Data-Driven Simulation Modeling of Construction and Infrastructure Operations Using Process Knowledge Discovery

Within the architecture, engineering, and construction (AEC) domain, simulation modeling is mainly used to facilitate decision-making by enabling the assessment of different operational plans and resource arrangements, that are otherwise difficult (if not impossible), expensive, or time consuming to be evaluated in real world settings. The accuracy of such models directly affects their reliability to serve as a basis for important decisions such as project completion time estimation and resource allocation. Compared to other industries, this is particularly important in construction and infrastructure projects due to the high resource costs and the societal impacts of these projects. Discrete event simulation (DES) is a decision making tool that can benefit the process of design, control, and management of construction operations. Despite recent advancements, most DES models used in construction are created during the early planning and design stage when the lack of factual information from the project prohibits the use of realistic data in simulation modeling. The resulting models, therefore, are often built using rigid (subjective) assumptions and design parameters (e.g. precedence logic, activity durations). In all such cases and in the absence of an inclusive methodology to incorporate real field data as the project evolves, modelers rely on information from previous projects (a.k.a. secondary data), expert judgments, and subjective assumptions to generate simulations to predict future performance. These and similar shortcomings have to a large extent limited the use of traditional DES tools to preliminary studies and long-term planning of construction projects. In the realm of the business process management, process mining as a relatively new research domain seeks to automatically discover a process model by observing activity records and extracting information about processes. The research presented in this Ph.D. Dissertation was in part inspired by the prospect of construction process mining using sensory data collected from field agents. This enabled the extraction of operational knowledge necessary to generate and maintain the fidelity of simulation models. A preliminary study was conducted to demonstrate the feasibility and applicability of data-driven knowledge-based simulation modeling with focus on data collection using wireless sensor network (WSN) and rule-based taxonomy of activities. The resulting knowledge-based simulation models performed very well in properly predicting key performance measures of real construction systems. Next, a pervasive mobile data collection and mining technique was adopted and an activity recognition framework for construction equipment and worker tasks was developed. Data was collected using smartphone accelerometers and gyroscopes from construction entities to generate significant statistical time- and frequency-domain features. The extracted features served as the input of different types of machine learning algorithms that were applied to various construction activities. The trained predictive algorithms were then used to extract activity durations and calculate probability distributions to be fused into corresponding DES models. Results indicated that the generated data-driven knowledge-based simulation models outperform static models created based upon engineering assumptions and estimations with regard to compatibility of performance measure outputs to reality

Automated Measurement of Heavy Equipment Greenhouse Gas Emission: The case of Road/Bridge Construction and Maintenance

Road/bridge construction and maintenance projects are major contributors to greenhouse gas (GHG) emissions such as carbon dioxide (CO2), mainly due to extensive use of heavy-duty diesel construction equipment and large-scale earthworks and earthmoving operations. Heavy equipment is a costly resource and its underutilization could result in significant budget overruns. A practical way to cut emissions is to reduce the time equipment spends doing non-value-added activities and/or idling. Recent research into the monitoring of automated equipment using sensors and Internet-of-Things (IoT) frameworks have leveraged machine learning algorithms to predict the behavior of tracked entities. In this project, end-to-end deep learning models were developed that can learn to accurately classify the activities of construction equipment based on vibration patterns picked up by accelerometers attached to the equipment. Data was collected from two types of real-world construction equipment, both used extensively in road/bridge construction and maintenance projects: excavators and vibratory rollers. The validation accuracies of the developed models were tested of three different deep learning models: a baseline convolutional neural network (CNN); a hybrid convolutional and recurrent long shortterm memory neural network (LSTM); and a temporal convolutional network (TCN). Results indicated that the TCN model had the best performance, the LSTM model had the second-best performance, and the CNN model had the worst performance. The TCN model had over 83% validation accuracy in recognizing activities. Using deep learning methodologies can significantly increase emission estimation accuracy for heavy equipment and help decision-makers to reliably evaluate the environmental impact of heavy civil and infrastructure projects. Reducing the carbon footprint and fuel use of heavy equipment in road/bridge projects have direct and indirect impacts on health and the economy. Public infrastructure projects can leverage the proposed system to reduce the environmental cost of infrastructure project

Machine Learning Recognition Models in Construction: A Systematic Review

Due to its growing acceptance and success in many sectors, there is a rapidly rising adoption and application of machine learning recognition models within construction. As a result of this adoption and usage surge, there is copious knowledge residing in different repositories. This surge makes it a daunting task for researchers and other stakeholders to access concise and summarised evidence of existing research showing the usage and adoption of different recognition models in construction. As a result, a systematic review of machine learning recognition models with their different applications in construction is inevitable. We leveraged PRISMA protocol and PICOC technique to retrieve 819 construction-related studies from SCOPUS. We grouped recognition models into Image Recognition, Pattern Recognition, Voice Recognition, and Natural Language Processing (NLP). Our thorough analysis and approach show that 53% of existing studies use Pattern Recognition, 42% Image Recognition, and 2% Voice Recognition. We identified that 45% of the studies focused on buildings, 31% on worker's health and safety, while 24% was on equipment detection, efficiency, and usage. We recommend that future studies leverage the textual and voice data generated from construction-related activities and studies. This will build more voice and NLP recognition models for training robots and other assistive technologies that can support construction workers to improve their safety and productivity. This study will guide researchers and other stakeholders in this field to widen their horizons on trends in recognition model application to construction, making informed decisions, and establish gaps in the literature while suggesting lasting contributions

A Hybrid Kinematic-Acoustic System for Automated Activity Detection of Construction Equipment

Automatically recognizing and tracking construction equipment activities is the first step towards performance monitoring of a job site. Recognizing equipment activities helps construction managers to detect the equipment downtime/idle time in a real-time framework, estimate the productivity rate of each equipment based on its progress, and efficiently evaluate the cycle time of each activity. Thus, it leads to project cost reduction and time schedule improvement. Previous studies on this topic have been based on single sources of data (e.g., kinematic, audio, video signals) for automated activity-detection purposes. However, relying on only one source of data is not appropriate, as the selected data source may not be applicable under certain conditions and fails to provide accurate results. To tackle this issue, the authors propose a hybrid system for recognizing multiple activities of construction equipment. The system integrates two major sources of data-audio and kinematic-through implementing a robust data fusion procedure. The presented system includes recording audio and kinematic signals, preprocessing data, extracting several features, as well as dimension reduction, feature fusion, equipment activity classification using Support Vector Machines (SVM), and smoothing labels. The proposed system was implemented in several case studies (i.e., ten different types and equipment models operating at various construction job sites) and the results indicate that a hybrid system is capable of providing up to 20% more accurate results, compared to cases using individual sources of data

Audio-Based Productivity Forecasting of Construction Cyclic Activities

Due to its high cost, project managers must be able to monitor the performance of construction heavy equipment promptly. This cannot be achieved through traditional management techniques, which are based on direct observation or on estimations from historical data. Some manufacturers have started to integrate their proprietary technologies, but construction contractors are unlikely to have a fleet of entirely new and single manufacturer equipment for this to represent a solution. Third party automated approaches include the use of active sensors such as accelerometers and gyroscopes, passive technologies such as computer vision and image processing, and audio signal processing. Hitherto, most studies with these technologies have aimed to activity identification or to identifying active and idle times. Given that most actions performed with construction machinery involve cyclic activities, cycle time estimation is much more relevant. In this study, hardware and software requirements were optimized toward that goal. This approach had three facets: first, signal spectral analysis was performed through the short-time Fourier transform (STFT) and the continuous wavelet transform (CWT) for comparison; second, audio and active sensor data have been submitted to a machine learning framework for activity classification accuracy comparison; and, third, Bayesian statistical models were used to include historical data for cycle time estimation enhancement. As a result, audio signals have been used along with a Markov-chain-based filter to achieve cycle time estimation with an accuracy of over 81% for up to five days of single-machine operation

Automatic estimation of excavator actual and relative cycle times in loading operations

This paper proposes a framework to automatically determine the productivity and operational effectiveness of an excavator. The method estimates the excavator\u27s actual, theoretical, and relative cycle times in the loading operation. Firstly, a supervised learning algorithm is proposed to recognize excavator activities using motion data obtained from four inertial measurement units (IMUs) installed on different moving parts of the machine. The classification algorithm is offline trained using a dataset collected via an excavator operated by two operators with different levels of competence in different operating conditions. Then, an approach is presented to estimate the cycle time based on the sequence of activities detected using the trained classification model. Since operating conditions can significantly influence the cycle time, the actual cycle time cannot solely reveal the machine\u27s performance. Hence, a benchmark or reference is required to analyze the actual cycle time. In the second step, the theoretical cycle time of an excavator is automatically estimated based on the operating conditions, such as swing angle and digging depth. Furthermore, two schemes are presented to estimate the swing angle and digging depth based on the recognized excavator activities. In the third step, the relative cycle time is obtained by dividing the theoretical cycle time by the actual cycle time. Finally, the results of the method are demonstrated by the implementation on two case studies which are operated by inexperienced and experienced operators. The obtained relative cycle time can effectively monitor the performance of an excavator in loading operations. The proposed method can be highly beneficial for worksite managers to monitor the performance of each machine in worksites

Construction Ergonomic Risk and Productivity Assessment Using Mobile Technology and Machine Learning

The construction industry has one of the lowest productivity rates of all industries. To remedy this problem, project managers tend to increase personnel\u27s workload (growing output), or assign more (often insufficiently trained) workers to certain tasks (reducing time). This, however, can expose personnel to work-related musculoskeletal disorders which if sustained over time, lead to health problems and financial loss. This Thesis presents a scientific methodology for collecting time-motion data via smartphone sensors, and analyzing the data for rigorous health and productivity assessment, thus creating new opportunities in research and development within the architecture, engineering, and construction (AEC) domain. In particular, first, a novel hypothesis is proposed for predicting features of a given body posture, followed by an equation for measuring trunk and shoulder flexions. Experimental results demonstrate that for eleven of the thirteen postures, calculated risk levels are identical to true values. Next, a machine learning-based methodology was designed and tested to calculate workers\u27 productivity as well as ergonomic risks due to overexertion. Results show that calculated productivity values are in very close agreement with true values, and all calculated risk levels are identical to actual values. The presented data collection and analysis framework has a great potential to improve existing practices in construction and other domains by overcoming challenges associated with manual observations and direct measurement techniques

Automated Approach for the Enhancement of Scaffolding Structure Monitoring with Strain Sensor Data

Construction researchers have made a significant effort to improve the safety of scaffolding structures, as a large proportion of workers are involved in construction activities requiring scaffolds. However, most past studies focused on design and planning aspects of scaffolds. While limited studies investigated scaffolding safety during construction, they are limited to simple cases only with limited failure modes and simple scaffolds. In response to this limitation, this study aims to develop an automated scaffold monitoring approach capable of monitoring large scaffolds. Accordingly, this study developed an automated scaffold safety monitoring framework that leverages sensor data collected from a scaffold, scaffold modeling techniques, and a machine-learning approach. The proposed framework is based on the capability of the machine-learning approach to identify patterns, which in this study are the patterns of the scaffold structural response based on different loads acting on it. Due to the cost and safety issues related to testing an actual scaffold with varying load applications, the scaffold monitoring framework was experimentally tested under a controlled laboratory setting with a single-bay two-story scaffold with four safety cases. After the field trial, this approach was applied on a four-bay and three-story scaffold involving 1,411 safety cases through computational exploration. During this process, this study integrated a divide-and-conquer strategy with machine-learning models to improve the performance of large-scale classification. The results show that the proposed scaffold monitoring approach is capable of large-scale classification of scaffold safety status. Therefore, this approach can be reliably applied to monitor similar scaffolds on construction sites. Further, this approach is replicable to solve other classification problems. In addition, this study is expected to encourage the use of sensing technologies and data analysis techniques to develop automated monitoring approaches