Self-healing concrete using encapsulated healing agent has shown great potential in enhancing concrete durability. However, the capsules are expensive to make and can lower the mechanical properties of concrete. In this study, a new type of manufactured aggregate that employs waste-derived fly ash cenosphere as a carrier of healing agent (SH-CS) was designed and produced. The effect of SH-CS incorporation on hydration, engineering properties and self-healing efficiency of cement mortar was systematically evaluated, with a special focus on self-healing mechanism through the analysis of the mineral composition of the healing product. The results indicate that the prepared SH-CS has good stability in and compatibility with cement mortar. The addition of SH-CS has small influence on the fresh properties of cement mortar and less negative effect on compressive strength at the hardened stage compared to the existing study. By replacing 3 wt.% of fine aggregate with SH-CS, up to 71% of the crack opening area of mortar specimens with a crack width of about 0.3 mm was self-healed after 28 days of water exposure. The self-healing behaviour of SH-CS led to a maximal 41% drop in water adsorption and contributed to the recovery of flexural strength. The healing products precipitated on the fracture surface were mainly composed of amorphous C-S-H and Calcite. It can be estimated that incorporating SH-CS in concrete would result in only a moderate (∼29%) rise in cost for C40 concrete.

Design for manufacturing and assembly (DfMA) is an engineering methodology which aims to increase ease of manufacture and efficiency of assembly by considering manufacturing and assembly constraints in the design process. However, current DfMA approaches in the construction sector are not automated enough to identify the design features that may cause project delay in real time. This leads to longer design cycle. Also, current scheduling algorithms rely on human intervention to generate activity network from a design output. Addressing these inefficiencies, we propose an interpretative machining learning model to predict the construction duration given a design output. More importantly, the same model identifies the design features that may cause the most delay in the project. The model is trained on a residential design dataset with various features, such as layout, geometry, and element typology. The output of the model is the project duration and an importance map, indicating the influence each feature of the given design has on the total project duration. The results from this model can considerably reduce the design cycle by supporting architects to create fabrication and assembly aware design even when they have little knowledge of production and assembly processes. This model will contribute to a novel computational approach for DfMA.

Van der Waals heterostructures consisting of graphene and transition metal dichalcogenides have shown great promise for optoelectronic applications. However, an in-depth understanding of the critical processes for device operation, namely, interfacial charge transfer (CT) and recombination, has so far remained elusive. Here, we investigate these processes in graphene-WS₂ heterostructures by complementarily probing the ultrafast terahertz photoconductivity in graphene and the transient absorption dynamics in WS₂ following photoexcitation. We observe that separated charges in the heterostructure following CT live extremely long: beyond 1 ns, in contrast to ~1 ps charge separation reported in previous studies. This leads to efficient photogating of graphene. Furthermore, for the CT process across graphene-WS₂ interfaces, we find that it occurs via photo-thermionic emission for sub-A-exciton excitations and direct hole transfer from WS₂ to the valence band of graphene for above-A-exciton excitations. These findings provide insights to further optimize the performance of optoelectronic devices, in particular photodetection.