Identification of Causes and Minimization of Delays in Highway Projects of Pakistan

The problem of delay in construction industry is a regular phenomenon worldwide, and construction industry of Pakistan has no exception, particularly in highways projects. Delay can be described as the extension of time to complete the construction project. The aim of this paper is to identify main causes of delays in highway projects of Pakistan, and to determine mitigating measures for the identified causes. The research method of this study is based on literature review, questionnaire survey and semi structured interview. From in-depth literature review, twenty-six common causes of delay were found. A questionnaire survey was carried out among construction professionals of highway projects. The causes of delay in highways projects were ranked referring to their Mean values. A semi structured interview was carried out to determine mitigation measures for the top ten causes of delays. The data gathered from questionnaire survey was analyzed using SPSS (Statistical Package for the Social Sciences) while, data collected through semi structured interviews was analyzed using Nvivo software. The findings of this study are expected to be useful for construction parties, to mitigate the delays in highway construction projects of Pakistan

Stakeholders' Management Approaches in Construction Supply Chain: A New Perspective of Stakeholder's Theory

Construction is dependent on accurate, timely and safe supply chain, otherwise whole project will be halted. Previously, it has found that most construction projects failed to complete on designated time that ultimately surges the cost as well. Although there are various approaches to deal with the situation, there is evidence that collaboration among stakeholders would reduce the risks and enhance the performance. Therefore, the aim of this study is to verify the relationship between the supply chain performance (SCP) with three stakeholder management approaches, namely supplier relationship (SR), customer relationship (CR), and risk and reward sharing (RRS). A total of 585 questionnaires were distributed using systematic probability sampling of listed construction organizations and only 258 responses were returned. The data were analyzed through the Smart PLS Software using two types of function i.e. PLS Algorithm and Bootstrapping. Based on the PLS Algorithm, the path coefficient results confirm that SR, CR, and RRS influence the SCP. It also has found that all three approaches have 56% of explaining power on SCP (R2 value = 0.560). The bootstrapping function revealed that the three hypotheses supported and this confirmed the hypotheses are true. This study enhances the relationship among stakeholders beyond the traditional collaboration to risk and reward sharing simultaneously. This integration will provide a competitive position as all members share their expertise that will ultimately improve the quality and lead time and enrich the flexibility. Thus, it can be concluded that long-term success is heavily dependent on relationships with the suppliers, customers, risk and reward sharing. This study will help construction managers to understand the importance of good relationships while doing strategic decision making

Darcy–Forchheimer Magnetized Nanofluid flow along with Heating and Dissipation Effects over a Shrinking Exponential Sheet with Stability Analysis

Nanoparticles have presented various hurdles to the scientific community during the past decade. The nanoparticles dispersed in diverse base fluids can alter the properties of fluid flow and heat transmission. In the current examination, a mathematical model for the 2D magnetohydrodynamic (MHD) Darcy–Forchheimer nanofluid flow across an exponentially contracting sheet is presented. In this mathematical model, the effects of viscous dissipation, joule heating, first-order velocity, and thermal slip conditions are also examined. Using similarity transformations, a system of partial differential equations (PDEs) is converted into a set of ordinary differential equations (ODEs). The problem is quantitatively solved using the three-step Lobatto-three formula. This research studied the effects of the dimensionlessness, magnetic field, ratio of rates, porosity, Eckert number, Prandtl number, and coefficient of inertia characteristics on fluid flow. Multiple solutions were observed. In the first solution, the increased magnetic field, porosity parameter, slip effect, and volume percentage of the copper parameters reduce the velocity field along the η-direction. In the second solution, the magnetic field, porosity parameter, slip effect, and volume percentage of the copper parameters increase the η-direction velocity field. For engineering purposes, the graphs show the impacts of factors on the Nusselt number and skin friction. Finally, the stability analysis was performed to determine which solution was the more stable of the two

Toward Design of Internet of Things and Machine Learning-Enabled Frameworks for Analysis and Prediction of Water Quality

The degradation of water quality has become a critical concern worldwide, necessitating innovative approaches for monitoring and predicting water quality. This paper proposes an integrated framework that combines the Internet of Things (IoT) and machine learning paradigms for comprehensive water quality analysis and prediction. The IoT-enabled framework comprises four modules: sensing, coordinator, data processing, and decision. The IoT framework is equipped with temperature, pH, turbidity, and Total Dissolved Solids (TDS) sensors to collect the data from Rohri Canal, SBA, Pakistan. The acquired data is preprocessed and then analyzed using machine learning models to predict the Water Quality Index (WQI) and Water Quality Class (WQC). With this aim, we designed a machine learning-enabled framework for water quality analysis and prediction. Preprocessing steps such as data cleaning, normalization using the Z-score technique, correlation, and splitting are performed before applying machine learning models. Regression models: LSTM (Long Short-Term Memory), SVR (Support Vector Regression), MLP (Multilayer Perceptron) and NARNet (Nonlinear Autoregressive Network) are employed to predict the WQI, and classification models: SVM (Support Vector Machine), XGBoost (eXtreme Gradient Boosting), Decision Trees, and Random Forest are employed to predict the WQC. Before that, the Dataset used for evaluating machine learning models is split into two subsets: Dataset 1 and Dataset 2. Dataset 1 comprises 600 values for each parameter, while Dataset 2 includes the complete set of 6000 values for each parameter. This division enables comparison and evaluation of the models’ performance. The results indicate that the MLP regression model has strong predictive performance with the lowest Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) values, along with the highest R-squared (0.93), indicating accurate and precise predictions. In contrast, the SVR model demonstrates weaker performance, evidenced by higher errors and a lower R-squared (0.73). Among classification algorithms, the Random Forest achieves the highest metrics: accuracy (0.91), precision (0.93), recall (0.92), and F1-score (0.91). It is also conceived that the machine learning models perform better when applied to datasets with smaller numbers of values compared to datasets with larger numbers of values. Moreover, comparisons with existing studies reveal this study’s improved regression performance, with consistently lower errors and higher R-squared values. For classification, the Random Forest model outperforms others, with exceptional accuracy, precision, recall, and F1-score metrics

An IoT and machine learning solutions for monitoring agricultural water quality: a robust framework

All living things, comprising animals, plants, and people require water to survive. The world is covered in water, just 1 percent of it is fresh and functional. The importance and value of freshwater have increased due to population growth and rising water demands. Approximately more than 70 percent of the world's freshwater is used for agriculture. Agricultural employees are the least productive, inefficient, and heavily subsidized water users in the world. They also utilize the most water overall. Irrigation consumes a considerable amount of water. The field's water supply needs to be safeguarded. A critical stage in estimating agricultural production is crop irrigation. The global shortage of fresh water is a serious issue, and it will only get worse in the years to come. Precision agriculture and intelligent irrigation are the only solutions that will solve the aforementioned issues. Smart irrigation systems and other modern technologies must be used to improve the quantity of high-quality water used for agricultural irrigation. Such a system has the potential to be quite accurate, but it requires data about the climate and water quality of the region where it will be used. This study examines the smart irrigation system using the Internet of Things (IoT) and cloud-based architecture. The water's temperature, pH, total dissolved solids (TDS), and turbidity are all measured by this device before the data is processed in a cloud using the range of machine learning (ML) approaches. Regarding water content limits, farmers are given accurate information. Farmers can increase production and water quality by using effective irrigation techniques. ML methods comprising support vector machines (SVM), random forests (RF), linear regression, Naive Bayes, and decision trees (DT) are used to categorize pre-processed data sets. Performance metrics like accuracy, precision, recall, and f1-score are used to calculate the performance of ML algorithms