Search

Bridge decks’ maintenance funding requirements are influenced by bridge decks' current and predicted future conditions. Additionally, the serviceability of bridges may be negatively impacted by the degradation of bridge decks. Bridge inspections require considerable effort, time, cost, and resources; besides, such inspections may introduce hazards and safety concerns. This paper introduces a data-driven hybrid feature selection framework for predicting bridge deck deterioration conditions and applying it to a bridge deck in Iowa State, USA. Firstly, the Boruta algorithm, stepwise regression, and multi-layer perceptron are employed to find the best subset of features that contribute to bridge deck deterioration. Then, four classification models were developed using the best feature subset of features, namely k-nearest neighbours, random forest, artificial neural networks, and deep neural networks. The hyperparameters of the models were optimized to get their best performance. The developed models showed comparable performance, and the random forest model outperformed the other models in prediction accuracy with fewer misclassifications. The developed models are thought to reduce field inspections and give insights into the most influential factors in bridge deck deterioration conditions.

A considerable body of literature exists on automated object localization and tracking of construction operations. While GPS-based solutions have been widely investigated in many studies for outdoor tracking of these operations, indoor tracking proved to be more challenging. This paper focuses on indoor material localization and investigates the use of two remote sensing technologies—ultra-wideband and radio frequency identification—and the integrated use of these technologies to leverage the benefits of each for a cost-effective and practical solution for location identification of materials on site. The developed method is based on an experimental study conducted in two phases. In the first phase, experiments are designed and performed to evaluate the accuracy of ultra-wideband for localization, as well as to determine the optimal output power for a hand-held radio frequency identification reader. The optimal power is identified by evaluating the range measurement accuracy and maximum reading range of the hand-held radio frequency identification reader. In the second phase, the integrated use of radio frequency identification device and ultra-wideband for object localization is studied, and an improved trilateration technique is developed. The results of the experiments show an absolute error of 0.52 m and 1.15 m for 2D and 3D localization, respectively. Accordingly, the integration of these two technologies eliminates the need for using a large number of radio frequency identification reference tags on site for indoor material localization. The method is expected to enhance automated material tracking on construction sites by improving the localization accuracy and providing a straightforward data acquisition protocol. The analysis of experimental data captured in a lab setting is also presented, demonstrating the advantages of the proposed method.

State Highway Agencies (SHAs) and Departments of Transportation (DOTs) allocate their limited resources to thousands of competing projects in multi-year transportation programs using expert judgement for the expected construction costs and durations. Such estimates overlook influencing parameters known in the planning phase and the importance of building reliable databases to support decision making. Meanwhile, it is possible to generate meaningful predictions in early stages of project development based on historical data gathering and analysis. The present research introduces a newly developed method for conceptual cost and duration estimation for public highway projects utilizing an ensemble of machine learning (ML) models and data collected for projects completed between 2004 and 2015 (roads, bridges, and drainage projects). Unlike previous studies, the proposed method includes project parameters that affect construction durations and costs and were not studied simultaneously before. The parameters considered are facility type, project scope, highway type, length, width, location, level of technical complexity, and new parameters pertinent to payment and procurement methods. The developed method was tested using 29 and 56 randomly selected projects, and the results yielded a Mean Absolute Percentage Error (MAPE) of 7.4% and 4.5% for the duration and cost, respectively, which are lower than the estimation errors of methods reported in recent literature. Additionally, the generalization abilities were assessed by the Mann-Whitney test, and the developed method is found to successfully handle diverse projects. Thus, machine learning models can assist agencies in the review process of competing projects from a high-level management perspective to ultimately develop better management execution programs.

Modular construction is a promising alternative to conventional construction; offering improved productivity, quality, and safety. To realize these benefits, sequencing the module fabrication process in a manner that ensures efficient allocation of labor resources is essential. However, the varying sizes and design specifications of modules lead to high variation in process times at workstations, ineffective utilization of resources, and imbalanced production lines. To address these challenges, this paper proposes a simulation-based statistical method to plan the sequencing of module fabrication and the allocation of workers at workstations for such that productivity and control are improved. The method consists of four processes: (i) data collection to obtain historical and near real-time data; (ii) identification of significant impact factors affecting process times at workstations along the production line; (iii) development of a predictive model for forecasting process times at workstations using statistical analysis and probability distribution function; and (iv) planning the sequencing of module fabrication in a manner that ensures efficient labor allocation (i.e., crew size). The developed method is validated using data captured from a light gauge steel wall panel production line operated by a modular fabricator in Edmonton, Canada. The industrial partner produces both interior and exterior light gauge steel wall panels on a production line consisting of multiple workstations. First, five significant impact factors for each workstation among the design factors that highly influence the process times were identified in order to develop cycle time formula as a predictive model. The simulation model developed and implemented in conjunction with cycle time formula (CTF) in this case study was deemed to be a reliable predictive model (i.e. 89.39% accuracy), which can be used to improve productivity. The method is shown to be capable of assisting in decision-making by enabling production managers to better understand the effects of proposed changes to the production line prior to implementation. In this way, production managers can plan effectively and thereby reducing non-productive idle time.

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.

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.

Estimating cost contingency of construction projects depends largely on data captured from previous projects and/or experience and judgment of members of project team. Mote Carlo simulation is commonly used in estimating contingency, where its accuracy was reported to depend on number of iterations used in the simulation process, probability density functions associated with each project cost item being considered and the correlation among these cost items. The literature reveals that the latter is the most important issue for accurate estimate of contingency. It, however, requires the calculation of coefficients of correlation among cost items based on captured historical records of cost data. Subjective correlation was introduced to alleviate the difficulties associated with the calculation of these coefficients. This paper presents a newly developed method for cost contingency estimation that considers subjective correlations and allows for contingency estimation with and without computer simulation. Unlike the methods reported in the literature, the present method considers uncertainty associated with the coefficients of correlation and utilizes earlier work of the first author in calculating the variance of total project cost. It also allows for assessing the impact of variable covariance matrix on the estimated project cost using a simple and user-friendly computational platform. The application of the developed method on cost data captured from two databases demonstrates its use and accuracy in estimating cost contingency. The results are compared to those produced by others using Monte Carlo Simulation with and without correlation using an actual project data.

Construction material represents a major component of the project cost. Therefore, it is essential to control material on construction job sites. Efficient material management system requires trade-offs and optimized balance among elements of material cost including purchase cost, storage cost, opportunity cost, ordering cost, and unavailability cost. Thus, there is a need to develop an automated method for optimizing the delivery and inventory of construction materials not only in the planning phase but also in the construction phase to account for introduced changes. In this research a novel genetic algorithm – multi-layer perceptron (GA-MLP) method is proposed to generate optimized material delivery schedule. Multi-layer perceptron (MLP) is utilized to improve genetic algorithm (GA) by generating memory to overcome local minima encountered in applying GA for optimization. This automated method supports contractors to buy construction materials with the least cost and without leading to material shortage or surplus. The proposed automated method has been validated through a numerical example. The obtained results demonstrate that GA-MLP outperform GA in optimizing construction material inventory.

This study introduces a newly developed method for optimized time-cost trade-off under uncertainty. It identifies the optimal execution mode for each project activity that results in minimizing the overall project cost and (or) duration while satisfying a specified joint confidence level of both time and cost. The method uses an evolutionary-based algorithm along with a design generator of experiments and blocking techniques. The developed method accounts for managerial flexibility towards the selection of execution modes. This accommodates experience-based judgement of project managers in this process. Hence, the second fold of the developed method is a completely randomized experiment module that depicts the main effect of changing an activity mode on the project total cost and overall duration. The method provides the decision-maker a guideline for making well-informed implementation strategies. The results obtained demonstrate benefits and accuracy of the developed method and its applicability for large-scale projects.

Key performance indicators (KPIs) evaluate different aspects of projects and are used to determine the health status of projects. While there is considerable work on project quantitative performance prediction, less attention, however, has been directed towards qualitative performance prediction. This paper offers a novel framework for qualitatively measuring and predicting six important construction project KPIs using the neuro-fuzzy technique. Neuro-fuzzy models are developed to map the KPIs of three critical project stages to whole project KPIs. Subtractive clustering is utilized to automatically generate initial fuzzy inference system (FIS) models and the artificial neural network (ANN) technique is used to tune the parameters of the initial FIS models. The relative weight of each KPI is determined using a series of computing methods namely, analytic hierarchy process (AHP) and genetic algorithm (GA), to generate the performance indicator (PI). The developed models are validated with real project data showing that the rate of error is reasonably low. The results show that the AHP method is more accurate when compared to the GA method. This framework can be used in building construction projects to help decision-makers evaluate the performance of their projects.

Purpose The purpose of this paper is to present a newly developed multi-objective optimization method for the time, cost and work interruptions for repetitive scheduling while considering uncertainties associated with different input parameters. Design/methodology/approach The design of the developed method is based on integrating six modules: uncertainty and defuzzification module using fuzzy set theory, schedule calculations module using the integration of linear scheduling method (LSM) and critical chain project management (CCPM), cost calculations module that considers direct and indirect costs, delay penalty, and work interruptions cost, multi-objective optimization module using Evolver © 7.5.2 as a genetic algorithm (GA) software, module for identifying multiple critical sequences and schedule buffers, and reporting module. Findings For duration optimization that utilizes fuzzy inputs without interruptions or adding buffers, duration and cost generated by the developed method are found to be 90 and 99 percent of those reported in the literature, respectively. For cost optimization that utilizes fuzzy inputs without interruptions, project duration generated by the developed method is found to be 93 percent of that reported in the literature after adding buffers. The developed method accelerates the generation of optimum schedules. Originality/value Unlike methods reported in the literature, the proposed method is the first multi-objective optimization method that integrates LSM and the CCPM. This method considers uncertainties of productivity rates, quantities and availability of resources while utilizing multi-objective GA function to minimize project duration, cost and work interruptions simultaneously. Schedule buffers are assigned whether optimized schedule allows for interruptions or not. This method considers delay and work interruption penalties, and bonus payments.

Continuous monitoring of productivity and assessment of its variations are crucial processes that significantly contribute to success of earthmoving projects. Numerous factors may lead to productivity variations. However, these factors are subjectively identified using manual knowledge-based expert judgment. Such manual recognition process is not only subject to errors but also time-consuming. There is a lack of research work that focuses on near real-time assessment of productivity variation and its effect on cost, schedule and effective utilization of resources in earthmoving projects. This paper presents a customized multi-source automated data acquisition model that acquires data from a variety of wireless sensing technologies. The acquired multi-sensor data are transmitted to a central MySQL database. Then a newly developed data fusion algorithm is applied for truck state recognition, and hence the duration of each earthmoving state. Multi-sensor data fusion facilitates measurement of actual productivity, and consequently the assessment of productivity ratios that support continuous monitoring of productivity variation in earthmoving operations. The developed tracking and monitoring model generates an early warning that supports proactive decisions to avoid schedule delays, cost overruns, and inefficient depletion of resources. A case study is used to reveal the applicability of the proposed model in monitoring and assessing actual productivity and its deviations from planned productivity. Finally, results are discussed and conclusions are drawn highlighting the features of the proposed model.

Fusing collected inspection data provides comprehensive and relatively more accurate diagnostics of defects and accordingly more accurate condition assessment of structures. This paper presents a new two-tier method that utilized data fusion methods for condition assessment of reinforced concrete bridge decks. The method utilizes pixel and feature levels fusion of data collected from multiple nondestructive evaluation (NDE) methods such as ground penetrating radar, impact echo, half-cell potential, and electrical resistivity. Data and measurements of NDE methods are extracted from the Iowa Highway research board project 2011 report for three case studies. It is observed from the three cases that each level of data fusion has its unique advantage. The power of pixel level fusion lies in its ability to provide an overview of bridge deck deterioration in one map as it appears in the fused image. On the other hand, feature fusion works better when only specific types of defects such as corrosion, delamination, and deterioration captured from inspection carried out by each of technologies referred to above. The proposed method is tested against filed inspection methods and core sample results described in the three case studies. The main findings of this research recommend utilizing data fusion in two levels as a new method to facilitate and enhance the confidence and capabilities of inspectors in interpretation of the NDE test results.

Integration of repetitive and non-repetitive scheduling methods utilizes the merits and unique features of those methods. This paper presents a new scheduling method for repetitive projects that integrates linear scheduling (LSM) and critical chain project management (CCPM) methods. The proposed method introduces a framework for scheduling of repetitive projects; accounting for constraints of resources continuity and uncertainties associated with activity durations. It introduces a new buffer, named resource conflict buffer (RCB) to account for delays that may occur due to conflict in controlling resources among successor and predecessor activities. The developed method provides a systematic procedure for identifying several critical chains to replace the visual identification method that is currently used in linear scheduling. The features of the proposed method are illustrated in a case example for scheduling of repetitive projects using an integration of LSM and CCPM scheduling techniques. A discussion of results is performed and conclusions are drawn to highlight the features and capabilities of the proposed method.

Modular construction gained considerable momentum over the last decade due to its positive impact on project cost, schedule, quality, and safety. Current literature in this field focused on cranes selection and scheduling methods, without due consideration for optimum module configuration. This paper introduces a novel modular suitability indicator which utilizes five indices; 1) connections index (CI) to evaluate module connections using the matrix clustering technique, 2) transportation dimensions index (TDI) to evaluate module dimensions' effects on transportation, 3) transportation shipping distance index (TSDI) to evaluate the distance between manufacturing facility and the construction site, 4) crane cost penalty index (CCPI) to evaluate the crane cost relevant to the module placing rate, and 5) concrete volume index (CVI) to evaluate the project's foundation concrete quantities. Calculating the modular suitability index (MSI) provides a unified indicator to accomplish a near optimum selection of module configuration for efficient delivery in residential construction.

Purpose The complexity and criticality of healthcare services highlight the importance of maintenance management function in healthcare facilities. The purpose of this paper is to review the literature on maintenance management of healthcare facilities and hospital buildings to provide an organized literature review and identify gaps from the perspective of research and practice. Design/methodology/approach The paper categorizes the literature and adopts a review hierarchy according to maintenance management functions in hospital buildings. It explores the impact of those functions on the performance of maintenance activities in hospitals. Furthermore, it examines the role of information technology and automated decision support systems in facilitating hospital maintenance management functions and performance. Findings Literature on maintenance management in healthcare facilities and hospital buildings has so far been very limited. Recently published literature focusing on healthcare facilities management and its maintenance management functions is classified into various areas and sub-areas. The paper highlights gaps in the literature and suggests avenues for future research and improvements. Originality/value The paper contains a comprehensive listing of publications and their classifications according to various attributes. It will be useful for researchers, maintenance managers, practitioners and stakeholders concerned with facility management of hospital buildings.

The current practice of bridge inspection relies heavily on visual inspection, which suffers from several limitations, including the safety of the inspection team, the accuracy of subsurface defects detection, and subjectivity. In visual inspection, data collection is time consuming and the collected data is typically documented by completing standard inspection reports. These reports do not provide sufficient visualization of locations and/or the extent of defects. Emerging combinations of non-destructive testing (NDT) technologies and Geographic Information Systems (GIS) have the potential to circumvent some of these limitations. More research is still needed to assess the potential of these technologies to be applied in the field and to address the challenges in deploying them. The challenges include data acquisition and processing, data interpretation to identify the location and extent of defects, and the integration of results obtained from multiple technologies. This research introduces an integrated method utilizing Infrared (IR) Thermography and Ground Penetrating Radar (GPR) technologies to enhance the detection of concrete bridge defects. Integrating results can improve confidence in defect detection and quantification, and as a result can enhance the reliability of the bridge condition rating process. The integrated system is implemented in a case study of a concrete bridge deck in the city of Laval, Quebec, Canada. The obtained results are compared to detailed and visual inspection results conducted on the bridge before its demolition. The study analyzes potential, limitations, and challenges of using the proposed integrated method. In addition, the study demonstrates the feasibility of integrating the collected data in ArcGIS for enhanced visualization of the inspection results.

Design changes by owners are common to many construction projects and can cause severe delays if owners do not consider that a change in one part of the project may cause a series of changes in other parts of that project. The impact of a changed component on other unchanged components is known as the “change ripple effect”. To efficiently manage design changes, the ripple effect should be quantified and impact evaluated. This paper presents a building information modeling (BIM)-based quantification model to quantify the ripple effect of owner-requested design changes. The developed model calculates the impact of design changes and their ripple effect on a project’s total duration. After analyzing the ripple effect over time, the model updates the project schedule accordingly. To demonstrate its usefulness and illustrate its essential features, the model is applied to a case study.

A comfortable thermal indoor environment is crucial for occupants’ well-being and productivity. Building Management System (BMS) is usually used to monitor the thermal condition of buildings. One of BMS’s main challenges is in the data visualization stage, in which 2D vector graphics are used, which is not fully interactive and can only be manipulated by a trained operator. Building Information Modeling (BIM) has emerged as a useful tool in the construction industry, which can be applied in all stages of a project lifecycle. The use of BIM in facilities management is currently limited since BIM applications have primarily been implemented within the design and construction phases. The main objective of this study is to integrate a sensor-based alert system into BIM models for thermal comfort monitoring in buildings during the operational phase and visualize a building’s thermal condition virtually. In order to improve the performance of environmental monitoring management of buildings in smart cities, this research presents a newly developed integrated solution based on a BIM platform and Internet of Things (IoT). The designed prototype explores the integration of commercial BIM platforms with sensor data to create a self-updating BIM model to provide real-time thermal condition monitoring based on the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE) Standard within an office environment. The temperature and humidity values, measured by sensors, are sent to the MySQL database server. An integrated workflow was developed to compile, standardize, integrate, and visualize monitoring data in a BIM environment to facilitate interpretation, analysis, and monitoring data exchange. The developed system was able to detect the time and location of a case study office room experiencing the levels of thermal comfort/discomfort based on the targeted thresholds. In this case, thirteen levels of thermal discomfort cases, out of forty-nine data points during the test, were detected, and the developed system was also able to generate a trigger and transmit alarms to facility managers via their wireless devices in real-time. The results demonstrate that the proposed system is a visually effective monitoring system for environmental monitoring management. The fully automated developed system is expected to provide a robust and practical tool for reliable data collection, analysis, and visualization to facilitate intelligent monitoring of the thermal condition in buildings and help decision-makers make faster and better decisions, which may help in maintaining the level of occupants’ thermal comfort to a satisfactory level.

Purpose Construction projects are complex projects taking place in dynamic environments, which necessitates accounting for different uncertainties during the planning stage. There is a significant lack of management tools for repetitive projects accounting for uncertainties in the construction environment. The purpose of this paper is to present an algorithm for the optimized scheduling of repetitive construction projects under uncertainty. Design/methodology/approach Fuzzy set theory is utilized to model uncertainties associated with various input parameters. The developed algorithm has two main components: optimization component and buffering component. The optimization component presents a dynamic programming approach that processes fuzzy numbers. The buffering component converts the optimized fuzzy schedule into a deterministic schedule and inserts time buffers to protect the schedule against anticipated delays. Agreement Index (AI) is used to capture the user’s desired level of confidence in the produced schedule while sizing buffers. The algorithm is capable of optimizing for cost or time objectives. An example project drawn from literature is analysed to demonstrate the capabilities of the developed algorithm and to allow comparison of results to those previously generated. Findings Testing the algorithm revealed several findings. Fuzzy numbers can be utilized to capture uncertainty in various inputs without the need for historical data. The modified algorithm is capable of optimizing schedules, for different objectives, under uncertainty. Finally AI can be used to capture users’ desired confidence in the final schedule. Originality/value Project planners can utilize this algorithm to optimize repetitive projects schedules, while modelling uncertainty in different input parameters, without the need for relevant historical data.

Project acceleration is a common yet complicated task performed by contractors in construction projects. In repetitive projects, contractors often face having to accelerate their projects using too...

Localization and tracking of resources on construction jobsites are an emerging area where the location of materials, labor, and equipment is used to estimate productivity, measure project's progress and/or enhance jobsite safety. GPS has been widely used for outdoor tracking of construction operations. However, GPS is not suitable for indoor applications due to the lack of signal coverage; particularly inside tunnels or buildings. Several indoor localization research studies had been attempted, however such developments rely heavily on extensive external communication network infrastructures. These developments also are susceptible to electromagnetic interference in noisy construction jobsites. This paper presents indoor localization system using a microcontroller equipped with an inertial measurement unit (IMU). The IMU contains a cluster of sensors: accelerometer, gyroscope and magnetometer. The microcontroller uses a direct cosine matrix algorithm to fuse sensors data and calculate non-gravitational acceleration using nine-degrees-of-freedom motion equations. Current position is calculated based on measured acceleration and heading, while accounting for growing error in speed estimation utilizing jerk integration algorithm. Experimental results are presented to illustrate the relative effectiveness of the developed system, which is able to operate independently of any external aids and visibility conditions.

Considerable work has been carried out on risk qualitative and quantitative assessment but far less on risk identification. This paper introduces a newly developed method for risk identification, based on micro risk breakdown structure and newly introduced identification procedure called preventive root cause and effective remedial. It also introduces a risk responsibility matrix that distributes the responsibilities associated with each risk among project stakeholders and introduces a newly developed method for qualitative and quantitative assessment of each item using fuzzy set and fuzzy probability theories. Output of the proposed assessment method is pre-mitigation contingency of each risk which represents a quantitative indicator for decision making whether to mitigate or not the risk being considered. Two case studies and one numerical example are presented to demonstrate the applicability and illustrate the essential features of proposed identification, allocation, and assessment methods.

This paper presents newly developed method for detecting and locating leaks in water distribution networks utilizing two detection techniques; ground penetrating radar (GPR) and infrared photography (IR). The experimental work and field investigation were carried out over 2 years in three locations in City of Doha, Qatar to capture 115 IR image frames and 23 GPR image frames. Firstly, GPR technology is utilized to accurately define location of buried pipes. After locating these pipes, IR images are collected for simulated and actual leaks. The developed algorithm segments each image into leakage and non-leakage areas and the centroid of each leakage is calculated using Green's theorem. Subsequently, GPR images are introduced as a second layer and overlaid with IR images to compare pipes location with leak location. The method was successfully applied to detect simulated and actual leaks in summer and winter seasons with small margin of error (2.9–5.6%) in estimating leakage areas. When examining the investigated four operating conditions, it was found that the developed method can predict leaks in a more reliable way if the camera height is 2 m and the speed is 1.65 m/s in both simulated and actual leaks. The newly developed method is robust and can aid operators and city engineers in detecting and locating water leaks with high accuracy.

Purpose – The purpose of this paper is to present a newly developed fuzzy-set based model for estimating, allocating, depleting, and managing contingency fund over the life cycle of construction projects. Design/methodology/approach – Fuzzy set theory is utilized in the design and development of proposed contingency modelling framework to incorporate uncertainties associated with the development phases of construction projects. A set of developed indices, measures, and ratios are introduced to quantify and characterize these uncertainties. The developed framework is designed to incorporate expert opinion and provide user-system interaction. Findings – The results obtained from the application of the developed framework on actual project case not only illustrate its accuracy, but also demonstrate its capabilities for contingency management over life cycle of construction projects. Unlike other methods, the framework provides project managers with structured method for contingency depletion utilizing a set of depletion curves and selection factors. Originality/value – The novelty of the developed framework lies not only in its new developments for contingency estimating but also its modelling for contingency allocation and depletion. It is expected to be of direct value to industry professionals and academics interested in contingency management over the entire life cycle of construction projects. The proposed framework provides management functions and features beyond those generated through Monte Carlo simulation and even those developed using fuzzy set theory.

Considerable research work had been conducted in recent years embracing the utilization of wireless technologies in construction with a focus on identification of locations of material, equipment and personnel. A fundamental key for reliable and accurate use of these technologies is path loss models, which are used to estimate distances based on received signal strength (RSSI). This paper introduces a newly developed path loss model accounting for signal de-noising using a Kalman filter. The developed model is tested using four wireless technologies (WLAN, Bluetooth, Zigbee and Synapse SNAP), 20 experiments were carried out in laboratory environment and 1500 data sets were analyzed to investigate the accuracy of distance estimation. The results show an average of 50% enhancement in the distance estimation accuracy, which considered a potential for enhanced localization on indoor construction jobsites.

The reliability of a Bridge management System depends on the quality of visual inspection and the reliable estimation of bridge condition rating. However, the current practices of visual inspection have been identified with several limitations, such as: they are time-consuming, provide incomplete information, and their reliance on inspectors\' experience. To overcome such limitations, this paper presents an approach of automating the prediction of condition rating for bridges based on digital image analysis. The proposed methodology encompasses image acquisition, development of 3D visualization model, image processing, and condition rating model. Under this method, scaling defect in concrete bridge components is considered as a candidate defect and the guidelines in the Ontario Structure Inspection Manual (OSIM) have been adopted for developing and testing the proposed method. The automated algorithms for scaling depth prediction and mapping of condition ratings are based on training of back propagation neural networks. The result of developed models showed better prediction capability of condition rating over the existing methods such as, Naive Bayes Classifiers and Bagged Decision Tree.

Contractors and/or owners frequently need to accelerate the delivery of construction projects. Contractors may have to accelerate in order to benefit from contractual bonus, avoid penalties, recover from delays and/or avoid undesirable weather and site conditions. Owners, on the other hand, may order acceleration to meat business and operational opportunities. This paper presents an algorithm for schedule updating, dynamic rescheduling and optimized acceleration of repetitive construction projects. Schedule updating captures the exact progress on site. Dynamic rescheduling aims at capitalizing on the repetitive nature of the project to fine-tune the remaining portion of the project. Optimized acceleration presents an optimized time–cost trade-off that is tailored for repetitive projects. Through a set of iterative steps, the optimized acceleration procedure divides each activity into segments and identifies the segments that would shorten project duration if accelerated. For those identified segments, the ones with the least cost slope are selected and queued for acceleration. Through the proposed segmentation of activities this algorithm provides optimum allocation of additional acceleration resources, thus is rendered capable of identifying least cost acceleration plans. The algorithm allows users to select among different acceleration strategies such as working overtime, working double shifts, working weekends, and employing more productive crews. The presented algorithm maintains work continuity and accounts for typical and non-typical activities. The algorithm is implemented in a spreadsheet application, which automates calculations, yet allows users to fine tune the algorithm to fit the project at hand. The developed algorithm is applied to a case study drawn from literature in order to illustrate its basic features and demonstrate its accuracy.

Cracking can invite sudden failures of concrete structures. The objective of this research is to develop an integrated model based on digital image processing in developing the numerical representation of defects. The integration model consists of crack quantification, change detection, neural networks, and 3D visualization models to visualize the defects in such a way that it mimics the on-site visual inspections. The crack quantification model evaluates crack lengths based on the perimeter of the skeleton of a crack which considers the tortuosity of the crack. The change detection model is based on the Fourier Transform of digital images eliminating the need for image registration as required in the traditional. Also, the integrated model as proposed here for crack length and change detection is supported by neural networks to predict crack depth and 3D visualization of crack patterns considering crack density as a key attribute.

Water distribution networks (WDNs) face various types of hazards during their extended life. Ensuring proper functioning of WDNs has always been a major concern for utility managers because of their impact on public health and safety. Resilience is an emerging concept that aims at maintaining functionality of the WDNs. Most of the previously developed resilience frameworks employed simulation methods to assess resilience of the WDNs, focusing only on the specific aspects of resilience. There is a need to develop a holistic approach to evaluate the resilience of WDNs considering various dimensions of resilience. This paper presents a new multi-attribute resilience metric based on the robustness and redundancy of the WDNs, which can be used to achieve the purpose. The developed metric is used to evaluate the resilience of a WDN in the city of London, Ontario. An optimization framework for enhancing the current resilience level is also presented. Resilience of the network is found to increase around 20% with a $500,000 investment. A hazard scenario is then analyzed to illustrate the practicality of using this metric in selecting effective restoration strategies. The proposed metric can be utilized by water agencies to evaluate and enhance the resilience of WDNs, as well as to optimize the recovery process after disruptive events.

Most forecasting techniques for repetitive construction operations use deterministic approaches or stochastic approaches that are based on historical data of similar past projects. Utilizing such data impacts the accuracy of simulation results as it does not account for the unique characteristics of the construction operation being considered. This paper presents an automated tool for earthmoving operations that performs adaptive Discrete Event Simulation (DES) through utilizing actual captured data. Adaptation is curried out utilizing actual captured data from earthmoving operations to forecast productivity and consequently time and cost of these operations. The data was captured by GPS mounted on earthmoving hauling trucks then transferred wirelessly to a central server. A specially designed relational database was developed to store the captured data in addition to the characteristics of the earthmoving operation. The captured data implicitly accounts for factors that give rise to uncertainty such as weather. The durations captured for loading, hauling, dumping and returning activities are used to generate representative Probability Distribution Functions (PDF). DES is used to forecast fleet productivity and consequently the forecasted activity's duration and cost. In this research “StroboScope” DES engine was integrated with the relational database and Oracle Crystal Ball in standalone software.

The global wireless sensor networks market is forecasted to increase at a 14.4% annual growth rate through 2016 [1]. This emerging technology trend is fueled by recent availability of sensors that are smaller, cheaper, and more intelligent. Wireless sensor networks have many potential applications in construction management such as progress tracking, equipment management, surveillance, site safety and others. This paper focuses on designing and configuring wireless sensor networks hardware and software for selected construction applications. The framework is design based on application's objective using a rapid prototyping and iterative system refinement mechanism. The rapid prototyping allows early evaluation of the configured system behavior and performance. The proposed design incorporates a self-adaptive computing concept, which is able to self-control its internal functions and operations, while adapting to the continually changing job site environment. Five prototype designs are presented to support current practices of construction operations in outdoor and indoor environments. The paper articulates the designs objectives, system functionalities, hardware constraints and the performance measures. The developed hardware were tested in real time, and quickly adjusted until targeted performance measures were satisfied. The obtained results show the consistency of the developed prototypes with their experimental applications and their efficiency in comparison to traditional practices.

Purpose – This paper aims to present a new method to circumvent the limitations of current schedule compression methods which reduce schedule crashing to the traditional time-cost trade-off analysis, where only cost is considered. Design/methodology/approach – The schedule compression process is modeled as a multi-attributed decision making problem in which different factors contribute to priority setting for activity crashing. For this purpose, a modified format of the Multiple Binary Decision Method (MBDM) along with iterative crashing process is utilized. The method is implemented in MATLAB, with a dynamic link to MS-Project to facilitate the needed iterative rescheduling. To demonstrate the use of the developed method and to present its capabilities, a numerical example drawn from literature was analysed. Findings – When considering cost only, the generated results were in good agreement with those generated using the harmony search (HS) method, particularly in capturing the project least-cost duratio...

Scheduling of the rehabilitation activities of water main networks depends mainly on available budget and planning time. Other factors such as network reliability, criticality, location, contract size, and rehabilitation method(s) affect the optimization of the scheduling process. This paper presents a method for optimized scheduling of rehabilitation work for water distribution networks. The method utilizes unsupervised neural networks (UNNs) and mixed-integer nonlinear programming (MINLP) and performs the scheduling in two stages. In the first stage, UNNs are used to cluster water mains into groups according to locations and rehabilitation methods of water mains. In the second stage, MINLP is used to determine the number of rehabilitation contract packages and the generation of optimized scheduling of these packages considering network reliability, criticality, contract size, and planning time. In order to demonstrate the essential features of the developed method, a case study was analyzed and ...

Wrong decisions or inappropriate selection of equipment may lead to increase in cost and reduction in efficiency and effectiveness. Selecting right equipment has always been a key factor in the success of the process it is used for. In this study, superiority and inferiority ranking (SIR) methodis utilized for evaluation of most suitable offer for procurement of equipment installed inside a facility, whereas, analytical hierarchy process (AHP) is used to calculate the weights of factors that influence procurement decision. To achieve this target, a methodological framework of a series of interviews are conducted, then two questionnaire surveys are developed for identifying the important factors affecting the selection process of equipment and determining their relative importance. A solution of the problem is then designed in a model using AHP and SIR methods in addition to using the simple additive weighting (SAW) and technique for order preference by similarity to the ideal solution (TOPSIS) procedures to generate the superiority and inferiority flows. The model is generic and flexible and is used for the application of the multiple criteria decision making (MCDM) methods in the procurement process. The model also offers an efficient and convenient tool that aids its users to act in an orderly and methodical thinking, and guides them in making logical and robust decisions. A case study is presented to demonstrate the use of the developed model and sensitivity analysis is carried out to measure the robustness of the model in different scenarios.

Decision support models are needed to facilitate long-term planning and priority setting among competing alternatives. Life-cycle cost is the most frequently used economic model that considers all cost elements and related factors throughout the service life of the alternatives being considered. These cost elements and related factors are usually associated with uncertainty and subjectivity. As such, it is important to model the uncertainty arising from the assumed data over the service life of competing alternatives. Probabilistic techniques, such as Monte Carlo simulation, are commonly used to deal with such uncertainty or vagueness. However, they have been criticized for their complexity and amount of data required. This paper presents a fuzzy-based life-cycle cost model that accounts for uncertainty in a manner that disadvantages commonly encountered in probabilistic models are alleviated. The developed model utilized fuzzy set theory and interval mathematics to model vague, imprecise, qualita...

Location aware computing in construction is an emerging area where the location of materials, workforce, and equipment can be used to derive knowledge about the state of the project. This paper aims to provide a low-cost location sensing solution for indoor construction and underground facilities, where satellite navigation systems cannot be used. In this solution a set of low-cost passive Radio Frequency Identification (RFID) tags is deployed using a predefined topology to provide a good spatial coverage of the project-active area. The locations are estimated and represented by zones and coordinates. The zone detection approach uses K-Nearest Neighbor classification method to assign a zone to the target. The coordinates of the target's location are also acquired by weighted average method using the Received Signal Strength Indicator (RSSI) as weight. The approach has been experimented on a construction project in Montreal as well as in a controlled lab environment. The experimental results show an average of 0.17 as error rate for detecting the target's zone and an average of 1.3 m accuracy in detecting the coordinates. The promising results presented in this paper demonstrate the potential for a high-accuracy and low-lost location estimation method for indoor construction job sites.

The deteriorating condition of water mains in Canada and US calls for rehabilitation strategies that accounts mainly for budget and level of service constraints. These water mains have received ‘D’ grade in the two countries. Decision support models can assist decision makers regarding when to rehabilitate and whether to repair, renovate or replace section(s) of water mains. The literature indicates that decision models should account for life cycle cost, uncertainty, long-term planning, targeted levels of service and budget constraints. The objectives of this paper are to: identify and group rehabilitation methods, present decision support model to rank and select most suitable rehabilitation method(s), and study the impact of rehabilitation methods on the functional and structural performance of water mains. The developed decision support model accounts for life cycle cost of each competing scenario along with the associated uncertainty. The model, unlike available models, can effectively account for va...

PurposeConstruction labour productivity is often influenced by variations in work conditions and management effectiveness. It is substantially important to understand the nature and extent to which individual parameters affect productivity. The purpose of this paper is to focus on providing insight on parameters that affect daily job‐site labour productivity by investigating their relative significance and influence on work output.Design/methodology/approachThe methodology is based on the illustration and use of three different data analysis techniques to rank parameters that affect a certain process. These techniques include Fuzzy Subtractive Clustering, Neural Network Modelling and Stepwise Variable Selection Procedure. The first one belongs to inferential statistics, while the other two are artificial intelligence based techniques. The collection of field information, spanning over a time period of ten months, comprised of daily real time observations of job‐site operations, work progress information collected from project managers and supervisors by using customized forms, and daily weather condition recorded through internet sources. Nine parameters are considered in the study presented in this paper. The data on these parameters is examined and their relative influence and contribution in productivity estimates are assessed. The approach was to consider a limited set of parameters relating to daily job‐site productivity. The methodology presented in this paper provides insight on the relative impact of parameters, affecting labour productivity on short term or daily basis. The results based on each of the three methods are analyzed and transformed into a final ranking of parameters.FindingsThe three most important parameters are identified in the same order by the fuzzy logic and neural networks methods. Regression analysis, however, provided somewhat different results.Originality/valueThis research investigates the contribution of a set of parameters towards the variations in daily job‐site labour productivity. For practitioners such as site engineers, this is of practical importance for making daily work plans. On the other hand, the structured approach presented to perform significance ranking of parameters relevant to an engineering process, may also be of interest to other researchers and practitioners.