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Flexible strain sensors can detect physical signals (e.g., temperature, humidity, and flow) by sensing electrical deviation under dynamic deformation, and they have been used in diverse fields such as human motion detection, medical care, speech recognition, and robotics. Existing sensing materials have relatively low adaptability and durability and are not stretchable and flexible enough for complex tasks in motion detection. In this work, a highly flexible self‐healing conductive polymer composite consisting of graphene, poly(acrylic acid) and amorphous calcium carbonate is prepared via a biomineralization‐inspired process. The polymer composite shows good editability and processability and can be fabricated into stretchable strain sensors of various structures (sandwich structures, fibrous structures, self‐supporting structures, etc.). The developed sensors can be attached on different types of surfaces (e.g., flat, cambered) and work well both in air and under water in detecting various biosignals, including crawling, undulatory locomotion, and human body motion.

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PurposeThere is an increasing recognition of the value of effective information and knowledge management (KM) in the construction project delivery process. Many architecture, engineering and construction (AEC) organisations have invested heavily in information technology and KM systems that help in this regard. While these have been largely successful in supporting intra‐organisational business processes, interoperability problems still persist at the project organisation level due to the heterogeneity of the systems used by the different organisations involved. Ontologies are seen as an important means of addressing these problems. The purpose of this paper is to explore the role of ontologies in the construction project delivery process, particularly with respect to information and KM.Design/methodology/approachA detailed technical review of the fundamental concepts and related work has been undertaken, with examples and case studies of ontology‐based information and KM presented to illustrate the key concepts. The specific issues and technical difficulties in the design and construction context are highlighted, and the approaches adopted in two ontology‐based applications for the AEC sector are presented.FindingsThe paper concludes that there is considerable merit in ontology‐based approaches to information and KM, but that significant technical challenges remain. Middleware applications, such as semantic web‐based information management system, are contributing in this regard but more needs to be done particularly on integrating or merging ontologies.Originality/valueThe value of the paper lies in the detailed exploration of ontology‐based information and KM within a design and construction context, and the use of appropriate examples and applications to illustrate the key issues.

Summary The long‐term safety and health monitoring of large dams has attracted increasing attention. In this paper, coupling prediction models based on long short‐term memory (LSTM) network are proposed for the long‐term deformation of arch dams. Principal component analysis (PCA) and moving average (MA) method, adopted to make dimension reduction for the input variables, are respectively combined with the LSTM to achieve two coupling prediction models, that is, LSTM‐PCA and LSTM‐MA. Lijiaxia arch dam, which has been in operation over 20 years, is taken as an analysis example. Compared with the traditional hydrostatic‐seasonal‐time model, the hydrostatic‐seasonal‐time thermal model, and the multilayer perceptron model, the proposed models show more effectiveness concerning the predicted displacements of the arch dam. The accuracy of the predicted results from the coupling prediction models is better. Furthermore, the coupling prediction models could capture the long‐term characteristics and provide better prediction with short monitoring data. Compared with the LSTM‐PCA model, the LSTM‐MA model is more suitable for engineering applications due to its convenience.