Reduction in strength and stiffness in rocks attributed to an increase in water content has been extensively researched on a large variety of rock types over the past decades. Due to the considerable variations of texture and lithology, the extent of water-weakening effect is highly varied among different rock types, spanning from nearly negligible in quartzite to 90 % of uniaxial compressive strength reduction in shale. Readers, however, often face difficulties in comparing the data published in different sources due to the discrepancy of experimental procedures of obtaining the water saturation state and how the raw laboratory data is interpreted. In view of this, the present paper first reviews the terminologies commonly used to quantify the amount of water stored in rocks. The second part of the paper reviews the water-weakening effects on rock strengths, particularly focusing on uniaxial compressive strength and modulus, as well as tensile strength, under quasi-static loading and dynamic loading. The correlation relationships established among various parameters, including porosity, density and fabric of rocks, and external factors such as strain rate, surface tension and dielectric constant of the saturating liquid, absorption percentage and suction pressure, are reviewed and presented toward the end of the paper.

No abstract

No abstract

No abstract

We propose an automated postconstruction quality assessment robot system for crack, hollowness, and finishing defects in light of a need to speed up the inspection work, a more reliable inspection report, as well as an objective through fully automated inspection. Such an autonomous inspection system has a potential to cut labour cost significantly and achieve better accuracy. In the proposed system, a transfer learning network is employed for visual defect detection; a region proposal network is used for object region proposal, a deep learning network employed as feature extractor, and a linear classifier with supervised learning as object classifier; moreover, active learning of top-N ranking region of interest is undertaken for fine-tuning of the transfer learning on convolutional activation feature network. Extensive experiments are validated in a construction quality assessment system room and constructed test bed. The results are promising in a way that the novel proposed automated assessment method gives satisfactory results for crack, hollowness, and finishing defects assessment. To the best of our knowledge, this study is the first attempt to having an autonomous visual inspection system for postconstruction quality assessment of building sector. We believe the proposed system is going to help to pave the way towards fully autonomous postconstruction quality assessment systems in the future.