Modified Niched Pareto Multi-objective Genetic Algorithm for Construction Scheduling Optimization

This research proposes a Genetic Algorithm based decision support model that provides decision makers with a quantitative basis for multi-criteria decision making related to construction scheduling. In an attempt to overcome the drawbacks of similar efforts, the proposed multi-objective optimization model provides insight into construction scheduling problems. In order to generate optimal solutions in terms of the three important criteria which are project duration, cost, and variation in resource use, a new data structure is proposed to define a solution to the problem and a general Niched Pareto Genetic Algorithm (NPGA) is modified to facilitate optimization procedure. The main features of the proposed Multi-Objective Genetic Algorithm (MOGA) are: A fitness sharing technique that maintains diversity of solutions. A non-dominated sorting method that assigns ranks to each individual solution in the population is beneficial to the tournament selection process. An external archive to prevent loss of optimal or near optimal solutions due to the random effect of genetic operators. A space normalization method to avoid scaling deficiencies. The developed optimization model was applied to two case studies. The results indicate that a wider range of solutions can be obtained by employing the new approach when compared to previous models. Greater area in the decision space is considered and tradeoffs between all the objectives are found. In addition, various resource use options are found and visualized. Most importantly, the creation of a simultaneous optimization model provides better insight into what is obtainable by each option. A limitation of this research is that schedules are created under the assumption of unlimited resource availability. Schedules created with this assumption in real world situations are often infeasible given that resources are commonly constrained and not readily available. As such, a discussion is provided regarding future research as to what data structure has to be developed in order to perform such scheduling under resource constraints

A Model for Predicting Construction Worker Fatigue

Fatigue impairs workers’ judgment, reduces their productivity, and jeopardizes their safety. The paper presents a tool to predict workers’ fatiguebased on their vital signs. An experimental study was conducted in whichthe heart rate and sleep quality for three individuals were monitored usingfitness trackers (wearable sensors). The data collected were used to developtwo models based on regression analysis and Artificial Neural Networks(ANN), to predict their fatigue level. A Borg’s scale was used to estimatethe Rating of Perceived Exertion (RPE) of the participants. The two modelswere able to satisfactorily predict the RPE (workers fatigue level) with anaverage validity of 75% and 80% for the regression ANN models, respectively. The developed models can provide project managers and superintendents with early warning to avoid potential worker overexertion, injuries,and fatalities

Respirable Dust Monitoring in Construction Sites and Visualization in Building Information Modeling Using Real-time Sensor Data

Construction activities, involving cutting, drilling, and grinding of materials, often produce toxic respirable dust that can cause fatal diseases and illnesses. To protect workers from breathing excessive amounts of respirable dust at job sites, superintendents should continuously monitor the level of respirable dust in workspaces and make timely interventions for overexposed workers. However, current practices of respirable dust monitoring have critical drawbacks, and superintendents cannot accurately estimate workers’ exposures to respirable dust or make prompt decisions to protect the workers. Therefore, there is a need for real-time air dust monitoring that can be deployed ubiquitously at a construction site and be integrated as part of daily construction management. In this research, we developed a real-time dust monitoring system that comprises a network of low-cost mobile dust sensors and visualization in building information modeling (BIM). Single-board computers and dust sensors were integrated as field deployment units. Inaccurate sensors were calibrated automatically on the basis of an accurate ground truth sensor. A BIM-based visualization system was developed to present the data collected from dust sensors in real time. A prototype system was developed and tested in a controlled environment

Feasibility of LoRa for Smart Home Indoor Localization

With the advancement of low-power and low-cost wireless technologies in the past few years, the Internet of Things (IoT) has been growing rapidly in numerous areas of Industry 4.0 and smart homes. With the development of many applications for the IoT, indoor localization, i.e., the capability to determine the physical location of people or devices, has become an important component of smart homes. Various wireless technologies have been used for indoor localization includingWiFi, ultra-wideband (UWB), Bluetooth low energy (BLE), radio-frequency identification (RFID), and LoRa. The ability of low-cost long range (LoRa) radios for low-power and long-range communication has made this radio technology a suitable candidate for many indoor and outdoor IoT applications. Additionally, research studies have shown the feasibility of localization with LoRa radios. However, indoor localization with LoRa is not adequately explored at the home level, where the localization area is relatively smaller than offices and corporate buildings. In this study, we first explore the feasibility of ranging with LoRa. Then, we conduct experiments to demonstrate the capability of LoRa for accurate and precise indoor localization in a typical apartment setting. Our experimental results show that LoRa-based indoor localization has an accuracy better than 1.6 m in line-of-sight scenario and 3.2 m in extreme non-line-of-sight scenario with a precision better than 25 cm in all cases, without using any data filtering on the location estimates

Automated temporary structure safety planning using building information modeling (BIM)

Construction safety is greatly impacted by temporary structures, such as scaffolding, formwork, and shoring. However, temporary structures are often in-stalled and utilized in many construction projects without sufficient analysis about potential safety hazards associated with the temporary structures. As a result, there are widespread safety, productivity, constructability, and site coordination problems caused by temporary structures. Focusing on scaffolding, this research proposes a method to integrate temporary structure objects into automated safety planning and optimization using Building Information Modeling (BIM). Automation algorithms for scaffolding placement, safety simulation, and optimization were created. Optimization engine generates multiple alternative scaffolding plans and the safety simulation engine simulates daily construction site conditions for quantitative evaluation of each scaffolding plan. In case studies using real world construction projects, the overall results demonstrate that the proposed approach can assist in the creation of safer construction plan by automatically evaluating and transforming a temporary structure plan into an optimized plan with lower safety risks.Ph.D

Automated Generation of Daily Evacuation Paths in 4D BIM

Spatial movements of workers and equipment should be carefully planned according to project plans. In particular, it is crucial for workers’ safety to prepare emergency evacuation paths according to changing construction site configurations and construction progress. However, creating evacuation paths for all crews for each day can be an extremely labor-intensive task if it is done manually. Consequently, in most construction projects, evacuation plans are not provided to managers and crews throughout the entire construction. Even state-of-the-art technologies do not suggest ways to generate evacuation paths according to changing progresses presented in 4-Dimensional Building Information Model (4D BIM). This research proposes a framework to automatically analyze, generate, and visualize the evacuation paths of multiple crews in 4D BIM, considering construction activities and site conditions at the specific project schedule. This research develops a prototype that enables users to define parameters for pathfinding, such as workspaces, material storage areas, and temporary structures to automatically identify the accessible evacuation paths. This prototype shows the secured evacuation paths in the 4D BIM environment and allows the users to organize the automatically generated evacuation paths. A case study using the BIM model of a real construction project involved in this paper demonstrates the potential of the proposed method

Automated and Optimized Sensor Deployment using Building Models and Electromagnetic Simulation

With the advent of wireless sensing technology and interest in tracking resources, researchers have developed advanced tracking algorithms by using one or more sensor systems for improved accuracy and reliability of tracking. The objective of this research lies in another aspect−deployment−of tracking that has received only little attention until now. The research explores a method for sensor deployment particularly designed for the building in which the sensors are used. To tailor our solution to a specific building, we integrate a building information model with an electromagnetic energy analysis. By using such a model, the system extracts the properties of building materials, which are used as parameters of sensor deployment optimization. Then, we find a method of optimizing the deployment of a Received Signal Strength Indication (RSSI)-based tracking sensors for reducing wireless energy dissipation during the operation of the tracking system. For the numerical validation of the proposed method, the High-Frequency Structural Simulator (HFSS) runs an electromagnetic simulation to generate comparison data of electromagnetic energy flow from optimized sensor deployment and random sensor deployment. The results indicate that the proposed method could produce results that are correlated to the HFSS results. In addition, the method shows clear evidence of a reduction in signal power loss. Finally, optimized sensor deployment through the proposed framework can use signals of electromagnetic energy more effectively and potentially improve the efficiency of the RSSI-based tracking system

Integration of Real-Time Semantic Building Map Updating with Adaptive Monte Carlo Localization (AMCL) for Robust Indoor Mobile Robot Localization

A robot can accurately localize itself and navigate in an indoor environment based on information about the operating environment, often called a world or a map. While typical maps describe structural layouts of buildings, the accuracy of localization is significantly affected by non-structural building elements and common items, such as doors, appliances, and furniture. This study enhances the robustness and accuracy of indoor robot localization by dynamically updating the semantic building map with non-structural elements detected by sensors. We propose modified Adaptive Monte Carlo Localization (AMCL), integrating object recognition and map updating into the traditional probabilistic localization. With the proposed approach, a robot can automatically correct errors caused by non-structural elements by updating a semantic building map reflecting the current state of the environment. Evaluations in kidnapped robot and traditional localization scenarios indicate that more accurate and robust pose estimation can be achieved with the map updating capability