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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.

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.

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.

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.

The complex, uncertain, and dynamic nature of the maintenance management environment is a source of concern to facility managers in hospitals due to the unexpected failure of building components, daily arrival of maintenance orders, and changes in schedule. In such circumstances, centralized systems become far-fetched because of their top-down approach which lacks a feedback mechanism and ignores new information. Therefore, to address any change, centralized systems have to be reformulated making it impractical, short-sighted, and problematic to adopt them in hospitals. As such, the use of centralized systems can lead to financial loss and dissatisfaction of patients. It, therefore, becomes necessary to establish a distributed maintenance management system to support the facility managers’ making-decision process. Multi-agent facility management system (MAFMS) was conceptually designed. This design employed Unified Modeling Language (UML) diagrams that illustrate the specific agents of the system and how these agents interact with each other. Maintenance data of a hospital building was used to initiate the multi-agent simulation for workflow management. The simulation results show the benefits of the proposed system, to reduce the response time to maintenance requests compared to the current maintenance system.

Workspace interferences and collisions occurring on construction jobsites are products of unexpected spatiotemporal overlaps of resources. They affect the project performance and can create an unsafe environment. Current practices for workspace planning adopt a linear/mechanistic approach in modeling, which ignores uncertainties occurring during construction operations. Those methods do not account for the emergent behavior of crews, irreversibility of decisions made in the case of interferences, and bifurcation of those decisions over the project lifecycle. This paper presents the results of qualitative and quantitative analysis of the significance of such gaps. A software tool was developed to model workspaces probabilistically and detects interferences from 4D models. The impact of uncertainties on interference magnitudes was evaluated based on available models, and an industry survey was conducted accordingly. The results suggest that compounding effects of uncertainties may significantly affect the frequency and volume of overlapped workspaces in a project (volumes can increase for as much as 220%, for a 40% deviation in the inputs). Our findings also propose crew attributes for formulating a bottom-up approach, capable of capturing crews' interactions and predicting their decision when managing collisions.

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.

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.

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.

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.

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.

article i nfo Article history: Accepted 4 December 2010 Available online 5 January 2011 Controlling construction projects necessitates controlling their time and cost in an effort to meet the planned targets. Management needs timely data that represent the status of the project to take corrective actions, if needed. This paper presents a control model that integrates different automated data acquisition technology to collect data from construction sites required for progress measurement purposes. Current automated data acquisition technologies are described, and their suitability for use in tracking and controlling construction activities is assessed. This includes bar coding, Radio Frequency Identification (RFID) 3D laser scanning, photogrammetry, multimedia, and pen-based computers. The user can move with a tablet PC in the construction site and record, take snapshots and also hand written comments about activities on site. The proposed cost/schedule control model integrates with the automated data acquisition technologies, a planning and scheduling software system, a relational database, and AutoCAD to generate progress reports that can assist project management teams in decision making.

Progress reporting is an essential management function for successful delivery of construction projects. It relies on tangible data collected from construction job sites, which is then used to compare actual work performed to that planned. One method used to collect actual work data is 3D laser scanning, where the construction site is scanned at different times to generate data, which can then be used to estimate the quantities of work performed within the time interval considered between two successive scans. Photogrammetry is another method for data collection where the geometrical properties of an object on site are generated from its photo image. This paper presents a method, which integrates 3D scanning and photogrammetry in an effort to enhance the speed and accuracy of data collection from construction sites to support progress measurement and project control. The application of the proposed method is demonstrated using a building presently under construction.

This paper presents a computer-aided design (CAD)-based site layout model designed to account for the diverse nature of construction sites. In the proposed model, the site layout problem is represented by a flexible object-based model. The model allows the configuration of physical objects and their encapsulated attributes to suit the unique demands of each project. This feature facilitates the transfer of experts' knowledge to a set of libraries imbedded in the developed model. The paper describes the structure of the proposed model and its four components: (i) user interface; (ii) database; (iii) project; and (iv) layout control modules. The functionality of these four components and their interconnectivity are also discussed. The developed model is implemented in a computer system that operates in CAD environment and makes use of object-based design concepts. Two numerical examples, drawn from the literature, are analyzed and the results are compared with those reported by other earlier. The examples demonstrate the use of the developed model and illustrate its essential features.

Automation is gaining momentum in industry, particularly in rehabilitation and inspection works of underground infrastructure facilities. This paper describes a model for automating inspection and identification of surface defects in underground water and sewer pipes. The paper describes the current efforts in identification of surface defects in underground water and sewer mains, and presents an automated system designed to assist infrastructure engineers in diagnosing defects in this class of pipe networks. It describes the general architecture of the system and its basic components, and focuses primarily on four modules designed for automating image acquisition, image processing, features extraction and classification of defects.

This paper introduces a neural network-based model for solving the percent markup estimation problem. Neural networks (NNs) are utilized as systems able to generalize solutions by learning from a set of examples representing previous encounters of problems and their corresponding solutions or decisions. NNs utilize these holistic examples (without their underlying logic) as patterns, to simulate the decision process and its related knowledge for devising solutions to new encounters even with incomplete and/or noisy information. In this paper, existing markup estimation models are reviewed and their limitations identified. The characteristics that render the markup problem more suitable for NN modeling are outlined. The markup estimation process is analyzed and the decision-governing attributes identified. Two alternative designs for the NN model are examined and their results compared. The first model is based on a single neural network architecture and the second is based on a five-network hierarchical system. As opposed to the single large network, the hierarchical model consists of four sub-networks, pertaining to the assessment of: job uncertainty; job complexity; marked conditions; and company capabilities. The results of the four sub-networks form the input to a macro-level neural network designed to estimate the optimum markup, for a given project environment. A questionnaire survey is developed to elicit the required knowledge, from general contractors in Canada and the U.S.A., pertaining to bidding situations or encounters of some past projects. Analyses of the survey responses are utilized to structure, design, implement, train and test the two NN models. The results show that the two NN models can be trained satisfactorily on the training examples presented, however the single-network model generalizes unseen examples better than the hierarchical model. Issues regarding practical implementation of the NN model, improving its generalization capabilities and integration with other decision analysis tools are outlined.