Reza Akhavian

An integrated data collection and analysis framework for remote monitoring and planning of construction operations

Recent advances in data collection and operations analysis techniques have facilitated the process of designing, analyzing, planning, and controlling of engineering processes. Mathematical tools such as graphical models, scheduling techniques, operations research, and simulation have enabled engineers to create models that represent activities, resources, and the environment under which a project is taking place. Traditionally, most simulation paradigms use static or historical data to create computer interpretable representations of real engineering systems. The suitability of this approach for modeling construction operations, however, has always been a challenge since most construction projects are unique in nature as every project is different in design, specifications, methods, and standards. Due to the dynamic nature and complexity of most construction operations, there is a significant need for a methodology that combines the capabilities of traditional modeling of engineering systems and real time field data collection. This paper presents the requirements and applicability of a data-driven modeling framework capable of collecting and manipulating real time field data from construction equipment, creating dynamic 3D visualizations of ongoing engineering activities, and updating the contents of a discrete event simulation model representing the real engineering system. The developed framework can be adopted for use by project decision-makers for short-term project planning and control since the resulting simulation and visualization are completely based on the latest status of project entities.

Knowledge-Based Simulation Modeling of Construction Fleet Operations Using Multimodal-Process Data Mining

AbstractIn order to develop a realistic simulation model, it is critical to provide the model with factual input data based on the interactions and events that take place between real entities. However, the existing trend in simulation of construction fleet activities is based on estimating input parameters such as activity durations using expert judgments and assumptions. Not only may such estimations not be precise, but project dynamics can influence model parameters beyond expectation. Therefore, the simulation model may not be a proper and reliable representation of the real engineering system. In order to alleviate these issues and improve the current practice of construction simulation, a thorough approach is needed that enables the integration of field data into simulation modeling and systematic refinement of the resulting models. This paper describes the latest efforts by authors to design and test a novel methodology for multimodal-process data capturing, fusion, and mining that provides a solid...

Construction equipment activity recognition for simulation input modeling using mobile sensors and machine learning classifiers

Although activity recognition is an emerging general area of research in computer science, its potential in construction engineering and management (CEM) domain has not yet been fully investigated. Due to the complex and dynamic nature of many construction and infrastructure projects, the ability to detect and classify key activities performed in the field by various equipment and human crew can improve the quality and reliability of project decision-making and control. In particular to simulation modeling, process-level knowledge obtained as a result of activity recognition can help verify and update the input parameters of simulation models. Such input parameters include but are not limited to activity durations and precedence, resource flows, and site layout. The goal of this research is to investigate the prospect of using built-in smartphone sensors as ubiquitous multi-modal data collection and transmission nodes in order to detect detailed construction equipment activities which can ultimately contribute to the process of simulation input modeling. A case study of front-end loader activity recognition is presented to describe the methodology for action recognition and evaluate the performance of the developed system. In the designed methodology, certain key features are extracted from the collected data using accelerometer and gyroscope sensors, and a subset of the extracted features is used to train supervised machine learning classifiers. In doing so, several important technical details such as selection of discriminating features to extract, sensitivity analysis of data segmentation window size, and choice of the classifier to be trained are investigated. It is shown that the choice of the level of detail (LoD) in describing equipment actions (classes) is an important factor with major impact on the classification performance. Results also indicate that although decreasing the number of classes generally improves the classification output, considering other factors such as actions to be combined as a single activity, methodologies to extract knowledge from classified activities, computational efficiency, and end use of the classification process may as well influence ones decision in selecting an optimal LoD in describing equipment activities (classes).

Visualization, Information Modeling, and Simulation: Grand Challenges in the Construction Industry

AbstractWith the rapid advancement of sensing and computing technology and the wide adoption of mobile computing, the construction industry has faced a rise in the amount of information and data generated during the lifecycle of the construction project. To deal with a large variety of project data and information to support efficient and effective decision making, visualization, information modeling, and simulation (VIMS) has become critical in the development of capital facilities and infrastructures. The objective of this paper is to identify and investigate grand challenges in VIMS for the construction industry, to assist the academic and industry communities in establishing a future research agenda to solve VIMS challenges. In particular, 17 VIMS grand challenges were identified by an expert task force in the VIMS committee of the ASCE Computing and Information Technology Division, and then VIMS experts in the civil and construction areas from both academia and industry participated in a survey to as...

Automated knowledge discovery and data-driven simulation model generation of construction operations

Computer simulation models help construction engineers evaluate different strategies when planning field operations. Construction jobsites are inherently dynamic and unstructured, and thus developing simulation models that properly represent resource operations and interactions requires meticulous input data modeling. Therefore, unlike existing simulation modeling techniques that mainly target long-term planning and close to steady-state scenarios, a realistic construction simulation model reliable enough for short-term planning and control must be built using factual data obtained from ongoing processes of the real system. This paper presents the latest findings of authors work in designing an integrated data-driven simulation framework that employs a distributed network of sensors to collect multi-modal data from construction equipment activities. Collected data are fused to create metadata structures and data mining methods are then applied to extract key parameters and discover contextual knowledge necessary to create or refine data-driven simulation models that represent the latest conditions on the ground.

Remote Monitoring of Dynamic Construction Processes Using Automated Equipment Tracking

Construction resource planning and control is traditionally done using static data and information available from similar projects. However, the uniqueness of and uncertainties involved in each construction project may require that field data from equipment is dynamically collected, analyzed, and integrated into the decisionmaking process in order to achieve the best possible operational plan. The collected data can be used to predict the performance of a construction system based on the latest status of the project, as well as to monitor if all pieces of equipment are operating according to the plan and if any corrective action is needed. This paper presents the results of a remote tracking technique developed to capture field data from construction equipment in real time for short term monitoring and control of construction operations. The developed technique uses a .NET environment thus providing a convenient means for data collection, sorting, filtering, and interpretation. The collected data is time-stamped and thus, can be used to create a real time 3D animation stream of the ongoing operation. This facilitates the communication of project details and can be ultimately used as a verification and validation tool for the underlying simulation model.

Ergonomic analysis of construction worker's body postures using wearable mobile sensors

Construction jobs are more labor-intensive compared to other industries. As such, construction workers are often required to exceed their natural physical capability to cope with the increasing complexity and challenges in this industry. Over long periods of time, this sustained physical labor causes bodily injuries to the workers which in turn, conveys huge losses to the industry in terms of money, time, and productivity. Various safety and health organizations have established rules and regulations that limit the amount and intensity of workers physical movements to mitigate work-related bodily injuries. A precursor to enforcing and implementing such regulations and improving the ergonomics conditions on the jobsite is to identify physical risks associated with a particular task. Manually assessing a field activity to identify the ergonomic risks is not trivial and often requires extra effort which may render it to be challenging if not impossible. In this paper, a low-cost ubiquitous approach is presented and validated which deploys built-in smartphone sensors to unobtrusively monitor workers bodily postures and autonomously identify potential work-related ergonomic risks. Results indicates that measurements of trunk and shoulder flexions of a worker by smartphone sensory data are very close to corresponding measurements by observation. The proposed method is applicable for workers in various occupations who are exposed to WMSDs due to awkward postures. Examples include, but are not limited to industry laborers, carpenters, welders, farmers, health assistants, teachers, and office workers.

Construction activity recognition for simulation input modeling using machine learning classifiers

Despite recent advancements, the time, skill, and monetary investment necessary for hardware setup and calibration are still major prohibitive factors in field data sensing. The presented research is an effort to alleviate this problem by exploring whether built-in mobile sensors such as global positioning system (GPS), accelerometer, and gyroscope can be used as ubiquitous data collection and transmission nodes to extract activity durations for construction simulation input modeling. Collected sensory data are classified using machine learning algorithms for detecting various construction equipment actions. The ability of the designed methodology in correctly detecting and classifying equipment actions was validated using sensory data collected from a front-end loader. Ultimately, the developed algorithms can supplement conventional simulation input modeling by providing knowledge such as activity durations and precedence, and site layout. The resulting data-driven simulations will be more reliable and can improve the quality and timeliness of operational decisions.

Wearable sensor-based activity recognition for data-driven simulation of construction workers' activities

Wearable technologies are becoming the main interface between human and surrounding environment for a variety of context-aware and autonomous applications. Ubiquitous, small-size, and low-cost smartphones carried by everyone nowadays are equipped with a host of embedded sensors that provide groundbreaking opportunities to collect and use multimodal data in data-driven decision support systems. Simulation models are one of the most widely used decision support tools in project management that can highly benefit from the integration of contextual knowledge with the model design. In this paper, a discrete event simulation (DES) model of construction operations involving human activities is designed, enriched with wearable sensor data using smartphones, and validated. The model parameters are defined using 1) a data-driven activity recognition and 2) a static engineering estimation method for comparison. Results show that the output of the data-driven simulation model is in a closer agreement with the values observed in the real system.

Simulation-based evaluation of fuel consumption in heavy construction projects by monitoring equipment idle times

A systematic approach to idle time reduction can significantly boost the efficiency of construction equipment during their lifetime, result in higher overall productivity, and ultimately protect public health and the environment. Towards this goal, this paper describes research aimed at designing a framework for estimating heavy equipment idle times during a construction project. A distributed sensor network is deployed to communicate and present metrics about idle times and production rates and inform project managers and field operators when idle time thresholds are exceeded. The designed user interface includes a graphical representation of the site layout to visualize the status of equipment in real time in support of project management and decision-making tasks. Collected data will be also used to determine energy consumption and CO2 emission levels as the project makes progress. Using simulation modeling, various operational strategies are evaluated from the point of view of equipment emission and idle times.