection which proves to be the best method for providing an opening in a beam in this study and application of CFRP helped in arresting shear cracks in the beams and enhanced the ductility of the beam.

The ELA (Equilibrium Line Altitude) and AAR (Accumulation Area Ratio) are two important parameters used to study glaciers and their response to climate change. Our study focuses on the estimation of ELA and AAR for selected glaciers in Chandra basin, for two hydrological years (HY), 2019–2020 and 2020–2021 using high-resolution multispectral datasets from Indian Remote Sensing Satellite (IRS-6) and Landsat-8. ELA has been derived considering the highest averaged satellite-derived Transient Snow Line (TSL) altitude from ablation months (June–September). The glacier equilibrium line is the boundary on a glacier where the accumulation of snow and ice equals the amount of melting and runoff. The estimated ELA has been used to calculate AAR for the selected glaciers of Chandra basin. The ratio of a glacier’s accumulation area to its overall area is the Accumulation Area Ratio. Findings reveal that during HY 2019–2020, the ELA and AAR are in the range of 4590 m–5344 m and 0.16–0.59, respectively. Similarly, ELA and AAR are estimated to be in the range of 4419 m–5298 m and 0.23–0.93, respectively, during HY 2020–2021. The higher ELA and smaller AAR values observed during HY 2019–2020 indicate that the glaciers have experienced more melting in comparison to ELA and AAR estimated during HY 2020–2021. Himalayan glaciers are an important part of the water cycle, and changes in their size and movement can impact water availability for communities’ downstream. Monitoring glaciers can help us better understand and manage our water resources.

The positions of earth observations or features, along with the properties that go with them and the spatial relationships that exist between them, are displayed using GIS (Geographic Information Systems) data. GIS statistical analysis ranges greatly and includes modeling and projections, these are typically highly computational and sophisticated, particularly whenever huge datasets must be handled. Due to its considerable quickness, precision, automation, and consistency, approaches like machine learning (ML) have been proposed as an imminent revolution in the evaluation of GIS data as computing technologies develop. The flexibility in transferring data from a particular database to a different one is possibly the most significant advantage when utilizing combined GIS and ML. The present study provides an overview of the ML models and their applications in infrastructure/urban development, health, flood prediction, groundwater detection and contamination, erosion modeling and prediction, landslide susceptibility prediction (LSP), LULCC modeling, managing forests and their resources, and biodiversity conservation using GIS tools. In addition to this, the study highlights several limitations associated with deploying different ML models in conjunction with GIS.

Monitoring the distressing effects on civil structures caused due to harsh environmental conditions, aging, and overloading is very important to assess the strength of the structure and to decide its durability. With the recent development in fiber-optic sensor fields, the fiber-optic sensors play an important role in monitoring the health of the structures by having several advantages over the other conventional sensors like resistance to electromagnetic interference, corrosion. In this article, structural health monitoring of civil structures at elevated temperatures up to about 200 °C is evaluated theoretically and demonstrated experimentally using the fiber Bragg grating (FBG) sensors. The sensor will be embedded in the different materials, the strain sensitivity at the elevated temperatures for different materials will be presented, and experimental results will be calibrated with theoretical calculations.

Building information modelling (BIM) is a comprehensive method of organizing and producing. BIM has recently served as the design manual for engineers, architects, and contractors. Engineers, architects, real estate developers, manufacturers, contractors, and other construction professionals work closely together to plan, design, and build a project using a single 3D model using a process known as building information modelling. The seismic performance of structural components for regular and irregular plans or elevations is now seen as a major problem. The usage of BIM could open new possibilities for the architectural, structural, and functional designing of structures at seismic-prone regions. The present study aims at developing a 3D architectural and structural model with a regular and irregular configuration for an enhanced seismic performance using an integrated BIM tool. Since the emphasis is now more on “performance” than “resistance”, seismic design methodology has undergone a significant change in terms of concepts and techniques. Open BIM tool Edificius from ACCA software was used to create an architectural configuration for a regular and irregular plan which was then partially integrated with another open BIM tool STAAD.Pro Connect edition for structural analysis and design. Results obtained from analysis and design using STAAD.Pro were integrated to open BIM tool RCDC for detailing, bar bending schedule, and bill of quantities. Results indicated that both the chosen configurations are ideal to be constructed at seismic-prone regions based on the deformations, moments, and shear force. It was also concluded that the open BIM can help multiple tools to be integrated with excellent accuracy and precision.

The paper explores the application of autonomous robots and neural radiance fields (NeRF) for 3D mapping and exploration in civil engineering. The integration of these advanced technologies holds significant promise for enhancing various aspects of civil engineering, including infrastructure development, construction planning, and maintenance. This study investigates key concepts, methodologies, and challenges associated with 3D mapping and exploration using autonomous robots and NeRF in the context of civil engineering projects. By harnessing the capabilities of autonomous robots and NeRF, we aim to revolutionize civil engineering practices by providing accurate and real-time 3D spatial data, optimizing construction processes, and enabling efficient maintenance and inspection of infrastructure. The findings from this research contribute to the advancement of robotic applications in the civil engineering industry, facilitating better decision-making and ensuring safer, more sustainable, and cost-effective infrastructure development.

An elevated water tank is constructed to store water at a certain height in order to pressurize the water distribution system. Elevated water tanks are often built with framed or shaft-type staging. When the staging height exceeds 15 m, shaft-type stagings are often used due to the fact that they may be constructed with slip form shuttering. In either case, lateral load analysis dominates the evaluation of seismic vulnerability. According to researchers, the elevated tanks supporting system (staging) is an especially vital structural aspect of the tanks. This paper deals with the stiffness of shaft-type elevated tank stagings. This study focus on the stiffness determination from analytical formula to software model implementation. In this work, a shaft-type raised water tank staging with varied time periods was taken to examine the difference in tank staging stiffness. It is observed that the conventional method which assumes staging stiffness calculation is very inaccurate.

Inland brackish aquaculture ponds are becoming more and more saline, hindering agriculture growth, and impacting food security in many locations of Asia. Several factors, including climate change, rising seawater levels and their mineralization, complex collector-drainage systems, and insufficient adherence to agro-technical requirements, are causing the expansion of salt-affected regions. These factors, in turn, cause a substantial decline in crop yields and the abandonment of cultivated land for agricultural use. To monitor the soil salinity in the irrigated areas of the Upputeru catchment of Andhra Pradesh, raw data derived from traditional methods were analyzed from 2022 to 2023 using an integrated conventional and geographic information system (GIS) based approach. Based on the findings of a field survey and laboratory investigations, soil salinity maps of the study area were produced to investigate the dynamic changes in soil salinity. These maps used the empirical Bayesian kriging (EBK) and inverse distance weighting (IDW) interpolation techniques. The geospatial interpolation findings demonstrated the EBK method’s high potential and accuracy for mapping longitudinal changes in irrigated regions affected by salt. In addition, the study found that intensive aquaculture has a strong relationship with soil salinity, while geographical features have a weak relationship. Based on these findings, agricultural experts and local farmers are strongly urged to take the following actions to improve the actual salinity of the soil state in the province’s irrigated areas: strategically and economically use surface water, monitor drainage networks to ensure maximum capacity, and quickly integrate traditional methods with cutting-edge GIS technologies.

Landuse and landcover change is a key driver in modeling urbanization. It is essential for sustainable urban planning. The study focuses on modeling landuse landcover for the Bengaluru urban area using the Soil and Water Assessment Tool and Geographic Information System. The landuse maps for 2001 to 2019 are used in modeling. The soil and water assessment model is calibrated and validated using observed streamflow data, and the model performance are evaluated using statistical indices. Linear, Nonlinear, Logarithmic and Exponential models were selected and analyzed. Among them, Exponential model was selected, due to high statistical value. The result shows that urbanization has a significant impact on the changes in the study area, with decrease in forest and agricultural land and an increase in urban and built-up area. The model also predicts future scenarios for 2030, which indicates further urban expansion and decline in green spaces. The linear model shows higher R2 value more than 0.94, during post monsoon. The model proved to be an effective tool for prediction and can be used to evaluate the potential impacts of different land use policies and urban planning strategies.

Rapid urbanization necessitates the discovery of viable alternatives to conventional building practices. Traditional construction methods can no longer keep up with the growing demand for new structures and infrastructure. The development of environmentally friendly and cost-effective alternatives to conventional building practices has become an important objective. New technology and materials enable us to construct buildings that are more durable, less expensive, and more productive. In addition, by employing multiple strategies, we can create a built environment that is more adaptable to our changing needs. In recent years, the use of 3D printing technology in contemporary building construction has increased in popularity. The time and money required to construct a structure can be cut in half due to the advent of 3D printing technology, which enables the rapid production of intricate structures and components. It is also used to create one-of-a-kind designs that would be impossible with more conventional construction methods. In this review, we are discussed about how 3D printing is currently being utilized in the construction industry, as well as its potential to save money and increase productivity, as well as its futuristic significance in the construction industry.

In this chapter, we consider an efficient finite element discretization of a fully nonlinear, one-dimensional Darcy–Brinkman–Forchheimer fluid model. We provide a competent Damped Newton’s method type linearization coupled with Lagrange finite elements for the numerical solution of the model. The accuracy of the finite element solution is verified by comparing it with a solution from the literature. The method proposed in this chapter is very effective in handling the nonlinear differential equations and can be readily applied to partial differential equations.

We have conducted a study on time independent MHD slip flow of UCM fluid across an elongating surface with upper grade chemical species. The fluids are transported through pipes and the flow rate depends on the pipe size. We have transformed the governing PDE’s into ODE’s by using similarity transformations and are presenting results in the form of graphs and tables. It is important to consider the various constraints that may affect the flow field and mass transfer in the study. It may be useful to use numerical methods to solve the transformed ODE’s, such as the shooting phenomena, in order to accurately and efficiently simulate the flow and mass transport processes. Additionally, the results of earlier studies are compared with those of the current study.

The construction sector is the largest employer provider in the world and with a high labor demand. Low productivity and few technological advancements have affected the construction sector for a few decades. In recent times, three-dimensional printing an automated method with layer-by-layer control has developed. Over the past decades, three-dimensional printing has changed along with new technology advancements in productive manufacturing. 3D printing has been done for design optimization and to provide changes over traditional production techniques. To compete in a market that is rapidly changing, all industries should adopt new development. The same applies to the construction sector. As a result, the construction sector is paying close attention to 3D printing technology as a new strategic challenge and also adopted it. Reviewing 3D printing is the paper’s primary goal through automation attempts from construction perspective. In several developing countries, prefabricated construction methods have gradually implemented the practice of mixing and pouring concrete on site.

A chute spillway is a hydraulic structure designed to convey water from a higher level to a lower level with a steep slope, typically used in dam structures. Flow enters through the inlet at a higher level and passes down the lined channel to outlet at the floor level. As water flows down the chute spillway, it gains kinetic energy attaining super critical velocity creating a hydraulic jump at the foot of the chute. In this study, a hydraulic jump forming in the chute spillway, with an expanding stilling basin, chute blocks, floor blocks and end sill, is simulated using SST K-omega turbulence model. Characteristics and energy dissipation of hydraulic jump are investigated by means of simulations using the finite element method. Free surface of the flow is determined by the volume of fluid method. Effects of different mesh sizes are compared. Velocities and subsequent depths are obtained.

Prefabricated vertical geodrain (PVD) is a ground improvement technique used to improve the soil’s strength and reduce its compressibility. PVDs are installed vertically in the ground and are made of geotextile or geocomposite materials. The use of IoT in PVD installation and monitoring can help optimize the technique’s performance and ensure that the desired level of improvement is achieved. Prefabricated drains are a popular ground improvement technique used to improve the strength and drainage characteristics of soil. However, the performance of prefabricated drains can be affected by various factors, such as soil properties, installation methods, and environmental conditions. To optimize the performance of prefabricated drains, it is important to monitor their performance in real-time and make adjustments as necessary. In this paper, we present an IoT-enabled monitoring system for prefabricated drains that can provide real-time data on drain performance and soil behaviour. The system includes sensors that can measure parameters such as soil moisture, pore water pressure, and drain flow rate. The data collected by the sensors is transmitted wirelessly to a central server where it is analyzed and used to optimize drain performance. We also present a case study of the application of the IoT-enabled monitoring system to a prefabricated drain installation in a construction site. The results of the case study demonstrate the effectiveness of the monitoring system in improving the performance of the prefabricated drain and reducing construction costs.

The compression index is an important consideration when figuring out how fine-grained soil settles. The compression index is determined from the oedometer consolidation test which is tedious and time-consuming. As a result, numerous correlations between the compression index and the index properties were developed. As soil is a very unpredictable substance, those correlations do not hold for all types of soil. This opens the door for the development of machine learning methods to forecast compression index. In this study, the compression index of soil is predicted using a decision tree, random forest, and multiple linear regression. Index properties, like liquid limit, natural moisture content, initial void ratio, and plasticity index are used as input variables in the machine learning models that are created to forecast the output variable compression index. The dataset used contains 359 data from diverse soil types and was gathered from several published articles (CH soil—62, CI soil—186, and CL soil—111). Since the machine learning models are trained using the training dataset before being evaluated using the testing dataset, the data has been divided into a training dataset and a testing dataset. In this paper, the impact of data splitting is also examined because it affects model performance. Mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R2) are used to assess the performance of the models. The results show that when training, decision trees perform well, whereas the testing dataset favors multiple linear regression for prediction. The data partitioning that results in the optimum performance for each model is different.

In watershed hydrology, it is challenging to physically monitor various aspects that influence the hydrological processes. To quantify these watershed processes in a basin with changing spatial and temporal characteristics, public domain hydrological models incorporating inverse modeling are considered. The quantified processes aid in the decision-making, design, and development of hydrological units. But the first confusion that arises in modeling these processes is which hydrological model should be considered and what methods should be adopted to quantify the best hydrological parameters. Even though a best model is considered hydrologists assumption of parameter insensitivity and uniqueness over varying climatic conditions and space, the conditionality of model calibration with unique technique and performance indicator is prone to the poor performance of the model. Betterment of model performance can be achieved by switching parameters sensitive to varying climatic conditions and reprieving the conditionality of model calibration. Hence, the purpose of this paper is to review (i) different hydrological models available around the globe, (ii) the selection criteria for the hydrological model and the superiority of the SWAT model, (iii) the description of the SWAT model, followed by sensitivity analysis and calibration techniques involved in SWAT output, and (iv) summaries of season-based SWAT evaluation.

The objective of the research is to develop a predictive model for evaluating the California bearing ratio (CBR) value of soaked soil by using conventional and hybrid artificial intelligence models. The study used field soil samples from a highway construction project area and gathered relevant input values based on literature recommendations and data analysis. The research aims to create reliable and simple predictive models employing artificial neural networks (ANNs) with regression analysis (RA) based on soil features such as gradation, Atterberg limits, and compaction qualities. A database comprising 197 CBR values from quality control reports of the Mid-Hill Road construction project in Nepal was compiled. The building of the model used around 70% of the data, while the validation of the model used about 30% of the data. Both RA and ANN were employed and evaluated for their prediction accuracy using the coefficient of determination (R2). The research mainly focuses on the importance of computational modeling in CBR value prediction and presents a comprehensive comparison between conventional and hybrid AI models. The findings bear significant implications for advancements in soil testing for sub-base soil applications.

Drip irrigation has become a successful and sustainable method of agricultural water management, which optimizes crop productivity. In irrigation system, water is provided to plants depending on the soil type. In agriculture, two things are crucial: first is learning about the soil’s fertility and second is measuring the moisture content of the soil. This study combines the use of augmented reality (AR) and Internet of Things (IoT) technologies to further improve the accuracy and efficacy of drip irrigation systems. IoT plays a vital role in drip irrigation with its network of interconnected devices and sensors. By deploying IoT-enabled smart sensors in agricultural fields, real-time data on soil moisture, humidity, temperature and weather conditions can be collected and analyzed. AR technology provides real-time visual overlays and interactive guides that aid in the installation, monitoring and maintenance of drip irrigation systems. Using AR applications, irrigation specialists and farmers can assess water flow rates, pressure levels and soil moisture data. The integration of AR technology into automated drip irrigation system has the potential to bring significant improvements to the agriculture industry. This suggested application uses a moisture sensor to deliver information about the soil’s moisture content and water plants accordingly. Here Arduino Uno, moisture sensor and other electronic tools are used to measure moisture content of the soil. An augmented reality prototype is proposed, using Unity software to visualize the model (Chetankumar in Augmented reality—theory, design and development, 1st edn, McGraw Hill, 2020; Amit et al. in Int J Electr Electr Data Commun 10(10):38–41, 2022). The proposed integration of AR and IoT technologies provides significant benefits, including optimized water usage, enhanced crop yield and reduced operational cost.

Integrated farming is the blend of agriculture, animal husbandry, agroforestry, aquaculture, combining many activities that are interdependent on each other creating a well-developed ecosystem which demands enormous time and manual operations. Implementation of IoT technology in integrated farming elevates the intelligence of integrated farming by reducing the need for manpower and enabling automated decision-making process, thereby saving substantial amount of time resulting in sustainable farming. This paper discusses civil engineering, in design and implementation of IoT-based integrated farming using Arduino Uno. The work implements interdependent ecosystem that includes vegetation, greenhouse, livestock, agroforestry, aquaculture, and biogas plant. The agricultural land is planned and constructed for the required ecosystem. Further it is made smarter by interfacing various sensors from vegetation, greenhouse, livestock, aquaculture, and biogas plant to cloud through Arduino using communication module/Wi-Fi. The smart integrated farming system monitors and controls soil moisture, humidity, soil nutrients, soil pH, temperature, weather conditions required for vegetation, and greenhouse while monitoring various gases, pH, and temperature in biogas maintenance, along with pH, temperature, salinity, and water level monitoring in aquaculture. The work also monitors for fire and intrusion detection in livestock. The crops, livestock are chosen depending on the climatic conditions, soil type, and budget.

The soil and water conservation measures to be implemented for quality improvement mainly require consideration of the terrain issues. In the same manner, the groundwater resources from wells in the region have to be assessed for its quality so that its suitability for different needs can be ascertained. The inland salinity threats in the semiarid tropics can get aggravated during extremities like droughts and low monsoonal rainfall. During drought period, groundwater table lowers and deteriorates groundwater quality, thereby making it unfit for drinking, cooking, industrial and arboriculture needs. In this context, the study of spatial variation of water quality parameters can help in protecting the environmental geology and subsequent management of the geo-environment of the area affected due to various issues. The unfavorable geo-environmental aspects can be reduced by means of reducing surface and groundwater pollution, sheet erosion, landslides, and land subsidence. The assessment regarding variation in groundwater quality in spatial and temporal mode is required for suggesting the line of treatment to be given for the groundwater for its suitability in various usages apart from making it potable. In this context, the spatial and temporal variations of different water quality parameters ascertained through composite water quality index can help in suggestion of possible alteration to the land use pattern, trouble free exploitation of resources without unacceptable consequences, and suggestion of artificial recharging measures that do not cause pollution of geo-environment condition of the region. The geo-environment in the study has been treated as a sustainability measure, which can be improved through investigation and prioritization of conservation measures and practices through use of temporal remote sensing and other associated data to enable the evaluation of delineated watersheds in the area by analytical hierarchy process model approach, required to prioritize the watersheds leading to evolution of action plans for maintaining sustainable geo-environment.

The Standard Penetration Test (SPT) is a widely used field test in geotechnical engineering to determine the consistency, strength, and other properties of soil. This paper presents an alternative approach to determine the Standard Penetration Resistance Value. A lot of times, due to budget limitations, time constraints, and other concerns, there is a tendency to discard this test. Often, N value is estimated from the adjacent site if the data is available, else it is discarded. Various studies have been carried out to determine factors such as shear velocity and angle of internal friction to estimate SPT-N value. This research is a novel approach to estimate N value of the soil with the help of various soil parameters. Here, N value of cohesionless soil is estimated using different techniques such as artificial neural networks (ANNs), XGBoost model, ElasticNet model, lasso regression model, extra trees, Bayesian linear regression, ridge regression, AdaBoost, and random forest models with the help of seven soil parameters, namely moisture content, soil composition (% of gravel, sand, silt, and clay content), bulk density, and dry density. The model is trained on a dataset of SPT-N values and corresponding soil properties. The performance of these models is evaluated using various statistical measures and compared with the existing empirical equations. The results show that the random forest and AdaBoost models have the most efficient performance with R2 values of 0.62 and 0.63, respectively, while their accuracies are 84.53% and 83.61%, respectively. The proposed approach can be a useful tool for geotechnical engineers to predict the SPT-N values of soils and thus facilitate more efficient and cost-effective site investigations.

The importance of precise temperature and humidity readings in a wide range of industries and scientific fields cannot be emphasized. They serve as the foundation for quality control, shelf-life evaluation, and yield optimization in industries like food, medicine, and agriculture. Real-time monitoring has entered a new era with the introduction of the ESP32 microcontroller technology, giving previously unheard-of capabilities with broad ramifications. Real-time data gathering and analysis are now possible because of the ESP32’s capability, enabling farmers to make wise decisions based on the most recent information about their crops and soil conditions. Because of this, they can quickly modify irrigation and fertilizer application, maximizing resource use and raising yields. Meanwhile, civil engineers use ESP32’s capabilities to improve construction projects’ efficiency, safety, and teamwork. The ESP32’s ability to integrate temperature and soil monitoring systems is a notable development in this area. It has the extraordinary ability to alter sectors by giving real- time, actionable knowledge.

Rapid urbanization and economic growth in Bengaluru city has resulted in highly congested road networks and hence nicknamed the “Choked City of India”. Improving the existing Mass Rapid Transit System (MRTS) is a sustainable solution to combat today’s rise in traffic congestion. Transport ecological footprint (TEF) is a tool that determines the impact of the urban infrastructure and transport modes on the environment. This study aims to understand the ecological footprint, that is, physical and energy footprints contributed by the transportation sector throughout the Bangalore Urban District (BUD). The analysis of data shows that the study area with a population of 1.36 million generates about a 1.63 million work/educational trips per day having a prime modal split of 30% for buses, 10% for 4-wheelers (4W), 22% for 2-wheelers (2W), 3% by auto rickshaw, 28% by foot 5% by bicycle and other 2% by train. The physical footprint is estimated to be approximately 7408.8 ha and the energy footprint of all four taluks comprising BUD is 1.095E + 05 ha. Therefore, it is estimated that 9 earths are required to sequester the emissions from transport. In view to reduce emissions, a scenario is proposed with a stated preference survey as Bus Transit, with urban and socio-economic attributes. A binary logit model developed using IBM SPSS software estimates that there is a 95% shift from 2W, 86.35% from auto, and 23% from 4W to bus transport. Thereby reducing the emissions rate by 44% and going down to 4earths to sequester the emissions from transport. The same scenario is forecasted for 2030 and 2050 and visualized spatially using QGIS software.

Pedestrian crossings have always been the most important road element as they provide a passage for pedestrians to cross the vehicle stream. A variety of pedestrian crossings are available all around the world viz. zebra crossing, underground crossing, sky bridge crossing, signalized controlled pelican crossing, and intersection crossings. Their establishment is related to the available crossing rules, vehicle volume, funds, and the choice of authority. The problem arises when the crossing facility is not selected adequately and when it is not used by pedestrians as required. This paper presents research conducted to obtain the additional vehicular delay caused due to signal rule violation by pedestrians at a signalized crossing which is helpful for the establishment of a suitable pedestrian crossing that can reduce the pedestrian-vehicular interaction and ultimately the overall delay for the vehicles can be reduced. Data is collected from two pelican crossing sites available in Chandigarh city of India as there is not any such study conducted in the past in this locality. A model is formulated and validated comparing with already existing similar models for the pedestrian rule violation and delay that occurred for the vehicular users at these sites are also compared with this model. It was found that the delay calculated using this model gives the values nearest to the actual conditions at the site.

Metro rail development has gained considerable traction in several Indian cities. The success of Indian infrastructure development will have a major impact on the success of a metro project that requires substantial capital expenditures. A large number of metro projects have already been completed, some are currently in progress, and many more are expected to be completed in the near future. The benefits offered by these projects are estimated in the Detailed Project Report (DPR) which also mentions different costs such as construction cost, rolling stock, signaling systems, etc. The road user cost due to diversions is seldom included in the total cost which is a major indirect contributor. This chapter is based on the data collection of the Pune Metro Rail construction in corridor II between Vanaz to Deccan, specifically related to route diversions. In this research paper, authors studied and analyzed the cost of diversion faced by the commuters during the construction of Metro Station at Nal stop on the busiest route. Traffic study using the moving traffic count survey were conducted at different locations as per guidelines given in IRC-106. The diversion cost is determined using the methods described in IRC SP 30. The cost of diversion is calculated using both distance and time related Vehicle Operating Cost (VOC) for the period of 2016–2022. The authors estimate that this cost is more than 6% of the cost of construction.

The use of drone survey and mapping has gained significant attention in recent years, particularly in the domain of infrastructure management. The SVAMITVA Scheme, which uses drone technology to survey and map rural inhabited lands, facilitating the issuance of legal ownership cards and promoting rural development. This paper demonstrates a detailed comparison between orthomosaic images generated using Pix4D against satellite map or image. The drone-based survey of Penumuru village offers an exceptionally detailed representation, facilitating detailed analysis and supporting diverse applications that demand accurate and comprehensive visual data. It highlights the transformative potential of drone-based survey and mapping, offering cost effective, accurate, and timely data for decision-making and enabling the development of sustainable and efficient infrastructure.

People can now interact with both physical and virtual objects thanks to advancements in computer technology. An innovation known as Augmented Reality (AR) coordinates computerized data with the client’s environmental elements continuously. It overlays virtual articles like data and PC-produced visuals over clients’ view of the real world. The “Interior Designing App” is an Android app with an augmented reality-based system that uses image-based tracking to position virtual objects on the real world, is our recommendation for this study. This system has been developed using Unity 3D, Vuforia, and Blender. Unity 3D has been used to create the AR application and design the functionalities in it. VUFORIA has been used to create and utilize the image targets inside the application. This has been achieved by linking the two portals and importing the necessary packages. Blender has been used to create the 3D models of objects that have been used in the application. Users can arrange objects and see how they will look in the real world with this software.

Job satisfaction is significant multifactor influencing workforce productivity. Job satisfaction is termed as positive emotional circumstances of an employee experience with particular job. Particular analysis is accomplished to examine the effects of job satisfaction towards increasing professional employee’s productivity levels in the construction organizations and to determine factors correlated with productivity decrease among professional construction employees with special reference to various construction industries in Chennai. In this study, 500 respondents selected on various job levels in construction industry are participated. Data was collected using demographic questionnaire and job performance-centric questionnaire as job descriptive index to investigate the job satisfaction level. Further questionnaire investigates effectiveness of the professional employee to increase the company productivity on managing the employee in the study. The job satisfaction level was considered as low, moderate and high, respectively. Also, their productivity was computed as moderate. Furthermore, positive association among between job satisfaction and work productivity indices was considered as significant with t-value as 2.624. The statistical analysis using SPSS software represents that productivity was significantly correlated with dimension of job satisfaction such as work autonomy and fringe benefits.

Due to the behavior of complex systems such as cities in the face of stresses, strengthening resilience to mass displacement is becoming a more pressing concern. The main objective of this study is to assess the resilience for Kandahar City. The study utilized a mixed approach, consisting of both primary and secondary information obtained through surveyed questionnaire with households and interviews with stakeholders in the study field. DPSIR was utilized as a tool for understanding the overall conditions as well as Stakeholders Analysis was also used in order to identify the views of authorities and stakeholders. After that, resilience to disasters was assessed through UNDISR framework with specific scores from zero to three chosen for each of different aspects. The results show that Kandahar City is not highly resilient to disasters such as flooding and drought. Some aspects such as poor urban governance, lack of institutional, capacity and financial capacity are the weakest and most concerned areas. On the other hand, urban issues identified are highly critical and have significant impact on the performance of urban resilience. The study proposed strategies covering different aspects of low resilience and current condition that would potentially enhance the urban resilience. The research findings can be used by planners and policymakers as well as other cities having the similar characteristics. In addition, the study could be taken as a source to start assessing urban resilience and for developing associated enhancing measures.

There are vast amount of transportation modes available today like rail, bus, car, water transport, rapid transit, etc. which satisfies the daily need of passengers for travel to their destination as well as goods transport. The Intelligent Transportation System (ITS) has a huge role to complete the process of connectivity between people and the transportation system. In recent days the Intelligent Transportation System (ITS) had critical demands due to its excellent characteristics. The Intelligent Transportation System (ITS) with EV has the ability to connect Infrastructure and vehicles in real-time via wireless technology. Further, this ITS has improved with advanced technologies, giving huge benefits to the people and goods movement in a safer and more efficient way across the globe. The present conventional transport system has a huge challenge to sustainable mobility as well as creates social, economic, and environmental challenges, which includes traffic congestion, accident of vehicle, pollution, increasing cost, etc. This paper aims to address these challenges by using an Advance Intelligent Transportation System, devising the methodology to incorporate more electric vehicles (EV), and creating EV charging infrastructure leading to sustainable mobility. With the development of smart cities, ITS with EV has become more promising in mobility, especially regarding the control of carbon emissions, energy conservation, cost reduction, etc. These interns benefit the sustainability aspects like social, economic, and environmental benefits to the people. This study has a comparison of electric and conventional vehicles which gives the general idea of sustainability. For a metropolitan city, like Bengaluru, Karnataka, India, this paper shows the generation of green energy from solar rooftop PV to supply the EV infrastructure.

The Koramangala-Challaghatta valley of area approximately 246 sq. km in Bangalore city is one of the three major watersheds, dividing the greater part of the urban city area into separate and distinct drainage zones. The valley has enclosed ponds and lakes and most of the southern and eastern city runoff goes into these water bodies and finally into the Dakshina Pinakini River. Major multinational IT companies have their offices in this region and the area is densely populated. Remote Sensing and Geographic Information Systems technology help in studying and analyzing real-time data through satellite image data sets, topography, related land use land cover changes along with catchment area analysis, runoff studies, and drainage patterns. Satellite data from Cartosat and Resourcesat has been studied along with Digital Elevation Model data and Survey of India toposheets. Land use and land cover in the city have seen a decrease of more than 90% in the vegetation cover since the past five decades, that being converted to built-up areas. Rainfall data from 1965 to 2022 has been studied and analyzed. The total rainfall Bangalore receives every year has seen a steady increase since the past 2 years, with percentage deviation from normal being 28% in 2020 and 33% in 2021. This paper has addressed the analysis of urbanization-induced flooding in the Koramangala-Challaghatta valley, and how it can be controlled and mitigated.

A study was conducted to assess the traffic situation at a specific road junction in order to determine the safety of vehicle movements and the urgent requirement for a control device to monitor and manage the traffic flow at that location. The main objective of this study was to identify deficiencies in previous research and propose a solution to the challenges faced at uncontrolled junctions with mixed traffic flows in densely populated Indian cities like Bengaluru. To gather data, various traffic flow conditions were considered, including conducting a road inventory survey, measuring traffic volume, and analyzing spot speeds for each direction at the selected junction. By analyzing the results of the traffic data, an appropriate remedial action was determined to address the existing traffic conditions and road condition. Consequently, measures such as implementing signals, markings, and signs were suggested to enhance traffic safety and regulate maneuvers at the junction. The analysis of traffic safety involved collecting and evaluating traffic data using well-established techniques.

Inventory management in the construction industry is a complex and time-consuming process that requires careful attention to detail to ensure materials and supplies are properly managed within budget and on schedule. Effective inventory management involves tracking, organizing, and optimizing construction equipment and supplies. Machine learning algorithms can play a significant role in enhancing inventory management in the construction industry. These algorithms can be used for demand forecasting, optimizing inventory levels, predicting maintenance needs, selecting suppliers, and generating reports. By analyzing historical data on material usage and equipment expenses, machine learning algorithms can predict future demand, optimize inventory levels in real-time, anticipate equipment maintenance requirements, proactively schedule maintenance, identify reliable and cost-effective suppliers, and generate reports for inventory tracking. Implementing machine learning in inventory management can help construction companies reduce costs, improve operational efficiency, enhance cash flow, and streamline procurement processes.

The construction industry plays a huge role in the growth of the country and the construction project management will aid the construction in all the aspects from inception to completion of project, the project management is the systematic way of planning and organizing of the resources to accomplish a specific task event or any activity. The project may be a one-time project or ongoing project. The resources to be managed may be personal, financial and material resources, etc. The project management is often associated with disciplines of information technology (IT) industry, manufacturing industry, healthcare industry and engineering and construction industry. As in the present time we in the construction industry are using the traditional project management (TPM) system which in other words is also called as Waterfall method. Whereas in IT and manufacturing industries they are all using the latest project management methods that is Agile project management (APM) system, all the other industries have the flexibility of applying this kind of project management system which is more dynamic and adaptive in nature. It is bit more challenging in the construction industry to apply the APM since we don’t have that flexible approach in the industry. In this work, an attempt is made to study the project management systems in brief and to understand the applicability of APM in construction industry by the semi-structured interview of the questionnaire. In this study, it is got to know that the implementation of APM in construction will be better when it is hybridized with the traditional project management system.