Civil Engineering Grand Challenges: Opportunities for Data Sensing, Information Analysis, and Knowledge Discovery

Becerik-Gerber, Burçin; Siddiqui, Mohsin; Brilakis, Ioannis; El-Anwar, Omar; El-Gohary, Nora; Mahfouz, Tarek; Jog, Gauri M.; Li, Shuai; Kandil, Amr

doi:10.1061/(asce)cp.1943-5487.0000290

Cited by 65 publications

(22 citation statements)

References 30 publications

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“…Moreover, these methods may not support (near) real time processing (Park et al 2013) which can negatively impact the timeliness and/or accuracy of resulting decisions. This issue has been cited as a major obstacle to the widespread adoption of data-driven decision-making tools in construction (Becerik-Gerber et al 2013). In light of this, the work presented in this paper seeks to create and test a new method that allows for a high-fidelity transformation of raw sensor data into contextual knowledge.…”

Section: Research Objective and Contributionsmentioning

confidence: 99%

An Exploratory Study of Sequence Alignment for Improved Sensor-Based Human Activity Recognition

Shrestha

Nath

Behzadan

2018

Construction Research Congress 2018

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Sequence alignment (SA) is a well-established technique in bioinformatics for analyzing deoxyribonucleic acid (DNA), ribonucleic acid (RNA), or protein sequences and identifying regions of similarity. The main goal of SA is to discover relationships between strings of data by deploying a series of heuristic or probabilistic methods to align a new string (e.g., DNA of a new species) with an existing string (DNA of a known species). SA has also been used sporadically in linguistics, social sciences, and finance. In this paper, the authors explore the prospect of coupling machine learning (ML) and SA to improve the output of human activity recognition (HAR) methods. In particular, several field experiments are conducted to collect heterogeneous human motion data via wearable sensors. Collected data is further mined using ML to identify sequences of activities performed in each experiment. Given the inaccuracy of sensor readings and the limitations of ML algorithms especially in handling datasets from complex human activities such as those performed by construction workers, it is expected that the resulting activity sequences not fully match actual activity sequences as observed in the field. To further clean up this inherent noise, SA is deployed to refine imperfections in the resulting activity sequences by manipulating the output of HAR and ultimately aligning noisy activity sequences with ground truth sequences. The outcome of this work is a systematic method to improve the reliability of HAR from sensor readings, which can benefit decision-making as related to task planning, resource management, productivity monitoring, and ergonomic assessment. Construction Research Congress 2018 347

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Section: Research Objective and Contributionsmentioning

confidence: 99%

An Exploratory Study of Sequence Alignment for Improved Sensor-Based Human Activity Recognition

Shrestha

Nath

Behzadan

2018

Construction Research Congress 2018

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“…Specifically, building energy consumption represents a significant portion of total energy consumption and carbon emissions worldwide (Dalia, 2014). Buildings in the United States (US), for instance, contribute to 69% of electricity use, 39% of energy use, and more than 29% of total U.S. CO2e emissions (Becerik-gerber et al 2013;USEPA 2017). Similarly, despite being challenging (Ahmad and Derrible, 2015), the importance of studying water consumption trends is essential, especially considering the fact that 36% of the world's population experiences water scarcity (Von Grebmer et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Interrelated Patterns of Electricity, Gas, and Water Consumption in Large-Scale Buildings

Movahedi¹,

Derrible²

2020

Preprint

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As cities keep growing worldwide, so does the demand for key resources such as energy (electricity and gas) and water that residents consume. Meeting the demand for these resources can be challenging and requires an understanding of their consumptions patterns. In this work, we apply XGBoost (Extreme Gradient Boosting) to predict and analyze water and energy consumption in large-scale buildings in New York City. For this, the New York City’s local law 84 extensive dataset was merged with the Primary Land Use Tax Lot Output (PLUTO) dataset as well as with other socio-economic databases. Specifically, we developed three models: electricity, gas, and water consumption. Seven major lessons were learnt in terms of interrelationships between electricity, gas, and water consumption. In particular, water and gas consumption are highly interrelated with one another (often because gas is used for water heating). Furthermore, electricity consumption is affected by building type, and electricity and water consumption are particularly interrelated in nonresidential buildings. Overall, the knowledge gained from the models and from the SHAP analysis can help planners, engineers, and policymakers develop more effective strategies and help them manage the demand for energy and water in large-scale buildings.

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“…Data sensing and analysis techniques can enable decision makers to make informed decisions in support of the management of day-to-day construction operations. Recent research works have identified construction safety and productivity as two main knowledge areas where data sensing and analysis can have a large impact [1]. Information and data requirements in support of these knowledge areas mainly consist of locations of different assets and materials.…”

Section: Introductionmentioning

confidence: 99%

Use of Ultra Wide Band Real-Time Location System on Construction Jobsites: Feasibility Study and Deployment Alternatives

Umer

Siddiqui

2020

IJERPH

Self Cite

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Ultra wide band (UWB)-based real-time location systems (RTLSs) have been widely adopted in the manufacturing industry for tracking tools, materials, and ensuring safety. Researchers in the construction domain have investigated similar uses for UWB-based RTLSs on construction jobsites. However, most of these investigations comprised small-scale experiments using average accuracy only to demonstrate use cases for the technology. Furthermore, they did not consider alternative deployment scenarios for practically feasible deployment of the technology. To overcome these limitations, a series of experiments were performed to study the feasibility of a commercially available RTLS on the construction jobsites. The focus of the work was on feasibility in terms of accuracy analysis of the system for a large experimental site, the level of effort requirements for deployment, and the impact of deployment alternatives on the accuracy of the system. The results found that average accuracy was found to be a misleading indicator of the perceived system performance (i.e., 95th percentile values were considerably higher than average values). Moreover, accuracy is significantly affected by the deployment alternatives. Collectively, the results arising from the study could help construction/safety managers in decision making related to the deployment of UWB-based RTLSs for their construction sites.

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Civil Engineering Grand Challenges: Opportunities for Data Sensing, Information Analysis, and Knowledge Discovery

Cited by 65 publications

References 30 publications

An Exploratory Study of Sequence Alignment for Improved Sensor-Based Human Activity Recognition

An Exploratory Study of Sequence Alignment for Improved Sensor-Based Human Activity Recognition

Interrelated Patterns of Electricity, Gas, and Water Consumption in Large-Scale Buildings

Use of Ultra Wide Band Real-Time Location System on Construction Jobsites: Feasibility Study and Deployment Alternatives

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