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The Rise against Landslides: Big Data to the Rescue

Challenges: Current slope monitoring exercises are carried out manually. Several methods are being used to monitor slope movements. Some of the most common methods are satellite imageries and conventional Global Positioning System (GPS) which require manual observations. Others prefer to use 3D laser scanners that can scan any topography and provide quick assessments of the slope. Though these methods are efficient in monitoring slope movements, they lack realtime monitoring and predictive analytics of when landslides will happen.


Realtime slope monitoring is still not good enough to prevent the loss of lives and assets during a landslide. Predictive monitoring and an early-warning solution is essential to save lives.

Current slope monitoring is carried out manually and lack realtime and predictive analytics.

Solution: To help tackle the issue of slope management and monitoring, Pembinaan TMC Sistem has taken the leap into Big Data and Analytics and has come up with a revolutionary solution. They developed a remote landslide sensing system with Predictive Monitoring and Early Warning Solution known as Integrated Deformation Early-warning Alert Solution (IDeas). This new system is a huge improvement over the traditional, labour-intensive and costly methods of slope monitoring.

IDeas uses mapping, 3-D scanning and smart sensing—all combined together to create one cohesive ecosystem that is able to analyse and predict slope movements, thus preventing landslides. This state-of-the-art integrated solution runs on a cloud platform, allowing the company to easily scale their resources, making the system flexible and cost-effective.


On site, a robotic station automatically tracks and measures the position of all fixed prisms at a defined interval.

The concept being used is to measure the displacement of prisms fixed on selected critical points on the slope or monitored structure. Then, the position (x.y,z) will be surveyed to produced a set of reference points for displacement calculation. Theoretically, if there is no movement, the position of the prisms will never change. If there are any differences in the measured positions, it can be interpreted that there is soil movement or worse, a landslide. The measurements will not only indicate the size of displacement, but the direction of soil movement as well.

If the displacement value is bigger than the acceptable defined tolerance, it will trigger an alarm and send out warnings to respective personnels via SMS, email and social media. Realtime monitoring might warn you when a movement is detected; sometimes, however, by the time movements are detected, it could be too late. IDeas tries to forecast the incident as precisely as possible before a landslide actually happens.


Accelerated soil erosion is detected 3 months before slope failure. By analysing realtime data, IDeas can tell if a slope failure will happen. Predictive analytics can be used on historical data to forecast when a slope failure will happen.

TMC IDeas integrates high-precision land surveying instrument with realtime communications to monitor any environment 24 hours a day, seven days a week.

Benefits: IDeas has been used in a Malaysian gold mine, one of TMC’s target industries. As safety is paramount in the mining industry, serious damages or life lost due to landslides will lead to the mine being shut down for investigations. With IDeas, the gold mine in Malaysia minimized their risk of a landslide which could potentially cost them US$1.5 million for every week its operations are disrupted..

Industry: Construction, Mining

Big Data Solutions Provider ProfileEstablished in 2009, Pembinaan TMC Sistem has since emerged as a systems integrator and developer for survey & monitoring solutions. TMC owns intellectual property rights to its flagship product – TMC Management System – which is the framework for vertical solutions such as Slope Monitoring, Bridge & Dam Monitoring, 3D Scanning & Mapping, centralized monitoring and analysis system in a web based platform. In 2010, TMC was awarded the MSC status by the government of Malaysia for its contribution to the development of flagship applications and for supporting Malaysia’s K-economy initiatives. Our products and solutions have been utilized in a number of government agencies and various corporations in Malaysia, Brunei & Asia Pacific. We have representatives & associates located across Hong Kong, China, India and USA.

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Advanced data analytics ensures flawless management of construction projects

Working with big data analytics (BDA), this construction company has been able to minimise losses from late deliveries in its project schedule. Data analytics facilitated predictive abilities that became integral to the company’s new centralized system that now enabled different levels of executives to monitor, benchmark and act on a variety of factors such as manpower allocation, machinery placement, resource scheduling, up-to-date project progress and expenditures.

In the construction sector, good project schedule and cost estimation, on top of ensuring timely project execution are very important. Contractors and developers get penalized with Liquidated and Ascertained Damages or LADs – these are fixed damages stated in the building contract, and usually set as an amount per week or part of a week which the contractor must pay the employer if completion is delayed beyond the contractual date for completion – that sometimes amount to millions of Ringgit for late deliveries. Therefore, the need arose for a centralized system that will allow different levels of executives to monitor, benchmark, and act on a variety of factors. These factors include manpower allocation, machinery placement, resource scheduling, up-to-date project progress and expenditures, which both directly and indirectly contribute to desired project schedules and costs.


Prior to implementing Quandatics’ advanced analytics solution, data was scattered around in spreadsheets owned by different departments, namely Site Management, Project Management, Finance and Human Resources. This made it difficult for upper management to obtain a complete overview of important metrics without spending time getting people together to consolidate on-hand data.

The limited interactive-ness of old-fashioned spreadsheet visualization also constrained the amount of understanding all the users could obtain from available data. A lot of times, problems are only detected after weeks of ongoing events and consequently, the implemented corrective measures were much less effective.

The first step toward a solution was the consolidation of historical datasets into a centralized database system, with mobile and web interfaces for supervisors to append up-to-date data of on-going projects into the system.  This allows for extraction of descriptive insights, and benchmarking to be performed on a number of important metrics that constitutes the project schedules, so that senior management can make data-driven decisions.

A key component in the current implementation is a project management portal for schedule, progress, and cost monitoring to optimize resources allocation. An interactive infographic service with multi-level access is provided for personnel with different access rights to visualize or append the data and results according to their job functions.


Project management infographics for monitoring individual project progress, resource usage and costing

Business Benefits:
Previously, manual work was required to consolidate and aggregate data from multiple departments in order to report the equivalent amount of information. It’s now instant, with consistently updated progress visualization for all on-going projects available on multiple devices and multiple access levels.  Furthermore, standardized reporting and progress update interface for all site supervisors are now enabled, eliminating scattered records and logs and potential loss of information. With these capabilities, the management team is now able to respond to potential deficiencies or problems much faster – from one week previously to within a day now.

The Future:
By consolidating data from past and current piling projects, correlations between their attributes and the project outcome, profits and schedules can be studied. Moving forward, more data sources, such as ground survey data and weather data can also be incorporated to make the system more comprehensive.

Future implementation with machine learning features will generate best-action recommendations for on-going projects. These outputs could potentially lead to the minimization of LADs, reduction of material wastage, and optimization of machinery placement, which are among the issues with the highest priorities in piling projects.