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Water distribution networks (WDNs) are complex interconnected networks that require extensive planning and maintenance to ensure good quality water is delivered to all consumers. The main task of WDNs is to provide consumers with a minimum acceptable level of supply (in terms of pressure, availability, and water quality) at all times under a range of operating conditions. However, the water infrastructure in North America signifies an urgent need of upgrading the aging and deteriorating distribution systems if they are to continue to provide consumers with reliable and safe water supplies. In this context, this research is proposing a new reliability model for assessing the mechanical/structural as well as hydraulic conditions of a WDN to identify failure-prone components and prioritize their renewal. The developed model provides a systematic and practical methodology to calculate the mechanical/structural reliability of the pipe and its accessories (valves, hydrants, and so on) through the consideration of historical failures of components. The research deployed pressure-dependent demand analysis to determine the hydraulic reliability of the network in meeting the pressure requirements and overall hydraulics of the network. The minimum-cut-set theory was implemented in both reliability assessments, and the outputs of the two models were integrated to provide a representative reliability of the network. A sensitivity analysis was followed to study the change in segments' attributes on the mechanical/structural and hydraulic reliabilities. The approach was implemented on the City of London, Canada, network (north and south) to test its applicability. The developed model is expected to assist decision makers in integrating the failure records with the hydraulic simulation to plan for optimum intervention actions. 2022 American Society of Civil Engineers. Scopus

Purpose The purpose of this paper is to present a new method for project tracking and control of integrated offsite and onsite activities in modular construction considering practical characteristics associated with this type of construction. Design/methodology/approach The design embraces building information modelling and integrates last planner system (LPS), linear scheduling method (LSM) and critical chain project management (CCPM) to develop tracking and control procedures for modular construction projects. The developed method accounts for constraints of resources continuity and uncertainties associated with activity duration. Features of proposed method are illustrated in a case example for tracking and control of modular projects. Findings Comparison between developed schedule and Monte Carlo simulation showed that baseline duration generated from simulation exceeds that produced by developed method by 12% and 10% for schedules with 50% and 90% confidence level, respectively. These percentages decrease based on interventions of members of project team in the LPS sessions. The case example results indicate that project is delayed 5% and experienced cost overrun of 2.5%. Originality/value Developed method integrated LPS, LSM and CCPM while using metrics for reliability assessment of linear schedules, namely, critical percent plan complete (PPCcr) and buffer index (BI). PPCcr and BI measure percentage of plan completion for critical activities and buffer consumption, respectively. The developed method provides a systematic procedure for forecasting look-ahead schedules using forecasting correction factor Δt and a newly developed tracking and control procedure that uses PPCcr and BI. Quantitative cost analysis is also provided to forecast and monitor project costs to prove the robustness of proposed framework.

Construction projects are unique and can be executed in an accelerated manner to meet market conditions. Accordingly, contractors need to compress project durations to meet client changing needs and related contractual obligations and recover from delays experienced during project execution. This acceleration requires resource planning techniques such as resource leveling and allocation. Various optimization methods have been proposed for the resource-constrained schedule compression and resource allocation and leveling individually. However, in real-world construction projects, contractors need to consider these aspects concurrently. For this purpose, this study proposes an integrated method that allows for joint consideration of the above two aspects. The method aims to optimize project duration and costs through the resources and cost of the execution modes assigned to project activities. It accounts for project cost and resource-leveling based on costs and resources imbedded in these modes of execution. The method's objective is to minimize the project duration and cost, including direct cost, indirect cost, and delay penalty, and strike a balance between the cost of acquiring and releasing resources on the one hand and the cost of activity splitting on the other hand. The novelty of the proposed method lies in its capacity to consider resource planning and project scheduling under uncertainty simultaneously while accounting for activity splitting. The proposed method utilizes the fuzzy set theory (FSs) for modeling uncertainty associated with the duration and cost of project activities and genetic algorithm (GA) for scheduling optimization. The method has five main modules that support two different optimization methods: modeling uncertainty and defuzzification module; scheduling module; cost calculations module; sensitivity IV analysis module; and decision-support module. The two optimization methods use the genetic algorithm as an optimization engine to find a set of non-dominated solutions. One optimization method uses the elitist non-dominated sorting genetic algorithm (NSGA-II), while the other uses a dynamic weighted optimization genetic algorithm. The developed scheduling and optimization method is coded in python as a stand-alone automated computerized tool to facilitate the needed iterative rescheduling of project activities and project schedule optimization. The developed method is applied to a numerical example to demonstrate its use and to illustrate its capabilities. Since the adopted numerical example is not a resource-constrained optimization example, the proposed optimization methods are validated through a multi-layered comparative analysis that involves performance evaluation, statistical comparisons, and performance stability evaluation. The performance evaluation results demonstrated the superiority of the NSGA-II against the dynamic weighted optimization genetic algorithm in finding better solutions. Moreover, statistical comparisons, which considered solutions’ mean, and best values, revealed that both optimization methods could solve the multi-objective time-cost optimization problem. However, the solutions’ range values indicated that the NSGA-II was better in exploring the search space before converging to a global optimum; NSGA-II had a trade-off between exploration (exploring the new search space) and exploitation (using already detected points to search the optimum). Finally, the coefficient of variation test revealed that the NSGA-II performance was more stable than that of the dynamic weighted optimization genetic algorithm. It is expected that the developed method can assist contractors in preparation for efficient schedule compression, which optimizes schedule and ensures efficient utilization of their resources.

Bridges are prone to severe deterioration agents which promote their degradation over the course of their lifetime. Furthermore, maintenance budgets are being trimmed. This state of circumstances entails the development of a computer vision-based method for the condition assessment of bridge elements in an attempt to circumvent the drawbacks of visual inspection-based models. Scaling is progressive local flaking or loss in the surface portion of concrete that affects the functional and structural integrity of reinforced concrete bridges. As such, this research study proposes a self-adaptive three-tier method for the automated detection and assessment of scaling severity levels in reinforced concrete bridges. The first tier relies on the integration of cross entropy function and grey wolf optimization (GWO) algorithm for the segmentation of scaling pixels. The second tier is designated for the autonomous interpretation of scaling area. In this model, a hybrid feature extraction algorithm is proposed based on the fusion of singular value decomposition and discrete wavelet transform for the efficient and robust extraction of the most dominant features in scaling images. Then an integration of Elman neural network and GWO algorithm is proposed for the sake of improving the prediction accuracies of scaling area though optimization of both structure and parameters of Elman neural network. The third tier aims at establishing a unified scaling severity index to assess the extent of severities of scaling according to its area and depth. The developed method is validated through multi-layered comparative analysis that involved performance evaluation comparisons, statistical comparisons and box plots. Results demonstrated that the developed scaling detection model significantly outperformed a set of widely-utilized classical segmentation models achieving mean squared error, mean absolute error, peak signal to noise ratio and cross entropy of 0.175, 0.407, 55.754 and 26011.019, respectively. With regards to the developed scaling evaluation model, it accomplished remarkable better and more robust performance that other meta-heuristic-based Elman neural network models and conventional prediction models. In this context, it obtained mean absolute percentage error, root-mean squared error and mean absolute error 1.513%, 29.836 and 12.066, respectively, as per split validation. It is anticipated that the developed integrated computer vision-based method could serve as the basis of automated, reliable and cost-effective inspection platform of reinforced concrete bridges which can assist departments of transportation in taking effective preventive maintenance and rehabilitation actions.

This paper introduces a newly developed model for automated monitoring and control of productivity in earthmoving operations. The model makes use of advancements in wireless sensing networks, Internet of things (IoT), and artificial intelligence. It utilizes data analytics and a dashboard to provide project managers with actionable data on the status of these operations in near-real time. The model consists of two modules; the first is a low-cost open-source remote sensing data acquisition module for collecting data throughout the execution of earthmoving operations. The collected data are sent to a cloud-based MySQL database, in which the second module is designed to (1) measure actual productivity in near-real-time, (2) detecting the location and condition of hauling roads, and (3) monitoring and reporting driving conditions over these roads. Artificial neural network (ANN) is used in cloud computing for analyzing the productivity to determine and prioritize causes behind experienced loss of productivity from that planned. This paper presents cloud computing over a web-based platform (Knowi®). Productivity measurement and analysis outputs are retrieved through any web browser. The work encompassed field and scaled laboratory experiments in the development and validation processes of the developed model. The laboratory experiments 1:24 scaled loader and dumping truck to simulate loading, hauling, and dumping operations. The data collected from the lab experiments and field work was used as input for the developed model. The results obtained highlight the accuracy of the developed model in recognition of the status of the hauling truck, traveled road condition, and in the estimated duration of the simulated earthmoving cycles.

Purpose To mitigate the problems in sensor-based facility management (FM) such as lack of detailed visual information about a built facility and the maintenance of large scale sensor deployments, an integrated data source for the facility’s life cycle should be used. Building information modeling (BIM) provides a useful visual model and database that can be used as a repository for all data captured or made during the facility’s life cycle. It can be used for modeling the sensing-based system for data collection, serving as a source of all information for smart objects such as the sensors used for that purpose. Although few studies have been conducted in integrating BIM with sensor-based monitoring system, providing an integrated platform using BIM for improving the communication between FMs and Internet of Things (IoT) companies in cases encountered failed sensors has received the least attention in the technical literature. Therefore, the purpose of this paper is to conceptualize and develop a BIM-based system architecture for fault detection and alert generation for malfunctioning FM sensors in smart IoT environments during the operational phase of a building to ensure minimal disruption to monitoring services. Design/methodology/approach This paper describes an attempt to examine the applicability of BIM for an efficient sensor failure management system in smart IoT environments during the operational phase of a building. For this purpose, a seven-story office building with four typical types of FM-related sensors with all associated parameters was modeled in a commercial BIM platform. An integrated workflow was developed in Dynamo, a visual programming tool, to integrate the associated sensors maintenance-related information to a cloud-based tool to provide a fast and efficient communication platform between the building facility manager and IoT companies for intelligent sensor management. Findings The information within BIM allows better and more effective decision-making for building facility managers. Integrating building and sensors information within BIM to a cloud-based system can facilitate better communication between the building facility manager and IoT company for an effective IoT system maintenance. Using a developed integrated workflow (including three specifically designed modules) in Dynamo, a visual programming tool, the system was able to automatically extract and send all essential information such as the type of failed sensors as well as their model and location to IoT companies in the event of sensor failure using a cloud database that is effective for the timely maintenance and replacement of sensors. The system developed in this study was implemented, and its capabilities were illustrated through a case study. The use of the developed system can help facility managers in taking timely actions in the event of any sensor failure and/or malfunction to ensure minimal disruption to monitoring services. Research limitations/implications However, there are some limitations in this work which are as follows: while the present study demonstrates the feasibility of using BIM in the maintenance planning of monitoring systems in the building, the developed workflow can be expanded by integrating some type of sensors like an occupancy sensor to the developed workflow to automatically record and identify the number of occupants (visitors) to prioritize the maintenance work; and the developed workflow can be integrated with the sensors’ data and some machine learning techniques to automatically identify the sensors’ malfunction and update the BIM model accordingly. Practical implications Transferring the related information such as the room location, occupancy status, number of occupants, type and model of the sensor, sensor ID and required action from the BIM model to the cloud would be extremely helpful to the IoT companies to actually visualize workspaces in advance, and to plan for timely and effective decision-making without any physical inspection, and to support maintenance planning decisions, such as prioritizing maintenance works by considering different factors such as the importance of spaces and number of occupancies. The developed framework is also beneficial for preventive maintenance works. The system can be set up according to the maintenance and time-based expiration schedules, automatically sharing alerts with FMs and IoT maintenance contractors in advance about the IoT parts replacement. For effective predictive maintenance planning, machine learning techniques can be integrated into the developed workflow to efficiently predict the future condition of individual IoT components such as data loggers and sensors, etc. as well as MEP components. Originality/value Lack of detailed visual information about a built facility can be a reason behind the inefficient management of a facility. Detecting and repairing failed sensors at the earliest possible time is critical to ensure the functional continuity of the monitoring systems. On the other hand, the maintenance of large-scale sensor deployments becomes a significant challenge. Despite its importance, few studies have been conducted in integrating BIM with a sensor-based monitoring system, providing an integrated platform using BIM for improving the communication between facility managers and IoT companies in cases encountered failed sensors. In this paper, a cloud-based BIM platform was developed for the maintenance and timely replacement of sensors which are critical to ensure minimal disruption to monitoring services in sensor-based FM.

Existing bridges are aging and deteriorating, raising concerns for public safety and the preservation of these valuable assets. Furthermore, the transportation networks that manage many bridges face budgetary constraints. This state of affairs necessitates the development of a computer vision-based method to alleviate shortcomings in visual inspection-based methods. In this context, the present study proposes a three-tier method for the automated detection and recognition of bridge defects. In the first tier, singular value decomposition ([Formula: see text]) is adopted to formulate the feature vector set through mapping the most dominant spatial domain features in images. The second tier encompasses a hybridization of the Elman neural network ([Formula: see text]) and the invasive weed optimization (I[Formula: see text]) algorithm to enhance the prediction performance of the ENN. This is accomplished by designing a variable optimization mechanism that aims at searching for the optimum exploration–exploitation trade-off in the neural network. The third tier involves validation through comparisons against a set of conventional machine-learning and deep-learning models capitalizing on performance prediction and statistical significance tests. A computerized platform was programmed in C#.net to facilitate implementation by the users. It was found that the method developed outperformed other prediction models achieving overall accuracy, F-measure, Kappa coefficient, balanced accuracy, Matthews’s correlation coefficient, and area under curve of 0.955, 0.955, 0.914, 0.965, 0.937, and 0.904, respectively as per cross validation. It is expected that the method developed can improve the decision-making process in bridge management systems.

Earned duration management (EDM) is an extension of earned value management (EVM) in which the schedule performance of the project is evaluated using time-based data with no dependency on project c...

PurposeThe purpose of this paper is to identify optimum crew formations at unit execution level of repetitive projects that minimize project duration, project cost, crew work interruptions and interruption costs, simultaneously.Design/methodology/approachThe model consists of four modules. The first module quantifies uncertainties associated with the crew productivity rate and quantity of work using the fuzzy set theory. The second module identifies feasible boundaries for activity relaxation. The third module computes direct cost, indirect cost and interruption costs, including idle crew cost as well as mobilization and demobilization costs. The fourth module identifies near-optimum crew formation using a newly developed multi-objective optimization model.FindingsThe developed model was able to provide improvements of 0.2, 16.86 and 12.98% for minimization of project cost, crew work interruptions and interruption costs from US$1,505,960, 8.3 days and US$8,300, as recently reported in the literature, to US$1,502,979, 6.9 days and US$7,222, respectively, without impacting the optimized project duration.Originality/valueThe novelty of this paper lies in its activity-relaxation free float that considers the effect of postponing early finish dates of repetitive activities on crew work interruptions. The introduced new float allows for calculating the required crew productivity rate that minimizes crew work interruptions without delaying successor activities and without impacting the optimized project duration. It safeguards against assignment of unnecessary costly resources.

With the increase in global population, industrialization, and urbanization, waste from construction, renovation, and demolition (CRD) activities has grown rapidly. There are some issues associated with the disposal of CRD waste in landfills. Depositing in landfills is still the main method for CRD waste disposal from the global perspective. The objective of this study is to comprehensively review the environmental impacts and management technologies for CRD waste in landfills. It includes the overview of the current CRD waste flow and relevant policies worldwide. The main environmental problems caused by CRD waste in landfills include leachate and H

Recently, the number of deteriorating bridges has drastically increased. As such, enormous amounts of resources are invested yearly to maintain the performance of bridges within acceptable ...