Incorporation of BIM-based probabilistic non-structural damage assessment into agent-based post-earthquake evacuation simulation

Earthquakes can cause severe damage to structural and non-structural elements of buildings; consequently, they pose high risks to human lives. To mitigate such risks, attention has been paid to enhancing the indoor environment for increased building safety. Yet little effort has been made to assess a building occupants' evacuation behaviors in response to damage to the indoor environment. This paper addresses this issue with a novel simulation framework that couples human behaviors with changes to the indoor building environment during post-earthquake evacuation. In particular, we present a building information modelling (BIM)-based prototype that simulates seismic damage to the non-structural indoor elements and visualizes its impacts on evacuation using a color-coded heat map. The simulated damage is then used as input to an agent-based model for post-earthquake evacuation. Using a probabilistic method to assess the non-structural elements' damage states, we are able to evaluate the impact of indoor damage on the evacuation process. We performed a trial of our prototype for a hypothetical earthquake in an educational building. The results revealed how the average evacuation time would increase as the earthquake intensity increases (from 38.6 s for the no-damage scenario to 122.9 for the highest-damage scenario). The proposed prototype has the potential to be joined with other tools, such as finite-element-based simulation, to incorporate structural analysis as well. Planners and designers can explicitly use our model's output to analyze the post-earthquake evacuation with the indoor non-structural damage to assess different building design geometries that increase the chances of a suitable evacuation process.     

Multi-task spatio-temporal augmented net for industry equipment remaining useful life prediction

Accurate estimating the machine health indicator is an essential part of industrial intelligence. Despite having considerable progress, remaining useful life (RUL) prediction based on deep learning still confronts the following two challenges. Firstly, the length of condition monitoring data obtained from sensors is inconsistent, and the existing fixed window data processing method cannot fully adapt to all individual samples. Secondly, it is challenging to extract local and global features for long-series prediction tasks. To address these issues, this paper proposes a Multi-task Spatio-Temporal Augmented Net(MTSTAN) for industrial RUL prediction, which enhances the local features of different sensors data through channel attention mechanism, and proposes a skip connected causal augmented convolution network to enhance the global feature extraction in time series. For the industrial scenario of inconsistent data lengths, a multi-window multi-task sharing mechanism is set up to capture various time dependencies among different time scales. The robustness and universality of the model are increased by sharing information among tasks and multi-task window mechanism. Finally, a large number of experiments were carried out on the turbofan aircraft engine run-to-failure prognostic benchmark dataset (C-MAPSS) to evaluate the proposed model, and compared with the existing 14 state-of-the-art approaches. The results show that the enhancement of local and global time series features can effectively improve the prediction accuracy. The Multi-task learning strategy has excellent applicability in dealing with the problem of inconsistent data length.     

Developing an advanced ANN-based approach to estimate compaction characteristics of highway subgrade

The current methodology of assessing the compaction characteristic of subgrade fillers is time-consuming. The spot-sampling method and the following laboratory tests bring bias to engineering practice. This study proposes an advanced approach to determine the maximum dry density, optimum moisture content, and compactness of subgrade based on ANN algorithms. A large number of compaction tests are performed to assess the compaction quality of various types of subgrade fillers. The particle gradation is specifically considered. Wave propagations of the compacted specimen are assessed based on the ultrasonic test, and the anisotropic feature is demonstrated. The dynamic elastic parameters are derived to further reveal the interaction mechanism. A dataset is proposed by combining these laboratory data and the literature data. The PSO-BP-NN model is developed to automatically predict the compaction parameters of different filling materials. A positive correlation is demonstrated between the elastic modulus and the total density, and Poisson’s ratio illustrates an inverse trend. The compaction energy is influential to the maximum dry density while the moisture content has the greatest impact on the compactness. This study aims to develop an accurate model to replace the extensive Proctor test and efficiently evaluate the compaction quality of subgrade for highway constructions.     

Proactive and visual approach for product maintainability design

Maintainability design, as a vital element in product design, is generally conducted after the physical or virtual prototype is done, and this design way always accompanies with the characters of lag, passiveness, subjectivity and lack of relevance. And even design flaws are exposed based on corresponding maintainability analysis, the improvements are hardly finished owing to various practical difficulties. This paper proposed a novel proactive maintainability design method. Based on the maintainability and functionability/structurability (F/S) factors of the product first, a “many to many” mapping relationship between maintainability and F/S spaces is established, in which quantitative assessment and visualization representation are two aspects. Separately, the quantitative assessment provides an objective and precise result of relationship confirmation, and the visualization representation brings a comprehensive and intuitive way for maintainability and F/S designers. Finally, the case study section shows the availability and effectiveness of the methodology by verification and comparison. The proposed methodology considers maintainability affection on F/S just from the start point of product design. Hence, compared with the physical prototype and virtual prototype approach, the implementation of the proposed methodology can make maintainability design and functional/structural design to be conducted almost simultaneously.     

A contradiction solving method for complex product conceptual design based on deep learning and technological evolution patterns

Contradictions caused by the various design constraints present increasing challenges to efficiency and innovation in product development. TRIZ provides Inventive Principles (IPs) and Contradiction Matrix that are the most frequently applied in conflict resolution. However, the high-level abstraction and subjective selection of IPs inhibit achieving the transformation process from paradoxical states to physical structures. To fill this gap, a contradiction solving method by integrating deep learning and technological evolution patterns for product conceptual design is proposed, which illustrates the mechanism of contradiction transition from the perspective of system evolution and supplies a systematic and model-based design approach. Firstly, generic engineering parameters are extracted to define the underlying contradictions transformed from critical defects which are found out through function modeling and root-conflict analysis. Then, a fully-connected deep neural network with excellent performance is developed to uncover the non-linear relationships between engineering parameters and evolution patterns. Finally, an evolution tree based on the predicted patterns is constructed to visualize transformation potentials of a technical system and help designers generate innovative specific solutions. In addition, a case study concerning design conflict resolution for beat-up system of three-dimensional tubular weaving machine is used to validate the adaptability and reliability of the proposed approach.     

An intelligent HVAC control strategy for supplying comfortable and energy-efficient school environment

In South Korea, school buildings require significant energy inputs for heating and air-conditioning, and the majority of the occupants are adolescent students, whose health and cognitive performance are vulnerable to poor indoor air quality (IAQ) and thermal discomfort. Using field measurements, some previous studies have reported that some Korean schools have poor IAQ and thermal conditions. Thus, it is necessary to develop effective heating, ventilation, and air-conditioning (HVAC) control strategies to improve the indoor environment and reduce energy consumption. Therefore, this study proposes an intelligent HVAC integrated control strategy that can improve indoor environmental quality (IEQ) and reduce energy consumption in school buildings. The proposed strategy utilizes an integrated neural network prediction model for IEQ and a heuristic method that can optimize control objectives (i.e., the predicted mean vote [PMV], carbon dioxide [CO₂], particulate matter with diameters of 10 and 2.5 μm [PM₁₀ and PM_2.5, respectively], and HVAC energy consumption). To evaluate the control performance of the proposed strategy, the present study employs two base algorithms (i.e., a rule-based and a non-adaptive control approach) under non-disturbance and forcing disturbance scenarios. The control failure period for PMV is found to be 1.6420% and 9.4773% of the total occupancy period under the non-disturbance and forcing disturbance scenarios, respectively, while CO₂ control failure does not occur under either scenario. The control failure periods for PM₁₀ and PM_2.5 were 5.1676%, and 7.1844%, respectively, under forcing disturbance. Under the non-disturbance scenario, the proposed strategy consumed 2,467.07 kWh and 870,26 kWh for heating and cooling, respectively, representing 91.1% and 84.08% of that for the rule-based algorithm. The proposed strategy can thus effectively improve the IEQ of a building and has the potential for use in the development of integrated environmental management solutions for buildings.     

特高土心墙堆石坝坝基防渗体渗流-溶蚀特征研究

针对渗透溶蚀效应下特高土心墙堆石坝的渗流与溶蚀问题，构建了以孔隙水压力、固相钙浓度与钙离子浓度为自由度的水泥基材料渗流-溶蚀耦合模型。以长河坝工程为背景，研究了特高土心墙堆石坝的渗流溶蚀特征，探讨了渗透溶蚀效应下特高土心墙堆石坝的失效标准，预测了坝体的服役年限。渗透溶蚀效应降低了坝体的防渗能力，服役100 a后坝体浸润线逸出点将较初始时刻抬升1.95 m;随着服役年限的增加，覆盖层、副防渗墙的渗透坡降增加，心墙、主防渗墙和防渗帷幕的渗透坡降降低;水泥基材料固相钙溶蚀相对严重的区域集中在两道防渗墙中下部、固结灌浆靠下游侧及防渗帷幕，靠近复合土工膜和高塑性黏土的坝基防渗体溶蚀程度较低。从固相钙的分解率、渗透系数、渗流量、渗透坡降和边坡稳定等角度分析，认为考虑渗透溶蚀效应时长河坝的服役年限约为68.3 a，降低主防渗帷幕的初始渗透性可较为有效地延长坝体服役年限。特高土心墙堆石坝坝基水泥基结构渗透溶蚀效应不可忽视，其设计、运行及维护应充分考虑水泥基材料的渗透溶蚀效应。     

Thermal image-based hand gesture recognition for worker-robot collaboration in the construction industry: A feasible study

Worker-robot collaboration (WRC) is a promising solution for complex construction tasks, which can integrate the robots’ advantages in strength and accuracy with human ability in intuitive decision-making and adaptability. A new imperative objective for real-world WRC is to design a user-friendly interface to support safe and efficient worker-robot interactions. Vision-based hand gesture is a simple but effective solution. However, existing methods mainly depend on 3-channel RGB images captured by visible cameras, which are prone to be affected by on-site environmental disturbances, such as poor illumination, fog, and dust. Moreover, previous networks strive for high accuracy, neglecting computational efficiency (e.g., model size and latency) when implementing the network on resource-constrained devices like mobile construction robots. Against this backdrop, this research presented a feasibility study to investigate whether hand signals can be detected from thermal images and designed a lightweight network that has fewer parameters and obtains lower latency without compromising accuracy. Experimental results indicated that thermal images were robust to different lighting conditions, and the proposed model achieved a high classification accuracy (97.54 %) with 1.8 M parameters. The comparative study demonstrated the superiority of our model to other advanced lightweight models, illustrating the feasibility of the developed method in supporting safe WRC applications by recognizing workers’ hand signals.     

Online merchant resource allocation and matching for open community collaborative manufacturing (OCCM) in mass personalization model

In order to meet the requirements of customers' pursuit of personalized and diversified needs, enterprises need to change from the traditional mass manufacturing model to mass personalization manufacturing model. On the other hand, collaborative manufacturing based on stakeholders will effectively solve the problems of information asymmetry, low operational efficiency and insufficient resource utilization in the process of manufacturing. Moreover, the new model of open community manufacturing is conducive to the planning, allocation, optimization and management of various networked and social resources. Therefore, combined with above three innovative models of mass personalization manufacturing, collaborative manufacturing and open community manufacturing, this paper studies the online merchant resource allocation and matching of open community collaborative manufacturing (OCCM) for mass personalization (MP) model to realize the innovation of design model, improve design efficiency and save design resource cost. Firstly, the workflow for OCCM in MP model based on system engineering analysis method is studied. Secondly, online merchant resource allocation for OCCM based on social network analysis (SNA) and online merchant resource matching for OCCM based on fuzzy clustering are proposed. At last, a case study for OCCM in the automotive industry is given to verify the effectiveness of the proposed model and methods.